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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