Python: Advanced Guide to Artificial Intelligence: Expert machine learning systems and intelligent agents using Python
Authors: Giuseppe Bonaccorso – Armando Fandango – Rajalingappaa Shanmugamani
ISBN-10: 1789957214
ISBN-13: 9781789957211
Released: 2018-12-21
Print Length 页数: 764 pages
Book Description
Demystify the complexity of machine learning techniques and create evolving,clever solutions to solve your problems
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
contents
1 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
