Modern Deep Learning in Python

Modern Deep Learning in Python Free Download. Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.

Modern Deep Learning in Python Description

Requirements

  • Be comfortable with Python, Numpy, and Matplotlib
  • 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.

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What you’ll 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

Who this course is for:

  • 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

Modern Deep Learning in Python Free Download

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