Book Image

Deep Learning Quick Reference

By : Mike Bernico
Book Image

Deep Learning Quick Reference

By: Mike Bernico

Overview of this book

Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples. You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks. By the end of this book, you will be able to solve real-world problems quickly with deep neural networks.
Table of Contents (15 chapters)

What this book covers

Chapter 1, The Building Blocks of Deep Learning, reviews some basics around the operation of neural networks, touches on optimization algorithms, talks about model validation, and goes over setting up a development environment suitable for building deep neural networks.

Chapter 2, Using Deep Learning to Solve Regression Problems, enables you build very simple neural networks to solve regression problems and explore the impact of deeper more complex models on those problems.

Chapter 3, Monitoring Network Training Using TensorBoard, lets you get started right away with TensorBoard, which is a wonderful application for monitoring and debugging your future models.

Chapter 4, Using Deep Learning to Solve Binary Classification Problems, helps you solve binary classification problems using deep learning.

Chapter 5, Using Keras to Solve Multiclass Classification Problems, takes you to multiclass classification and explores the differences. It also talks about managing overfitting and the safest choices for doing so.

Chapter 6, Hyperparameter Optimization, shows two separate methods for model tuning—one, well-known and battle tested, while the other is a state-of-the-art method.

Chapter 7, Training a CNN From Scratch, teaches you how to use convolutional networks to do classification with images.

Chapter 8, Transfer Learning with Pretrained CNNs, describes how to apply transfer learning to get amazing performance from an image classifier, even with very little data.

Chapter 9, Training an RNN from scratch, discusses RNNs and LSTMS, and how to use them for time series forecasting problems.

Chapter 10, Training LSTMs with Word Embeddings From Scratch, continues our conversation on LSTMs, this time talking about natural language classification tasks.

Chapter 11, Training Seq2Seq Models, helps us use sequence to sequence models to do machine translation.

Chapter 12, Using Deep Reinforcement Learning, introduces deep reinforcement learning and builds a deep Q network that can power autonomous agents.

Chapter 13, Generative Adversarial Networks, explains how to use generative adversarial networks to generate convincing images.