- eBook:Artificial Intelligence with Python Cookbook: Practical recipes for next-generation deep learning and neural networks using TensorFlow and PyTorch
- Author:Ben Auffarth
- Data:December 9, 2020
- Pages:286 pages
- Format:PDF, ePUB
- Get up and running with AI (artificial intelligence) in no time using hands-on problem-solving recipes
- Explore widely used Python packages and tools to perform smart automation
- Implement NLP, reinforcement learning, advanced neural networks, and much more to build AI solutions
Book DescriptionWith artificial intelligence systems, we can develop goal-driven agents to automate problem-solving. This involves predicting and classifying the available data and training agents to execute tasks successfully. This book will help you to solve complex AI problems using practical recipes.
The AI with Python book starts by showing you how to install Python and its essential packages and then takes you through the fundamentals of data loading and exploration of datasets. You'll learn how to build probabilistic models and work with heuristic search techniques. You'll also understand how to use deep learning techniques to perform optical character recognition and build models for videos, speech-to-text, and gender recognition. As you advance, the book also covers segmentation techniques, reinforcement learning, neural networks, and genetic programming with the help of independent and insightful recipes. You'll discover AI use cases in industries such as healthcare and insurance and explore techniques such as constraint optimization, reinforcement learning, and online learning. Finally, the book covers examples of deploying models to production.
By the end of this book, you'll be able to identify an AI approach for solving business problems, implement and test it, and deploy it as a service.
What you will learn
- Implement data preprocessing steps and optimize model hyperparameters
- Work with large amounts of data using distributed and parallel computing techniques
- Get to grips with representational learning from images using InfoGAN
- Delve into deep probabilistic modeling with a Bayesian network
- Create your own artwork using adversarial neural networks
- Understand a model's key performance characteristics and bring solutions to production as APIs
- Go from proof to production covering data loading and visualization as well as modeling and deployment as microservices
Who This Book Is ForThis AI book is for Python developers, data scientists, machine learning developers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. If you are looking for state-of-the-art solutions to perform different machine learning tasks in various use cases, this book is for you. Basic working knowledge of Python programming language and machine learning concepts will help you to work with the code.
Chapter 2: Advanced Topics in Supervised Machine Learning
Chapter 3: Patterns, Outliers, and Recommendations
Chapter 4: Probabilistic Modeling
Chapter 5: Heuristic Search Techniques and Logical Inference
Chapter 6: Deep Reinforcement Learning
Chapter 7: Advanced Image Applications
Chapter 8: Working with Moving Images
Chapter 9: Deep Learning in Audio and Speech
Chapter 10: Natural Language Processing
Chapter 11: Artificial Intelligence in Production