Key Features
● Extensive examples of Machine Learning and Deep Learning principles.
● Includes graphical demonstrations and visual tutorials for various libraries, configurations, and settings.
● Numerous use cases with the code snippets and examples are presented.
Description
'Essentials of Deep Learning and AI' curates the essential knowledge of working on deep neural network techniques and advanced machine learning concepts. This book is for those who want to know more about how deep neural networks work and advanced machine learning principles including real-world examples.
This book includes implemented code snippets and step-by-step instructions for how to use them. You'll be amazed at how SciKit-Learn, Keras, and TensorFlow are used in AI applications to speed up the learning process and produce superior results. With the help of detailed examples and code templates, you'll be running your scripts in no time. You will practice constructing models and optimise performance while working in an AI environment.
Readers will be able to start writing their programmes with confidence and ease. Experts and newcomers alike will have access to advanced methodologies. For easier reading, concept explanations are presented straightforwardly, with all relevant facts included.
What you will learn
● Learn feature engineering using a variety of autoencoders, CNNs, and LSTMs.
● Get to explore Time Series, Computer Vision and NLP models with insightful examples.
● Dive deeper into Activation and Loss functions with various scenarios.
● Get the experience of Deep Learning and AI across IoT, Telecom, and Health Care.
● Build a strong foundation around AI, ML and Deep Learning principles and key concepts.
Who this book is for
This book targets Machine Learning Engineers, Data Scientists, Data Engineers, Business Intelligence Analysts, and Software Developers who wish to gain a firm grasp on the fundamentals of Deep Learning and Artificial Intelligence. Readers should have a working knowledge of computer programming concepts.
Table of Contents
1. Introduction
2. Supervised Machine Learning
3. System Analysis with Machine Learning/Un-Supervised Learning
4. Feature Engineering
5. Classification, Clustering, Association Rules, and Regression
6. Time Series Analysis
7. Data Cleanup, Characteristics and Feature Selection
8. Ensemble Model Development
9. Design with Deep Learning
10. Design with Multi Layered Perceptron (MLP)
11. Long Short Term Memory Networks
12. Autoencoders
13. Applications of Machine Learning and Deep Learning
14. Emerging and Future Technologies.