Deep Learning and Machine Learning 2.0 with Python 3.7: : With Artificial intelligence and Natural Language Processing

How to Use This Book
This book is for Students, data scientists, Deep Learning experts and professionals, and researchers in academia who want to understand the understanding of machine learning approaches/algorithms in practice using Python 3.7. This book presents some common machine and deep learning and associated technologies and their relationship. It will help the reader grab some important concepts.

What is Deep Learning 2.0?
Deep learning is a branch of machine learning that has its roots in mathematics, computer science, and neuroscience. Deep networks learn from data the way that babies learn from the world around them, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments.
Deep learning is used at Google today in more than 100 services, from Street View to Inbox Smart Reply and voice search. Several years ago, engineers at Google realized that they had to scale up these compute-intensive applications to cloud levels. Setting out to design a special-purpose chip for deep learning, they cleverly designed the board to fit into a hard disk drive slot in their data centre racks. Google’s tensor processing unit (TPU) is now deployed on servers around the world, delivering an order-of-magnitude improvement in performance for deep learning applications.

Table of Contents
1.The Evolution of Artificial Intelligence and Deep Learning
2.Machine Learning 2.0
3.Deep Learning and Algorithms 2.0
4.The Rise of Machine Learning
5.Machine Learning Advancements and Developments
6.The Practical Concepts of Machine Learning
7.Artificial Intelligence: The New AI Rising
8.Artificial Intelligence Applications
9.Machine Learning and Deep Learning in Action
10.Learning Clusters and Recommendations using unsupervised learning
11.Python 3.7 Tutorial with Examples
12.Using Classes and Objects in Python
13.Natural Language Processing
14.Trends in Deep Learning
15.Machine Learning in Market Research
16.Genetic Algorithm
17.How will AI affect the law?
18.Modern Python Development Environments
19.Convolutional Neural Networks as a Machine Learning Tool

Deep Learning Frameworks & Compute
There are now many popular deep learning frameworks such as Tensorflow, PyTorch, CNTK, MXNet, and Caffe, as well as popular higher-level APIs such as Keras and Gluon. The choice of a deep learning toolkit depends on many factors, including the availability of good tutorials and existing implementations of model architectures and pretrained models, skill sets of the AI talents in the company, flexibility of the toolkit in expressing complex deep neural networks, availability of built-in helper functionalities, ability to effectively leverage both CPUs and GPUs, and ability to perform distributed training.

Evolution of Machine Learning 2.0

Machine learning (ML) is a field of computer science that studies algorithms and techniques for automating solutions to complex problems that are hard to program using conventional programming methods.
Machine learning algorithms have existed for virtually two decades but only in recent times has the computing power and data storage caught up to make machine learning so widely available.

Machine learning focuses on developing algorithms that can learn from the data and make subsequent predictions. For example, when you type into Google "machine learning", it pops up with a list of search results. Machine learning can also be applied independently or be applied to data mining on top of other data mining techniques.
Machine learning algorithms have shown great promise in providing solutions to complex problems. Some of the applications we use every day from searching the Internet to speech recognition are examples of tremendous strides made in realizing the promise of machine learning.

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