Data Science: Modern Deep Learning in Python

Data Science: Modern Deep Learning in Python, Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.
Created by Lazy Programmer Inc.
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What Will I Learn?

Apply momentum to backpropagation to train neural networks

Apply adaptive learning rate procedures like AdaGrad, RMSprop, and Adam to backpropagation to train neural networks

Understand the basic building blocks of Theano

Build a neural network in Theano

Understand the basic building blocks of TensorFlow

Build a neural network in TensorFlow

Build a neural network that performs well on the MNIST dataset

Understand the difference between full gradient descent, batch gradient descent, and stochastic gradient descent

Understand and implement dropout regularization in Theano and TensorFlow

Understand and implement batch normalization in Theano and Tensorflow

Write a neural network using Keras

Write a neural network using PyTorch

Write a neural network using CNTK

Write a neural network using MXNet

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Data Science: Modern Deep Learning in Python

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