Django 2 Web Development Cookbook: 100 practical recipes on building scalable Python web apps with Django 2,3rd Edition
Authors: Jake Kronika – Aidas Bendoraitis
ISBN-10: 1788837681
ISBN-13: 9781788837682
Publication Date 出版日期: 2018-10-31
Print Length 页数: 544 pages
Book Description
By finelybook
Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models
Explore and master the most important algorithms for solving complex machine learning problems.
Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms,which make even the most difficult things capable of being handled by machines. However,with the advancement in the technology and requirements of data,machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour.
Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised,unsupervised,and semi-supervised machine learning,and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models,this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks.
If you are looking for a single resource to study,implement,and solve end-to-end machine learning problems and use-cases,this is the book you need.
What You Will Learn
Explore how a ML model can be trained,optimized,and evaluated
Understand how to create and learn static and dynamic probabilistic models
Successfully cluster high-dimensional data and evaluate model accuracy
Discover how artificial neural networks work and how to train,optimize,and validate them
Work with Autoencoders and Generative Adversarial Networks
Apply label spreading and propagation to large datasets
Explore the most important Reinforcement Learning techniques
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 Ensemble Learning
9 Neural Networks for Machine Learning
10 Advanced Neural Models
11 Autoencoders
12 Generative Adversarial Networks
13 Deep Belief Networks
14 Introduction to Reinforcement Learning
15 Advanced Policy Estimation Algorithms
