Python: Advanced Guide to Artificial Intelligence : Expert Machine Learning Systems and Intelligent Agents Using Python

Get up to speed with machine learning techniques and create smart solutions for different problems Key Features Master supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation Build deep learning models for object detection, image classification, and similarity learning Develop, deploy, and scale end-to-end deep neural network models in a production environment Book Description Gaining expertise in artificial intelligence requires an in-depth understanding of the most popular machine learning algorithms. With this book, you'll be able to explore the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the most effective way possible. From Bayesian models, to the MCMC algorithm, and even Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll use TensorFlow and Keras to build deep learning models with concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll discover TensorFlow1.x's advanced features, such as distributed TensorFlow with TF clusters, and also understand the deployment of production models with TensorFlow Serving. As you progress, the book will guide you on how to implement techniques related to object classification, object detection, and image segmentation. By the end of this Python book, you'll have gained in-depth knowledge of TensorFlow, along with the skills you need for solving artificial intelligence problems. This Learning Path includes content from the following Packt books: Mastering Machine Learning Algorithms by Giuseppe Bonaccorso Mastering TensorFlow 1.x by Armando Fandango Deep Learning for Computer Vision by Rajalingappaa Shanmugamani What you will learn Get up to speed with how a machine model can be trained, optimized, and evaluated Work with autoencoders and generative adversarial networks Explore the most important reinforcement learning techniques Build end-to-end deep learning (CNN, RNN, and autoencoder) models Define and train a model for image and video classification Deploy your deep learning models and optimize them for high performance Who this book is for This Learning Path is for data scientists, machine learning engineers, and artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve predictions of trained models. Basic knowledge of Python programming and machine learning concepts is required to get the most out of this book. Table of Contents Machine Learning Model Fundamentals Introduction to Semi-Supervised Learning Graph-Based Semi-Supervised Learning Bayesian Networks and Hidden Markov Models EM Algorithm and Applications Hebbian Learning and Self-Organizing Maps Clustering Algorithms Advanced Neural Models Classical Machine Learning with TensorFlow Neural Networks and MLP with TensorFlow and Keras RNN with TensorFlow and Keras CNN with TensorFlow and Keras Autoencoder with TensorFlow and Keras TensorFlow Models in Production with TF Serving Deep Reinforcement Learning Generative Adversarial Networks Distributed Models with TensorFlow Clusters Debugging TensorFlow Models Tensor Processing Units Getting Started Image Classification Image Retrieval Object Detection Semantic Segmentation Similarity Learning Get up to speed with machine learning techniques and create smart solutions for different problems Key Features* Master supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation * Build deep learning models for object detection, image classification, and similarity learning * Develop, deploy, and scale end-to-end deep neural network models in a production environment Book DescriptionGaining expertise in artificial intelligence requires an in-depth understanding of the most popular machine learning algorithms. With this book, you'll be able to explore the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the most effective way possible. From Bayesian models, to the MCMC algorithm, and even Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll use TensorFlow and Keras to build deep learning models with concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll discover TensorFlow1.x's advanced features, such as distributed TensorFlow with TF clusters, and also understand the deployment of production models with TensorFlow Serving. As you progress, the book will guide you on how to implement techniques related to object classification, object detection, and image segmentation. By the end of this Python book, you'll have gained in-depth knowledge of TensorFlow, along with the skills you need for solving artificial intelligence problems. This Learning Path includes content from the following Packt books: * Mastering Machine Learning Algorithms by Giuseppe Bonaccorso * Mastering TensorFlow 1.x by Armando Fandango * Deep Learning for Computer Vision by Rajalingappaa Shanmugamani What you will learn* Get up to speed with how a machine model can be trained, optimized, and evaluated * Work with autoencoders and generative adversarial networks * Explore the most important reinforcement learning techniques * Build end-to-end deep learning (CNN, RNN, and autoencoder) models * Define and train a model for image and video classification * Deploy your deep learning models and optimize them for high performance Who this book is forThis Learning Path is for data scientists, machine learning engineers, and artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve predictions of trained models. Basic knowledge of Python programming and machine learning concepts is required to get the most out of this book. Table of Contents1. Machine Learning Model Fundamentals 2. Introduction to Semi-Supervised Learning 3. Graph-Based Semi-Supervised Learning 4. Bayesian Networks and Hidden Markov Models 5. EM Algorithm and Applications 6. Hebbian Learning and Self-Organizing Maps 7. Clustering Algorithms 8. Advanced Neural Models 9. Classical Machine Learning with TensorFlow 10. Neural Networks and MLP with TensorFlow and Keras 11. RNN with TensorFlow and Keras 12. CNN with TensorFlow and Keras 13. Autoencoder with TensorFlow and Keras 14. TensorFlow Models in Production with TF Serving 15. Deep Reinforcement Learning 16. Generative Adversarial Networks 17. Distributed Models with TensorFlow Clusters 18. Debugging TensorFlow Models 19. Tensor Processing Units 20. Getting Started 21. Image Classification 22. Image Retrieval 23. Object Detection 24. Semantic Segmentation 25. Similarity Learning Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key Features Master supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and more Build, deploy, and scale end-to-end deep neural network models in a production environment Book Description This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: Mastering Machine Learning Algorithms by Giuseppe Bonaccorso Mastering TensorFlow 1.x by Armando Fandango Deep Learning for Computer Vision by Rajalingappaa Shanmugamani What you will learn Explore how an ML model can be trained, optimized, and evaluated Work with Autoencoders and Generative Adversarial Networks Explore the most important Reinforcement Learning techniques Build end-to-end deep learning (CNN, RNN, and Autoencoders) models Who this book is for This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.