16. Deep Learning

Objectives

• Understand what a neural network is and how it enables deep learning.
• Create Keras neural networks.
• Understand Keras layers, activation functions, loss functions and optimizers.
• Use a Keras convolutional neural network (CNN) trained on the MNIST dataset to recognize handwritten digits.
• Use TensorBoard to visualize the progress of training deep-learning networks.
• Use a Keras recurrent neural network (RNN) trained on the IMDb dataset to perform binary classification of positive and negative movie reviews.

Outline

• 16.1 Introduction
  • 16.1.1 Deep Learning Applications
  • 16.1.2 Deep Learning Demos
  • 16.1.3 Keras Resources
• 16.2 Keras Built-In Datasets
• 16.3 Custom Anaconda Environments
• 16.4 Neural Networks
• 16.5 Tensors
• 16.6 Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset
  • 16.6.1 Loading the MNIST Dataset
  • 16.6.2 Data Exploration
  • 16.6.3 Data Preparation
  • 16.6.4 Creating the Neural Network
  • 16.6.5 Training and Evaluating the Model
  • 16.6.6 Saving and Loading a Model
• 16.7 Visualizing Neural Network Training with TensorBoard
• 16.8 ConvnetJS: Browser-Based Deep-Learning Training and Visualization
• 16.9 Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset
  • 16.9.1 Loading the IMDb Movie Reviews Dataset
  • 16.9.2 Data Exploration
  • 16.9.3 Data Preparation
  • 16.9.4 Creating the Neural Network
  • 16.9.5 Training and Evaluating the Model
• 16.10 Tuning Deep Learning Models
• 16.11 Convnet Models Pretrained on ImageNet Exercises

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