A comprehensive guide to advanced deep learning techniques, including Autoencoders, GANs, VAEs, and Deep Reinforcement Learning, that drive today’s most impressive AI results.
Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you’ll find hands-on projects throughout that show you how to create more effective AI with the latest techniques.
The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and Tensorflow, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create Autoencoders. You then learn all about Generative Adversarial Networks (GANs), and how they can open new levels of AI performance. Variational AutoEncoders (VAEs) are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans – a major stride forward for modern AI. To complete this set of advanced techniques, you’ll learn how to implement Deep Reinforcement Learning (DRL) such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.
What you will learn:
- Cutting-edge techniques in human-like AI performance
- Implement advanced deep learning models using Keras
- The building blocks for advanced techniques – MLPs, CNNs, and RNNs
- Deep neural networks – ResNet and DenseNet
- Autoencoders and Variational AutoEncoders (VAEs)
- Generative Adversarial Networks (GANs) and creative AI techniques
- Disentangled Representation GANs, and Cross-Domain GANs
- Deep Reinforcement Learning (DRL) methods and implementation
- Produce industry-standard applications using OpenAI gym
- Deep Q-Learning and Policy Gradient Methods
Grasp the fundamentals of Artificial Intelligence and build your own intelligent systems with ease
Hands-On Artificial Intelligence for Beginners will teach you what Artificial Intelligence is and how to design and build intelligent applications. This book will teach you to harness packages such as TensorFlow in order to create powerful AI systems. You will begin with reviewing the recent changes in AI and learning how artificial neural networks (ANNs) have enabled more intelligent AI. You’ll explore feedforward, recurrent, convolutional, and generative neural networks (FFNNs, RNNs, CNNs, and GNNs), as well as reinforcement learning methods. In the concluding chapters, you’ll learn how to implement these methods for a variety of tasks, such as generating text for chatbots, and playing board and video games.
By the end of this book, you will be able to understand exactly what you need to consider when optimizing ANNs and how to deploy and maintain AI applications.
What you will learn:
- Use TensorFlow packages to create AI systems
- Build feedforward, convolutional, and recurrent neural networks
- Implement generative models for text generation
- Build reinforcement learning algorithms to play games
- Assemble RNNs, CNNs, and decoders to create an intelligent assistant
- Utilize RNNs to predict stock market behavior
- Create and scale training pipelines and deployment architectures for AI systems
Insightful projects to master deep learning and neural network architectures using Python and Keras
Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system.
Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects.
By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way
What you will learn:
- Set up a deep learning development environment on Amazon Web Services (AWS)
- Apply GPU-powered instances as well as the deep learning AMI
- Implement seq-to-seq networks for modeling natural language processing (NLP)
- Develop an end-to-end speech recognition system
- Build a system for pixel-wise semantic labeling of an image
- Create a system that generates images and their regions