Deep Learning in Depth Bundle
Deep Learning Quick Reference
Dive deeper into neural networks and get your models trained, optimized with this quick reference guide
About This Book
- 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
If you are a Data Scientist or a Machine Learning expert, then this book is a very useful read in training your advanced machine learning and deep learning models. You can also refer this book if you are stuck in-between the neural network modeling and need immediate assistance in getting accomplishing the task smoothly. Some prior knowledge of Python and tight hold on the basics of machine learning is required.
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.
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.
Style and approach
An easy-to-follow, step-by-step guide to help you get to grips with real-world applications of training deep neural networks.
About this Bundle
Dive into neural networks and understand how to apply your new-found knowledge to applications, games and more with the Deep Learning in Depth Bundle.
With up to 20 eBooks to choose from across three tiers, including 17 new-to-Fanatical titles, you’ll get to grips with applying modern deep learning techniques to build and train deep neural networks through a variety of resources including Python, Tensorflow and PyTorch.
In Tier One, you’ll receive five eBooks which take you through essential techniques of deep learning and how to best put it into practice. This tier includes Hands-On Neural Networks, in which you’ll learn algorithms to solve common problems using back propagation and perceptrons, as well as Understand how to apply neural networks to applications with the help of useful illustrations.
Opt for Tier Two of the bundle and you’ll have an additional five eBooks to learn from, with step-by-step guides on design principles of reinforcement learning and deep reinforcement learning models and much more. With Reinforcement Learning Algorithms with Python, you’ll learn, develop, and deploy advanced reinforcement learning algorithms to solve a variety of tasks, as well as get to grips with evolution strategies for solving the lunar lander problem.
Choose Tier Three and you’ll receive all 20 eBooks, helping you expand your knowledge of deep learning and how it can play a huge part in your day to day role, whether that’s as a AI researcher/developer, Data scientist, or game developer.
With Python Reinforcement Learning Projects, you’ll explore the power of TensorFlow to build self-learning models and apply advanced deep RL algorithms to games such as Minecraft. There’s also Deep Reinforcement Learning Hands-On, in which you’ll master deep reinforcement learning (RL), from the first principles to the latest algorithms, as well as creating your own OpenAI Gym environment to train a stock trading agent.
The eBooks included in this bundle are available in EPUB, MOBI and PDF formats.