If deep learning is in place, acceleration comes first.

Deep learning is cool and very applicable to some problems, but it takes a lot of time to train a deep net.

To speed up this process accelerated hardware, such as Google’s Tensor Processing Unit (TPU) or Nvidia GPU can be used.

Why did deep learning become so popular?

  • Increase in computer processing capabilities
  • Massive amounts of data
  • Advances in machine learning algorithms and dramatic increases in computer research

Deep learning pipeline consists of three components: (1) preprocessing the data, (2) training the model and (3) deployment of the model

The pipeline is slow, because:

  • The process…


All you need to know about Restricted Boltzmann Machines

Another type of networks used in deep learning are Restricted Boltzmann Machines (RBM). RBMs are shallow networks used for data reconstruction and feature extraction.

Common applications of RBMs:

• feature extraction
• dimensionality reduction
• pattern recognition
• recommendation systems
• missing values handling
• topic modeling

Architecture

Structurally, an RBM is a shallow neural net with just two layers — the visible layer and the hidden layer. RBM is used for finding patterns and reconstructing the input in an unsupervised manner. The nodes are connected to each other across layers, but…


Introduction to Recurrent Neural Networks

A Recurrent Neural Network (RNN) is a type of deep learning approach used for modeling sequential data. The data is sequential, if the points in a dataset are dependent on the previous points, each data point representing an observation at a certain point in time.

RNN is useful for sentiment analysis, prediction of the next word in a sentence, translation of multiple words or speech-to-text conversion. Traditional neural network typically can’t handle sequential data, as it assumes that each data point is idependent of the others. …


Introduction to Convolutional Neural Networks

Why we need CNN?

Assume that you have a dataset of cats and dogs, and you want to build a model that can recognize and classify them, given an unseen image of a cat or a dog.The first step is feature extraction, i.e. choosing the best features from your images, such as color, object edges, pixel location, etc. The second step is classification into cats and dogs using the extracted features.

CNN for Cats vs Dogs

Convolutional Neural Networks can automatically find those features and classify the images for you.

A good CNN solution is required:

  • To detect the objects in the image and…


The Anatomy of Deep Learning

What is TensorFlow?

TensorFlow is an open source library developed by the Google Brain Team, originally created for tasks that require heavy numerical computations. This is why it is very useful for machine learning applications. It has a C++ backend and is able to run faster than pure Python code.

TensorFlow application uses a structure known as a data flow graph. …


Data Science in the Real World

Things that separate Russian standup from American

Humor is universal, but it can be expressed and perceived differently.

I’m a big fan of stand ups and to me those are more than just funny monologues. Stand-ups are able disclose many customs, lifestyles and values that characterize a society.

I conducted text analysis on Russian and American stand-up monologues and came up with some interesting insights. The full project can be accessed here: https://github.com/thegrigorian/HumorSentiments

Data

I took the top standup search results from YouTube and scraped the transcript using the R package “youtubecaption” (https://github.com/jooyoungseo/youtubecaption).

For sentiment analysis, “RuSentiLex (http://www.labinform.ru/pub/rusentilex/index.htm) dictionary was added as the Russian lexicon.

Preparation

First we…

thegrigorian

I am a data enthusiast willing to share my fresh projects with the world.

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