Deep learning in practice

"Deep Learning in Practice helps you learn how to develop and optimize a model for your projects using Deep Learning (DL) methods and architectures. This book is useful for undergraduate and graduate students, as well as practitioners in industry and academia. It will serve as a useful reference for learning deep learning fundamentals and implementing a deep learning model for any project, step by step"--

"A Chapman & Hall book."

PrefaceAcknowledgementsAbout the Author1. Introduction 1.1 What is Learning?1.2 What is Machine Learning?1.3 What is Deep Learning?1.4 About this Book!1.4.1 Introduction1.4.2 Python /NumPy1.4.3 TensorFlow and Keras fundamentals1.4.4 Artificial Neural Networks (ANNs) Fundamentals and Architectures1.4.5 Deep Neural Networks (DNNs) Fundamentals and Architectures1.4.6 Deep Neural Networks for Images and Audio Data Analysis1.4.7 Deep Neural Networks for Virtual Assistant Robots1.4.8 What is the best model?2. Python/NumPy Fundamentals2.1 Python2.1.1 Variables2.1.2 Keywords2.1.3 Operators and Operand2.1.4 Statements and Expressions2.1.5 Sequence2.1.6 For Loop2.1.7 While Loop2.1.8 String2.1.9 List2.1.10 Dictionary2.1.11 Tuple2.1.12 Sets2.1.13 Function2.1.14 File2.1.15 Object (class)2.2 NumPy2.2.1 Create Array2.2.2 ndarray2.2.3 Access Elements2.2.4 Array Slicing2.2.5 Data Type2.2.6 Array Data Check2.2.7 Shape and Reshape Array2.2.8 Array Iterating2.2.9 Joining Array2.2.10 Splitting Array2.2.11 Searching Arrays2.2.12 Sorting Arrays2.2.13 Filter Array2.2.14 Random Numbers2.2.15 Array Vectorization2.2.16 np.zeros and np.ones2.2.17 hstack and vstack2.2.18 Generate Random Numbers2.2.19 Mathemat…