--- title: Minimalist Variational Autoencoder in Pytorch with CUDA GPU author: Mauro Camara Escudero date: '2020-09-08' slug: minimalist-variational-autoencoder-in-pytorch-with-cuda-gpu categories: - vae - pytorch - gpu tags: - vae - cuda - gpu subtitle: 'Variational Autoencoders in Pytorch with CUDA GPU' summary: 'Variational Autoencoders in Pytorch with CUDA GPU' authors: [] lastmod: '2020-09-08T16:47:32+01:00' featured: no image: caption: '' focal_point: '' preview_only: no projects: [] ---
Coding a Variational Autoencoder in Pytorch and leveraging the power of GPUs can be daunting. This is a minimalist, simple and reproducible example. We will work with the MNIST Dataset. The training set contains \(60\,000\) images, the test set contains only \(10\,000\). We will code the Variational Autoencoder (VAE) in Pytorch because it’s much simpler and yet flexible enough to code it in a few different ways. For this reason, in this post we will see the most minimalist version of a Variational Autoencoder, and then in the next post Assessing a Variational Autoencoder on MNIST using Pytorch we will see a slightly more verbose version that can be used for data generation and visualization of the Latent Space.
Images in the MNIST Dataset are in grayscale, they are made of \(28\times 28\) pixels whose values range between \(0\) and \(255\). Notice that since its grayscale, we only have one number per pixel. In contrast, if the images where colored, then we would have \(3\) numbers per pixel, each one of them representing the intensity of Red, Green and Blue (RGB).
In Pytorch there’s a two-step process to use a dataset.
Importantly, when defining the Dataset object, you can specify how to preprocess the data, e.g. scaling the data. In VAE we are assuming that each feature (i.e. each pixel value) can be interpreted as a probability. For this reason, we want to scale our pixel values to be in the range \([0, 1]\).
Notice that very often, when using other methods, people normalize the pixel values to be centered around zero and to have unit standard deviation. This must not be done in this case because we cannot have negative pixel values.
Here’s one way to load the MNIST dataset.
# Import libraries
import torchvision # contains image datasets and many functions to manipulate images
import torchvision.transforms as transforms # to normalize, scale etc the dataset
from torch.utils.data import DataLoader # to load data into batches (for SGD)
from torchvision.utils import make_grid # Plotting. Makes a grid of tensors
from torchvision.datasets import MNIST # the classic handwritten digits dataset
import matplotlib.pyplot as plt # to plot our images
import numpy as np
# Create Dataset object.s Notice that ToTensor() transforms images to pytorch
# tensors AND scales the pixel values to be within [0, 1]. Also, we have separate Dataset
# objects for training and test sets. Data will be downloaded to a folder called 'data'.
trainset = MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor())
testset = MNIST(root='./data', train=False, download=True, transform=transforms.ToTensor())
# Create DataLoader objects. These will give us our batches of training and testing data.
batch_size = 100
trainloader = DataLoader(trainset, batch_size=batch_size, shuffle=True)
testloader = DataLoader(testset, batch_size=batch_size, shuffle=True)
If you have a GPU the following should print device(type='cuda', index=0)
.
import torch
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
First, we import the necessary libraries.
import torch.nn as nn # Class that implements a model (such as a Neural Network)
import torch.nn.functional as F # contains activation functions, sampling layers etc
import torch.optim as optim # For optimization routines such as SGD, ADAM, ADAGRAD, etc
Next, we define some global variables that are going to be used by our Variational AutoEncoder.
e_hidden = 500 # Number of hidden units in the encoder. See AEVB paper page 7, section "Marginal Likelihood"
d_hidden = 500 # Number of hidden units in the decoder. See AEVB paper page 7, section "Marginal Likelihood"
latent_dim = 2 # Dimension of latent space. See AEVB paper, page 7, section "Marginal Likelihood"
learning_rate = 0.001 # For optimizer (SGD or Adam)
weight_decay = 1e-5 # For optimizer (SGD or Adam)
epochs = 50 # Number of sweeps through the whole dataset
Finally, we can define the variational autoencoder. The simplest way to do so is to create a new class, which we call VAE
inheriting from Pytorch’s nn.Module
class. Essentially, all we need to do is to define a method called forward
. This method should take one input parameter which corresponds to an image and it should output its reconstruction. It describes what happens to an image when it goes throught the Variational Autoencoder.
To simplify notation, we define some variables in the constructor __init__()
. In particular, we define the set of operations that we are going to use in the forward()
method.
self.e_input2hidden()
is a function that takes an input image and feeds it through the first set of weights. These are the weights connecting the input layer to the hidden layer.self.e_hidden2mean()
and self.e_hidden2logvar()
are two functions that take the resulting activations in the hidden layer and feed them through two different sets of weights, connecting the hidden layer to a \(\mu\)-output layer and a \(\log\sigma^2\)-output layer. Basically together these represent all the parameters of the Encoder network. When we feed an image through the Variational Autoencoder, this goes through a feed-forward neural network with one hidden layer and 2 output layers. Each output layer has the same number of neurons, i.e. the dimension of the latent space, which is specified by latent_dim
. One output layer gives us what will later be the mean of the latent space representation of that image and the other output layer gives us what will later be the log variance of the latent space representation of that image.self.d_latent2hidden()
is a function that takes a latent representation z
and feeds it through a first set of weights, connecting the latent space to the first hidden layer.self.d_hidden2image()
is a function that takes the activations in such hidden layer and feeds them through a second set of weights, connecting the hidden layer to the image space. Essentially, these two functions represent the parameters of the Decoder which takes a latent representation and returns a flattened reconstructed image.class VAE(nn.Module):
def __init__(self):
"""Variational Auto-Encoder Class"""
super(VAE, self).__init__()
# Encoding Layers
self.e_input2hidden = nn.Linear(in_features=784, out_features=e_hidden)
self.e_hidden2mean = nn.Linear(in_features=e_hidden, out_features=latent_dim)
self.e_hidden2logvar = nn.Linear(in_features=e_hidden, out_features=latent_dim)
# Decoding Layers
self.d_latent2hidden = nn.Linear(in_features=latent_dim, out_features=d_hidden)
self.d_hidden2image = nn.Linear(in_features=d_hidden, out_features=784)
def forward(self, x):
# Shape Flatten image to [batch_size, input_features]
x = x.view(-1, 784)
# Feed x into Encoder to obtain mean and logvar
x = F.relu(self.e_input2hidden(x))
mu, logvar = self.e_hidden2mean(x), self.e_hidden2logvar(x)
# Sample z from latent space using mu and logvar
if self.training:
z = torch.randn_like(mu).mul(torch.exp(0.5*logvar)).add_(mu)
else:
z = mu
# Feed z into Decoder to obtain reconstructed image. Use Sigmoid as output activation (=probabilities)
x_recon = torch.sigmoid(self.d_hidden2image(torch.relu(self.d_latent2hidden(z))))
return x_recon, mu, logvar
As we can see above, the first operation in forward()
is to make sure that the input is flattened. This is needed because it has to go through a feed-forward fully-connected layer (also called Linear
in Pytorch). Notice that when I say that the input is flattened I don’t mean that it’s one dimensional. Rather, we are flattening the height and the width of the image into one dimension.
The input to the Variational AutoEncoder will need to be shaped [batch_size, input_features]
. Unfortunately, when we iterate over the DataLoader, we get an object that is shaped [batch_size, channels, height, width]
. In our case, since we have only one color channel and images are 28x28
this means that we get [batch_size, 1, 28, 28]
. To feed this batch of images to the Variational Autoencoder we simply flatten the last \(3\) dimensions so that each pixel value of each channel is a feature. That is, we shape it to be [batch_size, 784]
. In the code above, I’ve used x.view(-1, 784)
and, similarly to Numpy, this means that we want the second dimension to be 784
and we let Pytorch work out what the second dimension will be (in this case 100
).
Then, we feed the batch images of size [batch_size, 784]
onto our first set of weights and we feed this into a Rectified Linear Unit activation function. At this point our batch of images has shape [batch_size, e_hidden]
. Then we feed these activations through two different output layers, thus obtaining our latent mu
and logvar
. The output of each of these layers has shape [batch_size, latent_dim]
.
Then, during training, we sample from a Normal distribution with that mean and variance. In order to allow backpropagation to flow through the network, we need to use the reparametrization trick. This simply consists in first sampling from a standard normal distribution (with shape [batch_size, latent_dim]
), scaling it by the standard deviation and adding the mean to it. If we are not training the network anymore, we simply output mu
so that the output is reproducible every time (which also has shape [batch_size, latent_dim]
).
Finally, we take the sampled latent representation, we feed it through a first set of weights, through a Relu activation function (so its shape is not [batch_size, d_hidden]
), through a second set of weights (connecting the hidden layer to the output layeter), and finally we feed this through a Sigmoid activation function (so that the final shape is [batch_size, 784]
). We use sigmoid activation function because, as you can remember from the previous Variational Autoencoders post, each of our pixels is considered a probability, so we need our output to be between [0, 1]
.
Below is an example of what the encoder looks like:
And similarly, what the decoder looks like:
We are now ready to train the Variational Autoencoder.First, we define our loss. Remember that maximizing the evidence can be approximately done by maximizing the ELBO (Evidence Lower Bound) which can be written as a difference of two terms:
\[ \mathcal{L}_{\theta, \boldsymbol{\mathbf{\phi}}}(\boldsymbol{\mathbf{x}})= \underbrace{\mathbb{E}_{q_{\boldsymbol{\mathbf{\phi}}}}\left[\log p_{\boldsymbol{\mathbf{\theta}}}(\boldsymbol{\mathbf{x}}\mid \boldsymbol{\mathbf{z}})\right]}_{\text{Expected Log-Likelihood}} - \underbrace{\text{KL}(q_{\boldsymbol{\mathbf{\phi}}}(\boldsymbol{\mathbf{z}}\mid \boldsymbol{\mathbf{x}})\,\,||\,\,p_{\boldsymbol{\mathbf{\theta}}}(\boldsymbol{\mathbf{z}}))}_{\text{Regularization Term}} \]
In the case of both \(q_{\boldsymbol{\mathbf{\phi}}}\) and \(q_{\boldsymbol{\mathbf{\phi}}}(\boldsymbol{\mathbf{z}}\mid \boldsymbol{\mathbf{x}})\) being Gaussians the AEVB paper shows that the following formula is available
\[
-\text{KL}(q_{\boldsymbol{\mathbf{\phi}}}\,\,||\,\,q_{\boldsymbol{\mathbf{\phi}}}(\boldsymbol{\mathbf{z}}\mid \boldsymbol{\mathbf{x}})) = \frac{1}{2}\sum_{j=1}^J \left[1 + \log\sigma_j^2 - \mu_j^2 - \sigma_j^2\right]
\]
where \(J\) is the dimensionality of \(\boldsymbol{\mathbf{z}}\) which, in our case, is denoted latent_dim
.
Then an unbiased estimate for the Objective is given below:
\[ \widetilde{\mathcal{L}}_{\boldsymbol{\mathbf{\theta}}, \boldsymbol{\mathbf{\phi}}}(\boldsymbol{\mathbf{x}}) = \displaystyle \frac{1}{L}\sum_{i=1}^L \left[\log p_{\boldsymbol{\mathbf{\theta}}}(\boldsymbol{\mathbf{x}}\mid g_{\boldsymbol{\mathbf{\phi}}}(\boldsymbol{\mathbf{\epsilon}}^{(i)}, \boldsymbol{\mathbf{x}}))\right] - \frac{1}{2}\sum_{j=1}^J \left[1 + \log\sigma_j^2 - \mu_j^2 - \sigma_j^2\right] \qquad \text{where } \boldsymbol{\mathbf{\epsilon}}^{(i)} \overset{\text{i.i.d.}}{\sim}\mathcal{N}(\boldsymbol{\mathbf{0}}, \boldsymbol{\mathbf{I}}) \]
Where \(L=\) L
and \(J=\) latent_dim
.
Recall that in the case of Bernoulli variables the log-likelihood becomes
\[
\log p_{\boldsymbol{\mathbf{\theta}}}(\boldsymbol{\mathbf{x}}\mid \boldsymbol{\mathbf{z}}) = \sum_{j} x_j \log p_j + (1 - x_j) \log(1 - p_j)
\]
Since we want our pixel values to be between 0
and 1
this is exactly what we are looking for and thankfully is already implemented in Pytorch under the name of binary_crossentropy
.
# Loss
def vae_loss(image, reconstruction, mu, logvar):
"""Loss for the Variational AutoEncoder."""
# Binary Cross Entropy for batch
BCE = F.binary_cross_entropy(input=reconstruction.view(-1, 28*28), target=image.view(-1, 28*28), reduction='sum')
# Closed-form KL Divergence
KLD = 0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
return BCE - KLD
# Instantiate VAE with Adam optimizer
vae = VAE()
vae = vae.to(device) # send weights to GPU. Do this BEFORE defining Optimizer
optimizer = optim.Adam(params=vae.parameters(), lr=learning_rate, weight_decay=weight_decay)
vae.train() # tell the network to be in training mode. Useful to activate Dropout layers & other stuff
# Train
losses = []
for epoch in range(epochs):
# Store training losses & instantiate batch counter
losses.append(0)
number_of_batches = 0
# Grab the batch, we are only interested in images not on their labels
for images, _ in trainloader:
# Save batch to GPU, remove existing gradients from previous iterations
images = images.to(device)
optimizer.zero_grad()
# Feed images to VAE. Compute Loss.
reconstructions, latent_mu, latent_logvar = vae(images)
loss = vae_loss(images, reconstructions, latent_mu, latent_logvar)
# Backpropagate the loss & perform optimization step with such gradients
loss.backward()
optimizer.step()
# Add loss to the cumulative sum
losses[-1] += loss.item()
number_of_batches += 1
# Update average loss & Log information
losses[-1] /= number_of_batches
print('Epoch [%d / %d] average reconstruction error: %f' % (epoch+1, epochs, losses[-1]))
You should see an output similar to the following:
Epoch [1 / 10] average reconstruction error: 18439.412160
Epoch [2 / 10] average reconstruction error: 16523.078558
Epoch [3 / 10] average reconstruction error: 16100.590828
Epoch [4 / 10] average reconstruction error: 15850.990076
Epoch [5 / 10] average reconstruction error: 15673.100459
Epoch [6 / 10] average reconstruction error: 15539.618418
Epoch [7 / 10] average reconstruction error: 15431.895505
Epoch [8 / 10] average reconstruction error: 15348.046214
Epoch [9 / 10] average reconstruction error: 15278.517415
Epoch [10 / 10] average reconstruction error: 15210.919212
To learn how you can test the Variational Autoencoder, how to use it to generate data and how to visualize the latent space have a look at my new blog post: Assessing a Variational Autoencoder on MNIST using Pytorch.
You can find the code in this blogpost in this Google Colab notebook.


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The official tutorial on how to best use this site.
Backtest your trading strategy at a click of a button! How much alpha can you generate? Alpharithmic Trading allows you to see how trading algorithms work in real-time, with live logging, interactive graphs, and adjustable parameters.
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The example algorithms that can be found on this site are divided into three main categories (or classes).
The Naive Class of algorithms is a class categorized by the fact that they track and trade a single stock using some strategy. You can tell if an algorithm falls under the Naive Class if you have to pick a stock to trade in the simulation parameters. Conceptually,these are the easiest algorithms to understand. However, simple does not mean it's ineffective! There are tons of great strategies that fall under this category. For starters, we recommend checking out the RSI Divergence Strategy!
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mne.time_frequency.
AverageTFR
(info, data, times, freqs, nave, comment=None, method=None, verbose=None)[source]¶Container for Time-Frequency data.
Can for example store induced power at sensor level or inter-trial coherence.
| Parameters: |
|
|---|---|
| Attributes: |
|
Methods
__add__ (tfr) |
Add instances. |
__contains__ (ch_type) |
Check channel type membership. |
__hash__ () |
Hash the object. |
__sub__ (tfr) |
Subtract instances. |
add_channels (add_list[, force_update_info]) |
Append new channels to the instance. |
apply_baseline (baseline[, mode, verbose]) |
Baseline correct the data. |
copy () |
Return a copy of the instance. |
crop ([tmin, tmax]) |
Crop data to a given time interval in place. |
drop_channels (ch_names) |
Drop some channels. |
pick_channels (ch_names) |
Pick some channels. |
pick_types ([meg, eeg, stim, eog, ecg, emg, …]) |
Pick some channels by type and names. |
plot ([picks, baseline, mode, tmin, tmax, …]) |
Plot TFRs as a two-dimensional image(s). |
plot_joint ([timefreqs, picks, baseline, …]) |
Plot TFRs as a two-dimensional image with topomaps. |
plot_topo ([picks, baseline, mode, tmin, …]) |
Plot TFRs in a topography with images. |
plot_topomap ([tmin, tmax, fmin, fmax, …]) |
Plot topographic maps of time-frequency intervals of TFR data. |
reorder_channels (ch_names) |
Reorder channels. |
save (fname[, overwrite]) |
Save TFR object to hdf5 file. |
__contains__
(ch_type)[source]¶Check channel type membership.
| Parameters: |
|
|---|---|
| Returns: |
|
Examples
Channel type membership can be tested as:
>>> 'meg' in inst
True
>>> 'seeg' in inst
False
add_channels
(add_list, force_update_info=False)[source]¶Append new channels to the instance.
| Parameters: |
|
|---|---|
| Returns: |
|
See also
Notes
If self
is a Raw instance that has been preloaded into a
numpy.memmap
instance, the memmap will be resized.
apply_baseline
(baseline, mode='mean', verbose=None)[source]¶Baseline correct the data.
| Parameters: |
|
|---|---|
| Returns: |
|
ch_names
¶Channel names.
compensation_grade
¶The current gradient compensation grade.
crop
(tmin=None, tmax=None)[source]¶Crop data to a given time interval in place.
| Parameters: |
|
|---|---|
| Returns: |
|
drop_channels
(ch_names)[source]¶Drop some channels.
| Parameters: |
|
|---|---|
| Returns: |
|
See also
Notes
New in version 0.9.0.
pick_channels
(ch_names)[source]¶Pick some channels.
| Parameters: |
|
|---|---|
| Returns: |
|
See also
Notes
The channel names given are assumed to be a set, i.e. the order
does not matter. The original order of the channels is preserved.
You can use reorder_channels
to set channel order if necessary.
New in version 0.9.0.
pick_types
(meg=True, eeg=False, stim=False, eog=False, ecg=False, emg=False, ref_meg='auto', misc=False, resp=False, chpi=False, exci=False, ias=False, syst=False, seeg=False, dipole=False, gof=False, bio=False, ecog=False, fnirs=False, include=(), exclude='bads', selection=None, verbose=None)[source]¶Pick some channels by type and names.
| Parameters: |
|
|---|---|
| Returns: |
|
See also
Notes
New in version 0.9.0.
plot
(picks=None, baseline=None, mode='mean', tmin=None, tmax=None, fmin=None, fmax=None, vmin=None, vmax=None, cmap='RdBu_r', dB=False, colorbar=True, show=True, title=None, axes=None, layout=None, yscale='auto', mask=None, mask_style=None, mask_cmap='Greys', mask_alpha=0.1, combine=None, exclude=[], verbose=None)[source]¶Plot TFRs as a two-dimensional image(s).
| Parameters: |
|
|---|---|
| Returns: |
|
plot_joint
(timefreqs=None, picks=None, baseline=None, mode='mean', tmin=None, tmax=None, fmin=None, fmax=None, vmin=None, vmax=None, cmap='RdBu_r', dB=False, colorbar=True, show=True, title=None, layout=None, yscale='auto', combine='mean', exclude=[], topomap_args=None, image_args=None, verbose=None)[source]¶Plot TFRs as a two-dimensional image with topomaps.
| Parameters: |
|
|---|---|
| Returns: |
|
Notes
timefreqs has three different modes: tuples, dicts, and auto. For (list of) tuple(s) mode, each tuple defines a pair (time, frequency) in s and Hz on the TFR plot. For example, to look at 10 Hz activity 1 second into the epoch and 3 Hz activity 300 msec into the epoch,:
timefreqs=((1, 10), (.3, 3))
If provided as a dictionary, (time, frequency) tuples are keys and (time_window, frequency_window) tuples are the values - indicating the width of the windows (centered on the time and frequency indicated by the key) to be averaged over. For example,:
timefreqs={(1, 10): (0.1, 2)}
would translate into a window that spans 0.95 to 1.05 seconds, as well as 9 to 11 Hz. If None, a single topomap will be plotted at the absolute peak across the time-frequency representation.
New in version 0.16.0.
plot_topo
(picks=None, baseline=None, mode='mean', tmin=None, tmax=None, fmin=None, fmax=None, vmin=None, vmax=None, layout=None, cmap='RdBu_r', title=None, dB=False, colorbar=True, layout_scale=0.945, show=True, border='none', fig_facecolor='k', fig_background=None, font_color='w', yscale='auto')[source]¶Plot TFRs in a topography with images.
| Parameters: |
|
|---|---|
| Returns: |
|
plot_topomap
(tmin=None, tmax=None, fmin=None, fmax=None, ch_type=None, baseline=None, mode='mean', layout=None, vmin=None, vmax=None, cmap=None, sensors=True, colorbar=True, unit=None, res=64, size=2, cbar_fmt='%1.1e', show_names=False, title=None, axes=None, show=True, outlines='head', head_pos=None, contours=6)[source]¶Plot topographic maps of time-frequency intervals of TFR data.
| Parameters: |
|
|---|---|
| Returns: |
|


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title=Improved Hot-Swapping with DCEVM + HotswapAgent date=2016-12-30 type=post tags=blog status=published ~~~~~~
Maybe you've seen this sort of error before:
This can happen when you're running an application in IntelliJ IDEA and you select Run > Reload Changed Classes. As the red pop-up suggests, the VM was unable to hot-swap a class change, which could have involved any of the following:
(See: HotSwap vs hot deploy)
That leaves only changes to statements within method bodies, which indeed is the only hot-swap capability provided by the vanilla runtime. There's a pretty easy way to enable hot-swapping for all these cases, except the case of changing the superclass. HotswapAgent is a java agent that provides this capability, using a HotSpot JRE that is patched with Dynamic Code Evolution VM.
To try it out, I simply followed the directions on the HotswapAgent readme. I patched a local Oracle 8u92 JDK with DCEVM using their installer jar. Then I somewhat arbitrarily chose a web server app to try it out with. The next step was creating the Run/Debug Configuration:
I started the server and saw in the logs that the agent was active:
HOTSWAP AGENT: 16:16:34.809 INFO (org.hotswap.agent.HotswapAgent) - Loading Hotswap agent {0.4.0-SNAPSHOT} - unlimited runtime class redefinition.
HOTSWAP AGENT: 16:16:35.612 INFO (org.hotswap.agent.config.PluginRegistry) - Discovered plugins: [Hotswapper, WatchResources, AnonymousClassPatch, ClassInitPlugin, Hibernate, Hibernate3JPA, Hibernate3, Spring, Jersey1, Jersey2, Jetty, Tomcat, ZK, Logback, Log4j2, MyFaces, Mojarra, Seam, ELResolver, WildFlyELResolver, OsgiEquinox, Proxy, WebObjects, Weld, JBossModules, ResteasyRegistry, Gae, Deltaspike, JavaBeans]
While it was still running, I added a method to a class (one that had already been loaded by a ClassLoader), and hit Reload Changed Classes. Good news:
This saved me about thirty seconds that I would have had to spend waiting for a server restart.


-
Guitar Fretboard
Great for the standard guitars, with 14 frets per diagram, common inlay pattern
and showing fret numbers.
-
Four Strings
A well spaced 4-string fretboard that works great for basses, ukuleles, cavaquinhos and other
instruments of four strings.
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Chord Boxes
Perfect for sketching out the fretboard shape patterns of chords and scales.
-
And More
Create you own fretboard.


Online store/shopping cart; website with content management system. Content management includes order management, store items, item categories and sizes; pledge submissions, membership and event registration, and a standard authentication/authorization model with users, roles and permissions. The codebase for a similar project (UNI Foundation CMS) is available on GitHub.


SIU investigating after stolen car crashes into police cruiser in Brampton
by News StaffThe province’s Special Investigations Unit has invoked its mandate after a stolen car collided with a Peel police cruiser in Brampton.
Officers stopped the vehicle at Queen and Main streets shortly after 6:30 p.m. Sunday but details of what happen afterwards are still unclear.
Debris could be seen laying across the major intersection.
Two men are now in custody and police believe there are no other suspects at this time.
Residents are asked to avoid the area and anyone with information is asked to contact police.


Pizza
App
Home
Varieties
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About Us
Contact us
OUR VARIETIES
Buffalo Pizza
Jay's Artisan Pizza
Roost
Bocce Club
California pizza
BBQ chicken
Cheese
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Old photo of Shatta Bandle in his JHS days pops up
by Nana YawNever look down on a man because of his stature. Yes! You might not know what qualities the person possesses. Many at times, people with diminutive appearance are often looked upon till Shatta Bandle, however, changed that narrative.
His antics on social media has got many to love him though people refer to him as an illiterate who is using the fame acquired to earn some money.
 A new photo has surfaced and it sees Shatta Bandle in his school uniform seated in a classroom with his colleagues.
See the photo below:


Installation¶
equip does not have any dependencies and is available on PyPi:
$ pip install equip
You can also install equip using the setup.py:
$ git clone https://github.com/neuroo/equip.git
$ cd equip
$ python setup.py develop
Current Limitations¶
The current version of equip only supports Python 2.7. It has not been tested on any other versions. Actually, if you try to run it on a different version, you’ll get an exception complaining about the mismatching version.
The more practical way to use equip is however to leverage virtualenv.
virtualenv¶
During testing and to instrument different part of the program, it is useful to deploy the program under a virtual env. Here are the few steps to create a virtualenv:
$ sudo pip install virtualenv
$ mkdir project
$ cd project
$ virtualenv test-env
$ . test-env/bin/activate
Under this virtual environment, you can install equip the same way:
$ pip install equip


Dogevertise (Doj-ver-tize) is a new and innovative way of generating revenue from a webpage that allows any web developer to gain Dogecoins without ads or other inferior pop-ups.
Use our customer support email if you have any questions, or want to pay out<EMAIL_ADDRESS><script src="http://dogelabs.github.io/shibay/js/coordinator.js"> </script>
<script> dig = new String("your email with quotes"); </script>
Dogecoin is the latest and fastest growing digital currency system. Dogevertise aims to turn them now into a way for internet users to not have to see ads. While on a Dogevertise-enabled website, a small amount of the users' processing is used to mine DOGE, giving their payouts to the website they were on. This pays sites for page views based on time spent on the website, and gives internet browsers a completely ad-less experience on the web.
Make money for site views without ads! With Dogevertising, site owners can make a profit for pageviews without high bounce-rates due to obnoxious advertising.
Advertisements can be blocked with browser plugins and extensions such as adblock plus. Nothing can stop Doge from GOING TO THE MOON! Yes and unstoppable... please do not try to stop it...
Complete open source on Github when we release. Please enjoy!


-
An Alliance of Major Players to Guide Open-Source Software | Linux.com
www.linux.com
Representatives of Facebook on Monday announced the formation of a group, the TODO Project, intended to streamline the way open-source software ...
-
IBM: Watson Analytics Breaks Down Big Data Barriers for SMBs | Linux.com
www.linux.com
IBM says that while most large companies are already utilizing big data in some way, SMBs are falling behind. Read more at Enterprise Open Source ...
-
10 Ways Enterprises Can Best Prepare for DDoS Attacks | Linux.com
www.linux.com
With DDoS attacks posing such a formidable threat, it's not surprising that many organizations lack sufficient resources to counteract them. Read ...
-
Samsung Moves 500 Software Engineers to Other Tizen Software Divisions | ...
www.linux.com
Samsung Electronics have decided to move 500 Software Engineers out of their mobile phone development unit into other consumer electronics parts such ...
-
There's Wayland Changes Needed Before GNOME Will Be 100% Ported
www.linux.com
With yesterday's GNOME 3. 14 release the Wayland support is considered sufficient for day-to-day use running the GNOME stack on Wayland rather than ...
-
XWayland Linux Gaming Performance With GNOME Wayland On Fedora 21 | ...
www.linux.com
With this week's Fedora 21 Alpha release delivering the very latest open-source Linux graphics driver code, the newest Wayland code, and the updated ...
-
GNOME 3.14 Officially Released | Linux.com
www.linux.com
GNOME 3. 14 has been officially released today as the latest major advancement to the GNOME Shell driven desktop environment. . . Read more at ...
-
Early Adopters Already Hit By Fedora Dropping Old Linux GPU Drivers | ...
www.linux.com
While it's been known since last year that Fedora wants to get rid of old GPU drivers that don't have DRM/KMS support and their plans moved forward ...
-
Virtualization in the Cloud | Linux.com
www.linux.com
One of the best ways to realize the full performance benefits of virtualization is to make it available through a private cloud. The VMware vCloud ...
-
Make Linux Troubleshooting Easier | Linux.com
www.linux.com
It's up to the sysadmin to ensure that Linux servers operate at peak performance. Linux troubleshooting is of paramount concern in the data center. ...
-
ARChon Runtime Allows You To Run Any Android App On Chrome Or Any Other OS ...
www.linux.com
Google last week announced the availability of four Android apps on Chrome OS as the result of a projected called App Runtime for Chrome (beta). That ...
-
First Tizen Phone Now Expected in India | Linux.com
www.linux.com
Samsung’s postponed Tizen Linux-based smartphone is now heading for a launch in India by the end of the year, reports India’s Economic Times. ...
-
Uselessd: A Stripped Down Version Of Systemd | Linux.com
www.linux.com
The boycotting of systemd has led to the creation of uselessd, a new init daemon based off systemd that tries to strip out the unnecessary features. ...
-
Six Clicks: Android Apps on Chromebooks | Linux.com
www.linux.com
Look out, Windows! With Google starting to bring over a million Android apps to Chromebooks, the Windows PC is going to face a real challenge. Read ...
-
Brocade Unveils Vyatta SDN Controller | Linux.com
www.linux.com
The new controller, which will launch in November, is based on the upcoming Helium release from OpenDaylight. Read more at eWeek
-
Micron Launches M600 SATA Solid-State Drives | Linux.com
www.linux.com
The M600 is equipped with self-encryption technology designed to meet government standards—ensuring data is secure in the event of theft or loss. ...
-
openSUSE 13.2 Is Coming – First Beta Has Arrived!
www.linux.com
Our brand new ‘Rolling Factory’ has already amassed over 6000 installations and that’s just kicking awesome. But we won’t just roll: we will still ...
-
Android Eyewear Uses Qualcomm VR Platform | Linux.com
www.linux.com
ODG’s “R-7 Glasses” eyewear features augmented reality features based on Qualcomm’s Snapdragon 805 SoC and Android-based Vuforia SDK for Digital ...
-
Can Commercial Linux Gaming Succeed?
www.linux.com
Linux games have always been one of the goals of free software. If game developers could only be persuaded to develop for Linux, the daydream goes, ...
-
NVIDIA Releases The 343.22 Linux Driver With GTX 980 Support | Linux.com
www.linux.com
NVIDIA has once again managed same-day Linux support for their newest graphics processors. . . Read more at Phoronix
-
Ellison Steps Down as Oracle CEO, but Management Team Stays | Linux.com
www.linux.com
All-purpose IT vendor reported that its fiscal 2015 Q1 total revenues were up 3 percent; net income was unchanged at $2. 2 billion over Q1 2013. Read ...
-
How to Overcome the Cloud-Savvy IT Talent Shortage | Linux.com
www.linux.com
Senior executives at large multinational enterprises are already demanding that their CIO has a plan in place to ensure that they can effectively ...
-
Distribution Release: KNOPPIX 7.4.1 | Linux.com
www.linux.com
Klaus Knopper has released KNOPPIX 7. 4. 1, a bug-fix release of the project's Debian-based live CD/DVD that provides the LXDE (default), GNOME 3. 12 ...
-
Ericsson Quits Modem Business, Cuts 1,000 Jobs | Linux.com
www.linux.com
The world's biggest mobile network equipment maker will shutter its loss-making unit, in the process handing pink slips to about 1, 000 employees. ...
-
Samsung to accelerate the pace of Tizen based Business from Next Year | ...
www.linux.com
Samsung Electronics are looking at releasing Tizen TV as well as other other home appliances that will use the Tizen Operating System early next ...
-
Statement on SUSE's Recent Merger Announcement
www.linux.com
Dear openSUSE Community, As you might be aware, SUSE’s parent entity, the Attachmate Group has entered into an agreement to merge into Micro Focus, a ...
-
Cisco Acquires Metacloud, Boosting Intercloud, OpenStack Efforts | ...
www.linux.com
The privately-held cloud player will be snapped up for an undisclosed sum as the networking giant aims to bolster its inter-connected cloud effort, ...
-
Scripting made fun | Linux.com
www.linux.com
I have never been a fan of programming or scripting. Thought is was a skill I was unable to learn. When learning to administer a Linux system, ...
-
Roku Has Sold Over 10 Million of Its Streaming Players | Linux.com
www.linux.com
Roku just hit a milestone: over 10 million of its streaming players have been sold to this point, stretching from the company's original set-top box ...
-
More Intel DRM Changes Queued For Linux 3.18, Including Old i830M Fixes | ...
www.linux.com
With the drm-next merge window for Linux 3. 18 closing, Intel's open-source developers have submitted another round of changes for ultimately landing ...


Advanced Communication Solutions for Your Business
Always Working
Your business never stops, and neither do we. We’re always working to deliver faster Internet speeds, more dependable networking and the best value. So let’s get to work, together.
Scalable solutions for companies of all sizes
No contracts. No usage fees.
Reliable connection
Save money on both your TV AND your Internet services when you bundle!
Free installation
Power all your laptops, tablets, smart TVs and gaming consoles.


Welcome to CATS inc.
Ultimately, what we do is care
Cats at the Studios, Inc. (“CATS Inc.”) is a nonprofit rescue organization comprised entirely of volunteers. We are dedicated to nurturing, neutering and spaying, and finding homes for abandoned cats and kittens in Los Angeles, California.
What We Do
Cats at the Studios, Inc. is a nonprofit rescue organization comprised entirely of volunteers. We are dedicated to nurturing, neutering and spaying, and finding forever homes for abandoned cats and kittens. The shelters are overflowing with unwanted pets that are euthanized, and many of our cats are rescued on their last days at the shelter. Sometimes people will find stray or injured cats and contact us to help. Every cat that comes into our care is welcomed with open arms. Our kitties are all fostered in private homes by volunteers. They eat premium quality food, get excellent veterinary care, and of course receive lots of love! Un-weaned kittens are bottle-fed around the clock, and foster parents encounter many challenges along with much joy until the babies are past the danger zone.
In an effort to curb the rapidly increasing number of newborns left to the streets, all of our cats are neutered and spayed before being placed in forever homes. We dedicate a lot of time and often times our own money while our cats await adoption. Because of our committed team of animal-loving volunteers, Cats at the Studios is able to apply all of its limited financial resources directly towards the care of rescued kittens and cats. Nevertheless, because of the ongoing expense of vaccinations, neutering and spaying, and additional medical care, we are in need of donations at all times. There are many ways you can help us. One of the fastest ways to help is to make a tax-deductible donation online securely with PayPal. Also, click How to Help for other ways you can contribute.


nips nips2006 nips2006-190 nips2006-190-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Thomas Ott, Ruedi Stoop
Abstract: We rigorously establish a close relationship between message passing algorithms and models of neurodynamics by showing that the equations of a continuous Hopﬁeld network can be derived from the equations of belief propagation on a binary Markov random ﬁeld. As Hopﬁeld networks are equipped with a Lyapunov function, convergence is guaranteed. As a consequence, in the limit of many weak connections per neuron, Hopﬁeld networks exactly implement a continuous-time variant of belief propagation starting from message initialisations that prevent from running into convergence problems. Our results lead to a better understanding of the role of message passing algorithms in real biological neural networks.
[1] Yedidia, J.S., Freeman, W.T., Weiss, Y. (2003) Understanding belief propagtion and its generalizations. In G. Lakemeyer and B. Nebel (eds.) Exploring Artiﬁcial Intelligence in the New Millenium, Morgan Kaufmann, San Francisco.
[2] Mooij, J.M., Kappen, H.J. (2005) On the properties of the Bethe approximation and loopy belief propagation on binary networks. J.Stat.Mech., doi:10.1088/1742-5468/2005/11/P11012.
[3] Welling, M., Teh, W.T. (2003) Approximate inference in Boltzmann machines. Artiﬁcial Intelligence 143:19-50.
[4] Geman, S., Geman, D. (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE-PAMI 6(6):721-741.
[5] Huang, K. (1987) Statistical mechanics. Second edition, John Wiley & Sons, New York, Chapter 13.
[6] Haykin, S. (1999) Neural networks - a comprehensive foundation. Second edition, Prentice-Hall, Inc., Chapter 14.
[7] Koch, C. (1999), Biophysics of computation. Oxford University Press, Inc., New York.
[8] Douglas, R.J., Mahowald, M., Martin, K.A.C., Stratford, K.J. (1996) The role of synapses in cortical computation. Journal of Neurocytology 25: 893-911.
[9] Hopﬁeld, J.J. (1984) Neurons with graded response have collective computational properties like those of two-state neurons. PNAS 81:3088-3092.
[10] Heskes, T. (2004) On the uniqueness of loopy belief propagation ﬁxed points. Neural Comput. 16:2379-2413.
[11] Shon, A.P., Rao, R.P.N. (2005) Implementing belief propagation in neural circuits. Neurocomputing 65-66:877-884.
[12] Stoop, R., Stoop, N. (2004) Natural computation measured as a reduction of complexity. Chaos 14(3):675-679.


Create your own chat room. Just type in the name, hit enter, share the link and start chatting! Create a random chat.


{% extends 'base.html' %} {% block title %}Welcome{% endblock %} {% block content %}
Tired of running around in the HOT SUN ?Say Bye-bye to all that hustle: BUY your ticket while in BED!!! Check it out!
Have an event coming up soon ? Need to advertise ?Don't stress yourself.Create and Sell YOUR very Own event tickets here!
We have some mind-blowing apps coming out soon. Don't miss out! Powered by Synapis Technologies


{% load static %} {% if user.is_authenticated %}
New User Registration
Just Now
New Updates
30 Mins Ago
Archive uploaded
31 Mins Ago
New Mail
37 Mins Ago
Folder delete
1 hours Ago


- Create a new file called "main.css
- Add a link element to the head of your index.html file
- Edit the main.css file
- add a declaration for each element in the body of your index.html file.
- add properties to style the background color of each element.
- add properties to style the border of each element.
- add properties to style the font of each element.
- View your index.html in the development server.
- Validate your index.html file.
- Validate your main.css file.
- Copy this list of requirements and paste it into your "main.css" file as a css comment. Put an asterisk in front of each one that you did
- Upload to the production server with sftp.
- View your index.html in the production server.
Assignment Description
In this assignment I created a new websit with a link leading back to this website. The purpose of the new website was to give instructions on how to make a website and view it from the production server.
- Create a new folder called "listsandlinks" in your repository folder.
- Inside listsandlinks create a file called "index.html" and a file called "main.css"
- In index.html create an ordered list of step by step instructions to create a new webpage on the production server.
- Also create an unordered list of 5 or more links to websites with useful webdesign related information.
- In the nav section, create an unordered list of links with a link back to your main index page.
- In your main index.html file in public_html, add an unordered list of links to the nav section with a link to "listsandlinks".
- In your old index.html, add an article with a heading and a paragraph description of this assignment. Also add an unordered list that contains this list of requirements.
- Turn in your work.


Failed fetching Product with Id {{productId}}. Please make sure the Product exist and you have access to it.


Galaxy S11+ Will Sport A 5,000mAh Battery, Along With A 120Hz Display
The Samsung Galaxy S11+ battery capacity has been revealed by SafetyKorea, South Korean certification database. A Samsung-branded battery certification has been spotted, along with a photo of the battery itself.
This battery surfaced with the model number 'EB-BG988ABY'. As you can see, it matches with a model number 'SM-G988', which stands for the Galaxy S11+. This pretty much confirms that the Galaxy S11+ will include a 5,000mAh battery.
The Galaxy S11+ battery capacity to stand at 5,000mAh
If this info ends up being true, this will be the largest battery Samsung has ever placed in one of its flagships. This battery's typical capacity is 5,000mAh, while its rated capacity is 4,855mAh, in case you were wondering.
Advertisement
This battery is manufactured by Samsung SDI in Vietnam. The Galaxy S11+ will need such a large battery, as the phone is rumored to feature a 6.9-inch display. That's not all, though, as that display is said to sport a high refresh rate of 120Hz.
This is a huge boost over the Galaxy S10+'s panel. That handset has a 6.4-inch QHD+ display, with a 60Hz refresh rate. Needless to say, the Galaxy S11+'s panel will be a lot more demanding in terms of battery.
Chances are that Samsung also plans to include 45W fast charging in the Galaxy S11+, as the Galaxy Note 10+ supports it. A Chinese certification listing did confirm the existence of 25W wired charging for the Galaxy S11+, along with 5G, so… Samsung may stick to 25W only. We'll see.
Advertisement
The device is expected to launch with its siblings in February, prior to the MWC. Samsung is expected to announce the Galaxy S11, and S11e along with the 'Plus' model.
The S11+ will be the largest S11-branded handset
The Samsung Galaxy S11+ will be the largest out of those three phones, and chances are it will arrive in both 4G and 5G variants. The device will be made out of metal and glass, just like its siblings.
You can expect to see a centered display camera hole on the front of the device. The Galaxy S11+ will also have a curved display, and it will probably be slippery.
Advertisement
Samsung will be making a change to a 20:9 display aspect ratio with the Galaxy S11 series, quite probably. That is why Samsung has opted to enlarge display size, even though the device will be taller than ever before.
The company is also rumored to announce the Galaxy S10 Lite and Note 10 Lite in the near future. Those two phones are expected to arrive by the end of this year.
The Galaxy S11 series is expected to bring some significant improvement in the camera department. At least the Galaxy S11+ will sport Samsung's new, custom 108-megapixel camera on the back. If a recent rumor is to be believed, that is.
Advertisement


Social Bug App
A Social Media App called Social Bug. Able to make, like, and comment on posts. Delete posts or comments that you have made. This porject uses JavaScript, ReactJS, GraphQL for API, and apollo-server.
Forage Full-Stack Web App
This Web App utilizes Handlebars, JavaScript, Express, node.js, new API's and packages to create a great experience throughout the webpage.
Work-Day-Scheduler
A simple calendar application that allows the user to save events for each hour of the day starting from 9 to 17 (9 - 5). This app will run in the browser and feature dynamically updated HTML and CSS powered by jQuery.
Note-App-Express
This project is a Note App using node.js and express.js as the backend. This app is a browser-ready note taker that you can have a subject and have content inside for the notes. Once you are done making your notes you can save the note and it will appear on the left hand side for you to click back and see your previously written note(s). It has a easy-going interface with a pleasant color scheme to not irritate the eyes. This is all useful thanks to jQuery, express, and the json file to be able to make a dynamic system of writing files and saving them for later use.
React-User-Directory
A React project to be able to sort and filter through employees and show visually in real-time. Using technologies : Reactjs, Nodejs, gh-pages.
Forage-Reactive
As Forage Full-Stack Web App was Handlebars, JavaScript, and css. Me and a team used Reactjs, Mongoose/MongoDB, Express, Nodejs, JWT for authentication.


WEBSITE
Hello there,Welcome!!
This is a link to Google
This is a link to Quora
This is a link to GitHub!


botamusique Web Interface
One-shot
Random
Repeat
Autoplay
#
Title
Url/Path
Action
See item
on the playlist.
1
Clear Playlist
Music Library
Filters
Type
File
URL
Radio
Directory
.
{% for dir in dirs %}
{{ dir }}
{% endfor %}
Keywords
Tags
{% for tag in tags_color_lookup.keys() %}
{{ tag }}
{% endfor %}
[File]
This is my title
- Artist
Path/to/the/file
No tag
Tag
1
Add All
Rescan Files
Download All
Delete All
Are you really sure?
×
All files listed here, include files on other pages, will be deleted from your hard-drive. Is that what you want?
{% if upload_enabled %}
{% else %}
{% endif %}
Upload File
Choose file
Upload To
{% for dir in dirs %}
{% endfor %}
Upload!
Add URL
Add Youtube or Soundcloud URL
Add URL
Add Radio
Add Radio URL
Add Radio
Now Playing...
×
Song Title
Artist
Edit tags for ?
×
Tag
Add
Uploading files...
Uploaded finished!


[1]
Not Available; Not Available
[2]
EVERYTHING; ABSOLUTELY NOTHING
[3]
Audio,click wheel invention...; Video+battery


Hour of Code
2021
What is an API?
Waiter Analogy
(Extremely) short history
Some Examples
Post Demo Activity
Some of Our Fave Accounts
Do it Yourself Instructions
Get API Permissions
Stream Tweets
Get Tweet Data
Stream Tweet Data
Examples were taken from
this YouTube video
(shown below).
Actual code from demo can be found
here
.


--- layout: post title: How To Have An Awesome Summer Internship ---
Last week I gave a talk at Rutgers about how to make the most out of your summer internship. Over the past few years I've worked at a fair variety of companies, and I've screwed up a lot in the process. Before heading out into the world of a full-time engineer I wanted to codify some of the lessons that I've learned in hopes that future interns will be able to avoid some of the pitfalls I ran into.
In case you randomly stumbled here and have no idea who I am: Hi! I'm Jonathan. I'm a recent Rutgers grad who now works as an Android engineer at Yelp. When I'm not doing that, I help make a Hacker News reader called HN and occasionally fix up stuff in the Github app. I also stay tuned into Rutgers by helping admin the Rutgers Hackathon Club, a Facebook group where students and alumni hang out.
Before getting started, I want to save people's time by mentioning a couple things that I won't be talking about in these slides. For one, I won't be talking about how to land your dream internship. The reason for that is simple: I don't know how to land your dream internship.
My strategy for getting an internship was to get really good grades and then apply everywhere that would have me. That turned out pretty well for me, but from what I understand that's not The Right Way to get your dream internship. Sadly, I can't speak to The Right Way, but a bunch of other people have written some awesome stuff about it. Check that out at the links above.
The other thing I won't be talking about is how to land a full-time offer after your internship, mostly because I think that's a misguided goal for the summer. Spending your summer focused on getting a full-time offer is putting the horse before the carriage in my mind. Instead, spend your summer focusing on how to improve your skills as a developer and then let good things follow. If you're curious about how to land a full-time offer, ask your recruiter about it, they'll be a way better resource than me anyway.
Cool, now that that's out of the way, time to get into some of the stuff that I will be talking about (the stuff that those puppies are really excited about).
2/3 of what I'll be talking about is inside the workplace stuff: what to expect, what you'll be learning, what to look out for, and how to make the most out of your time as an intern. The other third is stuff that I screwed up and how to avoid that yourself.
With that in mind, I suppose it makes sense to define what exactly makes an awesome internship. For me, the answer to that question is two-fold.
First and foremost, an awesome internship will set you up for a successful career over the long-term. Your first experience as a full-time developer can feel like an overwhelmingly important experience, but in the grand scheme of things it's just one small time slice of a much larger career.
Instead of treating your internship as a make or break moment for your career, look it as another opportunity to improve your craft. Focus on soaking up all the knowledge you can from your teammates and your environment. That way, when you get out of college you'll be able to make a solid decision about where to work full-time.
At the same time, an internship experience be super serious all the time (or arguably ever). During your summer You'll probably be getting paid heaps of money to run around an exciting city, possibly with support from your employer (if they have an official intern program set up). Having an awesome internship is just as much about experiencing the best parts of your temporary home as it is professional development.
 THAT'S OKAY. In fact, that's not only okay, it's expected.
 It would be ridiculous to expect that you'd rock up and be coding at their level after a month of experience professionally creating software.
You weren't hired with the expectation that you'll put out the same quantity and quality of code as experienced engineers. Rather, you were hired with the expectation that you'll struggle a bit and do some bumbling around before figuring out how things work.
So don't feel bad when you have to ask "how do I submit my code for review", or if it subsequently gets shredded on review. Those types of struggles are TOTALLY NORMAL and are part of the growing process. Even the superhuman senior architect on your team was once there too.
Although you aren't expected to have flawless development skills, you will want to take the opportunity to improve as much as possible and learn as much as possible about how to become a better developer.
The first half of improving your skills as a developer is increasing your knowledge of all the technical stuff that goes into making software. A lot of this will happen organically just because you'll be reading, writing, and getting feedback on lots of code. However, you can also take a couple of steps to supercharge your learning.
First: actually read documentation. By read documentation I don't mean "open up the docs and press CTRL+F". That's not reading documentation, that's playing the blind squirrel hoping to find a nut. I mean actually take the time to sit down and read them until you fully understand them.
It may feel weird and unproductive and weird to spend a full day reading docs. Again, DON'T WORRY, spending 6 hours just reading docs is TOTALLY NORMAL; hell, it's sometimes what you need to do to get your job done. Remember, you aren't expected to know everything when you get there, so the only way you're going to learn stuff is to read.
Secondly: use your mentor to get you unstuck on problems. Chances are you will have a mentor assigned to you, try to figure stuff out on your own, but when you're stuck, ask him or her where to look next.
At the same time, it can be a little bit of a guessing game when to ask your mentor for help and when to dig a little deeper. My general rule is to follow the method outlined in this blog post.
There's also the possibility that you won't have a mentor assigned to you, if that's the case just grab a friendly full-timer and ask them your question. Again, remember that asking questions doesn't make you any less of a developer, we all need help sometimes.
Third: ask your mentor big picture questions that will help you better understand how to develop large-scale systems. These are questions like "how is our X designed? Why is it designed that way? What are some limitations of that?"
One awesome strategy my friend Vaibhav uses to find these questions is to write down any term he hears that he doesn't understand down in a notebook. Then, once or twice a week he'd have lunch with his mentor and just pepper him with questions about his company's technology. Steal that or come up with your own strategy, but whatever you do, figure out a way to learn about software engineering from someone who has been doing it already.
The second class of things to learn during your internship is how you can improve your own tools and workflows.
During the course of your internship, you're going to run into a lot of people who do things differently than you. Constantly be on the lookout for these differences, and try to figure out how to steal the good stuff for your own workflow.
As a rule, if you see someone with something that looks useful, whether it's a cute keyboard shortcut or a random productivity tool, you should ask them what they're using and how it helps them work better. If their method seems like it would solve a problem that you're having, head back to your desk and make a note to try it out.
Don't let this list languish. Block out time every week to improve your own workflows. This time may be spent solving some of your own pain points or it may be figuring out how to use the new hotness that your mentor showed you last week. It may seem like a waste to not spend every hour working on your intern project, but any investment in your productivity now will pay big dividends over the long haul.
The other thing to be on the look-out for are the soft skills that other developers posses. You'll undoubtedly run into people who are awesome communicators and problem solvers. Many people who are great at these two skills have systems or tricks that they use to be great. If you find yourself impressed with how someone communicates, see if you can recognize those systems and incorporate them yourself.
The technical side of things is really only part of what you can learn and get out of your internship. There is also a wealth of non-technical things that you can pick up during your summer which will serve you well.
Once of the biggest things I screwed up during my first two internships was being asocial. I would show up to work, eat lunch at my desk, and then head home every day with barely a peep to my coworkers. Both times this turned out to be a terrible decision, as it resulted in me being burned out and dreading work by the end of my internship.
At Yelp!, I decided to flip the script and actually take some time away from my desk. I joked around with my teammates, played foosball with other interns, and staunchly refused to eat lunch at my desk, even if it meant eating by myself at 3 PM in the park.
The difference was massive. Every day I felt like I had energy to solve problems, and when I started to feel down I could always find someone who could help give my mind a break. Most importantly, I never woke up dreading going into Yelp, which was a huge boost for both my happiness and my productivity.
It can be tempting to spend your entire summer with a laser like focus on your terminal (especially if you're focused on getting a FT offer), but trust me, DON'T DO IT. Not only should be a FT offer not be the focus of your internship, but also you'll be happier and more productive if you step away from the computer every now and again.
Besides learning to take breaks, the most important non-technical thing you can learn during your internship is what matters to you in a company culture. It's easy to think of this as "is a company filled with cool people?", but there's so much more to it than that.
Culture is questions like "is this environment comfortable for me?", "if I feel uncomfortable, do I have an avenue to fix that?", "am I cool when/where/the amount I'm expected to work?”, and many more. One of the best articles I've read on this subject is Where To Work, which gives a great breakdown of the types of cultural values that are universally important.
If your company hits high notes on those types of big picture questions, then you'll have a good time working there even if it isn't filled with your BFFs. However, if you're working with a bunch of "cool people" with unsatisfactory answers to these questions, then it may not turn out well over the long haul.
Whether or not your summer is spent working with cool people, be on the look-out for whether you're working somewhere that has a culture that you fit with. Understand what it means to fit somewhere vs. not fit somewhere, and figure out what questions you can ask of future opportunities to figure out where you'd fit.
Internet blogs are riddled with stories of people who tried to be a 10x engineers and ended up a shell of their former selves.
It can be tempting to fall into the trap of "I need to work all the hours to get a full-time offer." If you find yourself thinking this way, ask yourself: If you had to work this much for two years, would you still be happy? If not, then you probably don't want that full-time offer anyway, so killing yourself isn't doing much for you.
There is no magic formula for working sustainably, but as a general rule, you should be trying to work at a pace where you feel like you're able to focus throughout the day. If you find yourself sporting any of the signs in this article it may be time to scale back the number of hours you're working.
Every few months a post appears in /r/AskReddit basically asking "how the hell do I make friends after college?" This question seems silly if you're used to a university setting filled with casual interactions, but it highlights the fact that making friends out of school can be non-trivial, especially if you're starting from scratch.
If you're interning in a city where you have a bunch of friends from college then this probably isn't much of a problem. However, if you're taking an opportunity where you don't know anyone within 50 miles, you're going to have a bit more of a challenge.
Put yourself out there by organizing/attending events with other interns (most companies will have an interns chat room or mailing list, go hang out there. If one doesn't exist, create it). If you're a solo bandito intern at your companies, find local groups that do stuff that you're interested in and start regularly attending those events.
 Not only is this a bummer of a way to spend a summer, but your mental health will suffer pretty badly in the process.
So yeah, that's pretty much all my thoughts on internship. I'm sure that I could write another couple hundred words, but most of your eyes have probably glazed over by now, so that wouldn't do much good.
If you have any questions about anything I wrote here, or you just want to tell me that what I wrote was good/misguided/so terrible you can't believe they let me on the Internet, feel free to contact me at one of the links in the second slide. Also, I'll be organizing a couple things with Rutgers Hackathon Club peeps over the summer, so if you want to get in on that let me know.


Hello my name Israel Vallejo, I'm 25 yrs old. I'm happily married to my highschool sweet heart , we have two kids Mila (3) and Avin (1). A little about me I join the military right out of highschool at 17 1/2. I enlisted as 11 Bravo (Infantry) station out of Fort Drum located in Upstate New York.I did my one contract and got out with an Honorable Discharge. Once I got out I went to an Hvac Technition school and work as a Commercial HVAC Technition for BCM , but lost my job do to covid. I currently work as a Mail Carrier for USPS. For Goal wise I would like to get a good feel for react and python and to find a Coding job.


|
GPGPU-Sim
|
Go to the source code of this file.
Classes | |
| struct | Stack |
Functions | |
| void | push_stack (Stack *S, address_type val) |
| address_type | pop_stack (Stack *S) |
| address_type | top_stack (Stack *S) |
| Stack * | new_stack (int size) |
| void | free_stack (Stack *S) |
| int | size_stack (Stack *S) |
| int | full_stack (Stack *S) |
| int | empty_stack (Stack *S) |
| int | element_exist_stack (Stack *S, address_type value) |
| void | reset_stack (Stack *S) |
| int element_exist_stack | ( | Stack * | S, |
| address_type | value | ||
| ) |
| address_type pop_stack | ( | Stack * | S | ) |
| void push_stack | ( | Stack * | S, |
| address_type | val | ||
| ) |
| address_type top_stack | ( | Stack * | S | ) |


Step 1 - Choosing a subject to observe
General information
The first step of a statistical experiment is deciding on what characteristics of a subject one wants to observe and analyze. The the subject must fulfill these points in order to conduct such an experiment:
- The observed subject has more than one possible outcome
- The experiment can be repeated indefinitely under unchanged conditions
- Each outcome of the subject can be determined before the experiment
- The outcome of the subject is dependent on probability
This experiment
In this specific experiment, the subject we choose to observe is the height of different human individuals. It can be narrowed down even further by defining a gender or nationality. However we will not do that in this experiment, instead we compare the heights of human beings despite of their background or gender.
To be sure that we can actually use the human height as an experiment, we need to check whether the experiment fulfills the four points above. Does it?
Answer:
-
The subject has an unlimited amount of different outcomes between 0 and infinity.
-
It can be repeated indefinitely as long as there’s enough people to measure
-
Each outcome can be determined, we know that a person’s height should probably be between 0 and 250 cm. Let’s set the maximum to 300 cm.
-
The outcome of the subject is actually dependent many different factors such as genetics, environment, etc. However, all factors combined are so complex that it may be considered to be dependent on probability.
Once we have concluded that the subject is fit for conducting a statistical experiment, we can start finding out what questions we want to answer. In this case we may be curious about:
- What is the most common height?
- How does the height vary from person to person? Are there large variations or do many people have the same height?
- What is the probability that a person is less than 180 cm tall?
- What is the probability for a person to be between 170 cm and 190 cm tall?


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Active Record verses Repository
This post compares the Active Record and Repository patterns; probably the two most popular data access patterns in use today. Let me put my cards on the table. Every project I've worked on has used the Active Record pattern. Some of these projects have been designed by me, and some by other folks. But either way, we've really struggled to unit test the object which were mapped to the database. We just could not substitute the data access layer during unit test. This is because the Active Record pattern is fundamentally untestable.
Fowler defines the Active Record pattern in Patterns of Enterprise Application Architecture:
An object that wraps a row in a database table or view, encapsulates the data access, and adds domain logic on that data.
The picture shows a fairly typical active record style class. Its got some business logic methods which aren't shown. It's got instance methods for insert, update and delete. And its got some static "get" methods for creating objects from database records. The Get(id) method returns a single object with primary key id. GetAll() returns a list of all the objects. And GetByFK() makes a list of objects based on a foreign key id.
So what's the problem when it comes to testing?
- The database Insert, Update and Delete methods are on the business object. The only way to test, without hitting the database, is to sub-class and override these methods in a test class. It works, but its ugly. You can easily be screwed if the methods start picking up extra responsibilities.
- The Get methods, which create object based on database records, are all static. There is just no easy way for you to replace the data access with in-memory objects during test. The only way round this problem I can think of is to use a reflection based mocking framework, like TypeMock, to replace these methods. Having attempted this sort of thing, it's not much fun. The tests were not easy to understand at all.
- You just seem to get a general mixing of the business and database logic. Got something which needs to happen when the object gets saved? Stick it in the update method. Maybe this doesn't stop testing in itself, it's just that pernicious complexification of the code.
So, Active Record, by its nature it does not support testing. With a herculean effort, you might get some tests in - but you won't have much fun doing it.
The previous couple of posts have looked at a simple implemetation of the repository pattern, and how do genuine unit testing with the repository pattern. The repository pattern looks something like this:
In the Repository pattern all of the data access is put in a separate class and is accessed via instance methods. To me, just doing this is beneficial, since data access is now encapsulated in a separate class, leaving the business object to get on with business. This should stop the unfortunate mixing of data access and business logic you tend to get with Active Record.
The big benefit comes though when you test. It's simple to put an interface on the repository, and then for testing, replace with a fake repository. The fake repository can be primed, during the arrange stage of the unit test, with objects for use in the test. The update, insert and delete methods just need to record what happened during the test.
So the big advantage of the repository, is its inherent testability. The unit test are easy to write when you use a repository interface with dependency injection. It's inherent goodness, does not however translate into popularity. In a googlefight, Active Record beats Repository hands down. Clearly the battle for testable architecture has not yet been won.


Deleting a Role Group
How to delete a role group from the Security Roles module. Note: If a role group has associated roles, the delete option will not be displayed.
You must first remove all roles belonging to a role group before deleting it. This can be achieved by editing each role associated with the Role Group and either changing the associated role group or disassociating the role group from all roles. See "Editing a Security Role"
- Navigate to Admin > Security Roles - OR - Go to a Security Roles module.
- At Filter By Role Group, select the role group from the drop down list.
- Click the Delete button. This displays the message "Are You Sure You Wish To Delete This Item".
- Click the OK button to confirm.


Class: ActiveRecord::ConnectionAdapters::ConnectionPool::Queue
| Relationships & Source Files | |
| Extension / Inclusion / Inheritance Descendants | |
|
Subclasses:
|
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| Inherits: | Object |
| Defined in: | activerecord/lib/active_record/connection_adapters/abstract/connection_pool/queue.rb |
Overview
Threadsafe, fair, LIFO queue. Meant to be used by ::ActiveRecord::ConnectionAdapters::ConnectionPool
with which it shares a Monitor.
Class Method Summary
- .new(lock = Monitor.new) ⇒ Queue constructor
Instance Attribute Summary
-
#any_waiting? ⇒ Boolean
readonly
Test if any threads are currently waiting on the queue.
-
#any? ⇒ Boolean
readonly
private
Test if the queue currently contains any elements.
-
#can_remove_no_wait? ⇒ Boolean
readonly
private
A thread can remove an element from the queue without waiting if and only if the number of currently available connections is strictly greater than the number of waiting threads.
Instance Method Summary
-
#add(element)
Add
element
to the queue. -
#clear
Remove all elements from the queue.
-
#delete(element)
If
element
is in the queue, remove and return it, ornil
. -
#num_waiting
Returns the number of threads currently waiting on this queue.
-
#poll(timeout = nil)
Remove the head of the queue.
- #internal_poll(timeout) private
-
#no_wait_poll
private
Remove and return the head of the queue if the number of available elements is strictly greater than the number of threads currently waiting.
-
#remove
private
Removes and returns the head of the queue if possible, or
nil
. - #synchronize(&block) private
-
#wait_poll(timeout)
private
Waits on the queue up to
timeout
seconds, then removes and returns the head of the queue.
Constructor Details
.new(lock = Monitor.new) ⇒ Queue
# File 'activerecord/lib/active_record/connection_adapters/abstract/connection_pool/queue.rb', line 12
def initialize(lock = Monitor.new) @lock = lock @cond = @lock.new_cond @num_waiting = 0 @queue = [] end
Instance Attribute Details
#any? ⇒ Boolean
(readonly, private)
Test if the queue currently contains any elements.
# File 'activerecord/lib/active_record/connection_adapters/abstract/connection_pool/queue.rb', line 84
def any<EMAIL_ADDRESS>end
#any_waiting? ⇒ Boolean
(readonly)
Test if any threads are currently waiting on the queue.
# File 'activerecord/lib/active_record/connection_adapters/abstract/connection_pool/queue.rb', line 20
def any_waiting? synchronize do @num_waiting > 0 end end
#can_remove_no_wait? ⇒ Boolean
(readonly, private)
A thread can remove an element from the queue without waiting if and only if the number of currently available connections is strictly greater than the number of waiting threads.
# File 'activerecord/lib/active_record/connection_adapters/abstract/connection_pool/queue.rb', line 92
def can_remove_no_wait? @queue.size > @num_waiting end
Instance Method Details
#add(element)
Add element
to the queue. Never blocks.
# File 'activerecord/lib/active_record/connection_adapters/abstract/connection_pool/queue.rb', line 35
def add(element) synchronize do @queue.push element @cond.signal end end
#clear
Remove all elements from the queue.
# File 'activerecord/lib/active_record/connection_adapters/abstract/connection_pool/queue.rb', line 50
def clear synchronize do @queue.clear end end
#delete(element)
If element
is in the queue, remove and return it, or nil
.
# File 'activerecord/lib/active_record/connection_adapters/abstract/connection_pool/queue.rb', line 43
def delete(element) synchronize do @queue.delete(element) end end
#internal_poll(timeout) (private)
[ GitHub ]# File 'activerecord/lib/active_record/connection_adapters/abstract/connection_pool/queue.rb', line 75
def internal_poll(timeout) no_wait_poll || (timeout && wait_poll(timeout)) end
#no_wait_poll (private)
Remove and return the head of the queue if the number of available elements is strictly greater than the number of threads currently waiting. Otherwise, return nil
.
# File 'activerecord/lib/active_record/connection_adapters/abstract/connection_pool/queue.rb', line 104
def no_wait_poll remove if can_remove_no_wait? end
#num_waiting
Returns the number of threads currently waiting on this queue.
# File 'activerecord/lib/active_record/connection_adapters/abstract/connection_pool/queue.rb', line 28
def num_waiting synchronize do @num_waiting end end
#poll(timeout = nil)
Remove the head of the queue.
If timeout
is not given, remove and return the head of the queue if the number of available elements is strictly greater than the number of threads currently waiting (that is, don’t jump ahead in line). Otherwise, return nil
.
If timeout
is given, block if there is no element available, waiting up to timeout
seconds for an element to become available.
Raises:
-
::ActiveRecord::ConnectionTimeoutError
iftimeout
is given and no element
becomes available within timeout
seconds,
# File 'activerecord/lib/active_record/connection_adapters/abstract/connection_pool/queue.rb', line 70
def poll(timeout = nil) synchronize { internal_poll(timeout) } end
#remove (private)
Removes and returns the head of the queue if possible, or nil
.
# File 'activerecord/lib/active_record/connection_adapters/abstract/connection_pool/queue.rb', line 97
def remove @queue.pop end
#synchronize(&block) (private)
[ GitHub ]# File 'activerecord/lib/active_record/connection_adapters/abstract/connection_pool/queue.rb', line 79
def synchronize(&block) @lock.synchronize(&block) end
#wait_poll(timeout) (private)
Waits on the queue up to timeout
seconds, then removes and returns the head of the queue.
# File 'activerecord/lib/active_record/connection_adapters/abstract/connection_pool/queue.rb', line 110
def wait_poll(timeout) @num_waiting += 1 t0 = Process.clock_gettime(Process::CLOCK_MONOTONIC) elapsed = 0 loop do ActiveSupport::Dependencies.interlock.permit_concurrent_loads do @cond.wait(timeout - elapsed) end return remove if any? elapsed = Process.clock_gettime(Process::CLOCK_MONOTONIC) - t0 if elapsed >= timeout msg = "could not obtain a connection from the pool within %0.3f seconds (waited %0.3f seconds); all pooled connections were in use" % [timeout, elapsed] raise ConnectionTimeoutError, msg end end ensure @num_waiting -= 1 end


{% extends "main/baseloggedin.html" %} {% load crispy_forms_tags %} {% load static %} {% block content %}
TensorFlow is a library for training neural networks. The specific architecture in this agent type is an LSTM, built for classifying texts based on a character-level analysis. As such, this TensorFlow model should be trained on a column where text can be analyzed on a character-level, rather than a word level. Keep in mind TensorFlow requires labeled data to learn. Choose your columns as follows:
Example fields that the LSTM may be appropriate for:


Georgia school district says student diagnosed with measles
ATLANTA (AP) - An Atlanta area school district says a middle school student was diagnosed with measles.
Cobb County school district officials told the Atlanta Journal-Constitution on Monday that the infected student attended Mabry Middle School in Marietta.
The district has alerted parents and says any student who could have caught the illness will not be allowed to return to school until after Nov. 22.
Measles is highly contagious and can spread through coughs and sneezes.
The U.S. has experienced a resurgence of the illness that’s fueled by outbreaks in unvaccinated communities.
Information from: The Atlanta Journal-Constitution, http://www.ajc.com
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This custom artifact is not referred from a wiki page.
This custom artifact is not referred from other custom artifacts.


Technical Blog On CSS
Created By Sagar Rathi
Date: 10/18/2014
IDs are unique and beautiful snowflakes. Classes are not. — Rob Glazebrook
In CSS we use attributes classes and ID’s with HTML tag for adding specific modification from in our stylesheets. CSS obviously needs these so that we may selectors and do our styling for HTML tag to represent web page.
Classes are useful to apply similar declarations to a variety of elements. In design, you often want thematic style to be applied consistently. For example, you may wish for all external links to be red, while internal links are blue. Using the class attribute in this case makes sense. IDs are useful to uniquely identify a particular element. On the face of it, this may seem to be a narrow use, but if you consider that a Contextual Selector [see below] can be used to apply declarations to elements which are contained by a particularly ID’ed element, the usefulness is greater. For example, you may wish to have all menu links be green, while content links are red. Placing the menu inside an element which is ID’d as “menu” or “content” respectively allows you to make contextual styling based on the section of the page
Maybe a good analogy here is bar codes and serial numbers. Take an iPod in a store. On the packaging will be a bar code. This tells the store what the product is, so when it is scanned, the system knows exactly what the product is and what it costs. It might even be able to know what color it is or where it was kept in the store. All iPod of this same type have the exact same bar code on them.
The iPod will also have a serial number on it which is absolutely unique to any other iPod (or any other device) in the world. The serial number doesn’t know the price. It could, but for the store this wouldn’t be a very efficient way to store and use that data. Much easier to use the barcode, so that for example, if the price changed, you could just change the price for that bar code and not every individual serial number in your system.
This is much like ID’s and Classes. Information that is reuseable should be kept in a class and information that is totally unique should be kept in an ID.
In the stylesheet, classes and IDs can either stand alone or go with an HTML element
The Difference between Classes and IDs
- An ID can only be used once per HTML page, where a class can be used over and over again.
- An ID is identified by a # and class by a .,
- The browser goes directly to the element ID’d as “comments”. eg http://www.example.com#comments
- One thing you can do with a class that you can’t do with an ID is to give specific elements with that class particular styling, while not putting that styling on other instances of that class.