Machine Learning Concepts with Python and the Jupyter Notebook Environment : Using Tensorflow 2.0

Create, execute, modify, and share machine learning applications with Python and TensorFlow 2.0 in the Jupyter Notebook environment. This book breaks down any barriers to programming machine learning applications through the use of Jupyter Notebook instead of a text editor or a regular IDE. You’ll start by learning how to use Jupyter Notebooks to improve the way you program with Python. After getting a good grounding in working with Python in Jupyter Notebooks, you’ll dive into what TensorFlow is, how it helps machine learning enthusiasts, and how to tackle the challenges it presents. Along the way, sample programs created using Jupyter Notebooks allow you to apply concepts from earlier in the book. Those who are new to machine learning can dive in with these easy programs and develop basic skills. A glossary at the end of the book provides common machine learning and Python keywords and definitions to make learning even easier. **What You Will Learn** * Program in Python and TensorFlow * Tackle basic machine learning obstacles * Develop in the Jupyter Notebooks environment **Who This Book Is For** Ideal for Machine Learning and Deep Learning enthusiasts who are interested in programming with Python using Tensorflow 2.0 in the Jupyter Notebook Application. Some basic knowledge of Machine Learning concepts and Python Programming (using Python version 3) is helpful. Understand the fundamental concepts of machine learning with Python and TensorFlow 2.0, within the Jupyter Notebook environment. Even if you're an absolute beginner, develop a strong understanding of the crucial ideas without feeling intimidated by the immensity of the sector. Start with a gentle introduction to artificial intelligence and machine learning to understand how the field has grown over the years and why it is still relevant. Then learn how the notebook interface has become increasingly popular for writing code--with Jupyter Notebook being preferred to a regular text editor or IDE. Once these topics have been covered, you'll dive into the TensorFlow 2.0 library. Obtain a good understanding of what TensorFlow is, and how it has improved from its initial release. You'll be able to compare the two versions in a theoretical as well as practical way, and you'll go through the procedure required to convert code from TensorFlow 1.0 to TensorFlow 2.0. Finally, you will work through projects that use TensorFlow 2.0 with Python and the Jupyter Notebook to help build your own neural networks for deep learning. This will enable you to put everything that you have learned from the book into practice. Each project is given in a step-by-step format for better comprehension Create, execute, modify, and share machine learning applications with Python in the Jupyter Notebook environment. This book breaks down any barriers to programming machine learning applications through the use of Jupyter Notebooks instead of a text editor or a regular IDE. Youll start by learning fundamental concepts in Python necessary for working with machine learning application development. Then use Jupyter Notebooks to improve the way you program with Python. After grounding your skills in working with Python in Jupyter Notebooks, youll dive into what TensorFlow is, how it helps machine learning enthusiasts, and how to tackle the challenges it presents. Along the way, sample programs created using Jupyter Notebooks allow you to apply concepts from earlier in the book. Those who are new to machine learning can start in with these easy programs and develop basic skills. A glossary at the end of the book provides common machine learning and Python keywords and definitions to make learning even easier. You will: Program machine learning models in Python Tackle basic machine learning obstacles Develop in the Jupyter Notebooks environment Front Matter ....Pages i-xxvii Front Matter ....Pages 1-1 An Overview of Artificial Intelligence (Nikita Silaparasetty)....Pages 3-19 An Overview of Machine Learning (Nikita Silaparasetty)....Pages 21-39 Introduction to Deep Learning (Nikita Silaparasetty)....Pages 41-56 Machine Learning vs. Deep Learning (Nikita Silaparasetty)....Pages 57-65 Machine Learning With Python (Nikita Silaparasetty)....Pages 67-87 Front Matter ....Pages 89-89 Introduction to Jupyter Notebook (Nikita Silaparasetty)....Pages 91-118 Python Programming in Jupyter Notebook (Nikita Silaparasetty)....Pages 119-145 Front Matter ....Pages 147-147 The Tensorflow Machine Learning Library (Nikita Silaparasetty)....Pages 149-171 Programming with Tensorflow (Nikita Silaparasetty)....Pages 173-189 Introducing Tensorflow 2.0 (Nikita Silaparasetty)....Pages 191-213 Machine Learning Programming with Tensorflow 2.0 (Nikita Silaparasetty)....Pages 215-277 Back Matter ....Pages 279-290