▶Book Description
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.
▶What You Will Learn
⦁ Solve regression and classification challenges with TensorFlow and Keras
⦁ Learn to use Tensor Board for monitoring neural networks and its training
⦁ Optimize hyperparameters and safe choices/best practices
⦁ Build CNN's, RNN's, and LSTM's and using word embedding from scratch
⦁ Build and train seq2seq models for machine translation and chat applications.
⦁ Understanding Deep Q networks and how to use one to solve an autonomous agent problem.
⦁ Explore Deep Q Network and address autonomous agent challenges.
▶Key Features
⦁ A quick reference to all important deep learning concepts and their implementations
⦁ Essential tips, tricks, and hacks to train a variety of deep learning models such as CNNs, RNNs, LSTMs, and more
⦁ Supplemented with essential mathematics and theory, every chapter provides best practices and safe choices for training and fine-tuning your models in Keras and Tensorflow.
▶Who This Book Is For
I'm a practicing data scientist, and I'm writing this book keeping other practicing data scientists and machine learning engineers in mind. If you're a software engineer applying deep learning, this book is also for you.
If you're a deep learning researcher, then this book isn't really for you; however, you should still pick up a copy so that you can criticize the lack of proofs and mathematical rigor in this book.
If you're an academic or educator, then this book is definitely for you. I've taught a survey source in data science at the University of Illinois at Springfield (go Prairie Stars!) for the past 3 years, and in doing so, I've had the opportunity to inspire a number of future machine learning people. This experience has inspired me to create this book. I think a book like this is a great way to help students build interest in a very complex topic.
▶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.
