Python Advanced Guide to Artificial Intelligence

Demystify the complexity of machine learning techniques and create evolving clever solutions to solve your problemsKey Features://Master supervised unsupervised and semi-supervised ML algorithms and their implementationBuild deep learning models for object detection image classification similarity learning and moreBuild deploy and scale end-to-end deep neural network models in a production environmentbr>/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./br>/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./br>/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/br>/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/br>/What you will learn://Explore how an ML model can be trained optimized and evaluatedWork with Autoencoders and Generative Adversarial NetworksExplore the most important Reinforcement Learning techniquesBuild end-to-end deep learning (CNN RNN and Autoencoders) modelsbr>/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./br>/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./