Deep learning pipeline : building a deep learning model with TensorFlow /

Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You'll learn what a pipeline is and how it works so you can build a full application easily and r...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: El-Amir, Hisham
Otros Autores: Hamdy, Mahmoud
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berkeley, CA : Apress LP, ©2020.
Temas:
Machine learning.
Apprentissage automatique.
Machine learning
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Intro
  • Table of Contents
  • About the Authors
  • About the Technical Reviewer
  • Introduction
  • Part I: Introduction
  • Chapter 1: A Gentle Introduction
  • Information Theory, Probability Theory, and Decision Theory
  • Information Theory
  • Probability Theory
  • Decision Theory
  • Introduction to Machine Learning
  • Predictive Analytics and Its Connection with Machine learning
  • Machine Learning Approaches
  • Supervised Learning
  • Unsupervised Learning
  • Semisupervised Learning
  • Checkpoint
  • Reinforcement Learning
  • From Machine Learning to Deep Learning
  • Lets' See What Some Heroes of Machine Learning Say About the Field
  • Connections Between Machine Learning and Deep Learning
  • Difference Between ML and DL
  • In Machine Learning
  • In Deep Learning
  • What Have We Learned Here?
  • Why Should We Learn About Deep Learning (Advantages of Deep learning)?
  • Disadvantages of Deep Learning (Cost of Greatness)
  • Introduction to Deep Learning
  • Machine Learning Mathematical Notations
  • Summary
  • Chapter 2: Setting Up Your Environment
  • Background
  • Python 2 vs. Python 3
  • Installing Python
  • Python Packages
  • IPython
  • Installing IPython
  • Jupyter
  • Installing Jupyter
  • What Is an ipynb File?
  • Packages Used in the Book
  • NumPy
  • SciPy
  • Pandas
  • Matplotlib
  • NLTK
  • Scikit-learn
  • Gensim
  • TensorFlow
  • Installing on Mac or Linux distributions
  • Installing on Windows
  • Keras
  • Summary
  • Chapter 3: A Tour Through the Deep Learning Pipeline
  • Deep Learning Approaches
  • What Is Deep Learning
  • Biological Deep Learning
  • What Are Neural Networks Architectures?
  • Deep Learning Pipeline
  • Define and Prepare Problem
  • Summarize and Understand Data
  • Process and Prepare Data
  • Evaluate Algorithms
  • Improve Results
  • Fast Preview of the TensorFlow Pipeline
  • Tensors-the Main Data Structure
  • First Session
  • Data Flow Graphs
  • Tensor Properties
  • Tensor Rank
  • Tensor Shape
  • Summary
  • Chapter 4: Build Your First Toy TensorFlow app
  • Basic Development of TensorFlow
  • Hello World with TensorFlow
  • Simple Iterations
  • Prepare the Input Data
  • Doing the Gradients
  • Linear Regression
  • Why Linear Regression?
  • What Is Linear Regression?
  • Dataset Description
  • Full Source Code
  • XOR Implementation Using TensorFlow
  • Full Source Code
  • Summary
  • Part II: Data
  • Chapter 5: Defining Data
  • Defining Data
  • Why Should You Read This Chapter?
  • Structured, Semistructured, and Unstructured Data
  • Tidy Data
  • Divide and Conquer
  • Tabular Data
  • Quantitative​ vs. ​Qualitative​ Data
  • Example-the Titanic
  • Divide and Conquer
  • Making a Checkpoint
  • The Four Levels of Data
  • Measure of Center
  • The Nominal Level
  • Mathematical Operations Allowed for Nominal
  • Measures of Center for Nominal
  • What Does It Mean to be a Nominal Level Type?
  • The Ordinal Level
  • Examples of Being Ordinal
  • What Data Is Like at the Ordinal Level
  • Mathematical Operations Allowed for Ordinal

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