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Streaming Video Analysis in Python

Streaming Video Anal...

This was originally posted on the Silicon Valley Data Science blog by authors Matthew Rubashkin Data Engineer at SVDS, and Colin Higgins, Data Scientist at Vevo. At SVDS we have analyzed Caltrain delays in an effort to use real time, publicly available data to improve Caltrain arrival predictions. However, the station-arrival time data from Caltrain was not […]

Faster deep learning with GPUs and Theano

Faster deep learning...

Originally posted by Manojit Nandi, Data Scientist at STEALTHbits Technologies on the Domino data science blog Domino recently added support for GPU instances. To celebrate this release, I will show you how to: Configure the Python library Theano to use the GPU for computation. Build and train neural networks in Python. Using the GPU, I’ll […]

Dropout with Theano

Dropout with Theano...

Almost everyone working with Deep Learning would have heard a smattering about Dropout. Albiet a simple concept (introduced a couple of years ago), which sounds like a pretty obvious way for model averaging, further resulting into a more generalized and regularized Neural Net; still when you actually get into the nitty-gritty details of implementing it […]

How the Multinomial Logistic Regression Model Works

How the Multinomial ...

In the pool of supervised classification algorithms, the logistic regression model is the first most algorithm to play with. This classification algorithm again categorized into different categories. These categories purely based on the number of target classes. If the logistic regression model used for addressing the binary classification kind of problems it’s known as the […]

How the Logistic Regression Model Works in Machine Learning

How the Logistic Reg...

In this article, we are going to learn how the logistic regression model works in machine learning. The logistic regression model is one member of the supervised classification algorithm family. The building block concepts of logistic regression can be helpful in deep learning while building the neural networks. Logistic regression classifier is more like a […]

TensorFlow and Queues

TensorFlow and Queue...

There are many ways to implement queue data structures, and TensorFlow has some of its own. FIFO Queue with a list In Python, a list can implement a first-in first-out (FIFO) queue, with slightly awkward syntax: >>> my_list = [] >>> my_list.insert(0, 'a') >>> my_list.insert(0, 'b') >>> my_list.insert(0, 'c') >>> my_list.pop() 'a' >>> my_list.pop() 'b' […]

Gaussian Naive Bayes Classifier Implementation in Python

Gaussian Naive Bayes...

Building Gaussian Naive Bayes Classifier in Python In this post, we are going to implement the Naive Bayes classifier in Python using my favorite machine learning library scikit-learn. Next, we are going to use the trained Naive Bayes (supervised classification), model to predict the Census Income. As we discussed the Bayes theorem in naive Bayes classifier […]

Here’s What Twitter Was Like During the Super Bowl.

Here’s What Tw...

The Patriots 34-28 win in Super Bowl 51 was, quite possibly, one of greatest football games of all time. It had the largest Super Bowl comeback of all time and was the first to ever to go to overtime. For data-minded folks, the game exhibited striking parallels to the election. ESPN’s live prediction models was saying that Atlanta was almost certain to […]

Implementing a Principal Component Analysis (PCA) in Python, step by step

Implementing a Princ...

Sections Sections Introduction Principal Component Analysis (PCA) Vs. Multiple Discriminant Analysis (MDA) What is a “good” subspace? Summarizing the PCA approach Generating some 3-dimensional sample data Why are we chosing a 3-dimensional sample? 1. Taking the whole dataset ignoring the class labels 2. Computing the d-dimensional mean vector 3. a) Computing the Scatter Matrix 3. […]