Modern Deep Learning in Python

Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.

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

Requirements

Be comfortable with Python, Numpy, and Matplotlib. Install Theano and TensorFlow.
If you do not yet know about gradient descent, backprop, and softmax, take my earlier course, deep learning in Python, and then return to this course.

Who is the target audience?

Students and professionals who want to deepen their machine learning knowledge
Data scientists who want to learn more about deep learning
Data scientists who already know about backpropagation and gradient descent and want to improve it with stochastic batch training, momentum, and adaptive learning rate procedures like RMSprop
Those who do not yet know about backpropagation or softmax should take my earlier course, deep learning in Python, first

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