Python: Advanced Guide to Artificial Intelligence: Expert machine learning systems and intelligent agents using Python
EPUB
 eBook:Python: Advanced Guide to Artificial Intelligence: Expert machine learning systems and intelligent agents using Python
 Author:Giuseppe Bonaccorso, Armando Fandango, Rajalingappaa Shanmugamani
 Edition:
 Categories:
 Data:December 21, 2018
 ISBN:1789957214
 ISBN13:9781789957211
 Language:English
 Pages:764 pages
 Format:EPUB
Key Features
 Master supervised, unsupervised, and semisupervised ML algorithms and their implementation
 Build deep learning models for object detection, image classification, similarity learning, and more
 Build, deploy, and scale endtoend 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 semisupervised 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 Pythonbased 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 indepth knowledge of TensorFlow, making you the goto 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 endtoend 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.

Content
2. Introduction to SemiSupervised Learning
3. GraphBased SemiSupervised Learning
4. Bayesian Networks and Hidden Markov Models
5. EM Algorithm and Applications
6. Hebbian Learning and SelfOrganizing 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
Free sample
