Part One: Introduction
Prepares the readers with the prerequisites needed.
Chapter 1: Tools, Theories, and Equations
This chapter provides the big picture that shows the audience the field that the book describes. Introduces the mathematical equations and notations that describe how machine;earning works, the programming tools and packages needed in this book, and some theories.
- Probability Theory, Decision Theory and Information Theory
- Introduction to machine learning
o What is machine learning
o What is deep learning
- From machine learning to deep learning
- Mathematical notation
- Python installation
o Python and Jupyter
oCommon Deep-Learning Packages
oTensorFlow Installation
- Summary
-
Chapter 2: A Tour Through the Deep Learning Pipeline
In chapter two, we introduce the pipeline. What are the deep learning approaches and related sub-fields. What are the steps of a deep learning pipeline. And what are the extras added to TensorFlow that make it unique compared to other deep learning frameworks.
● Deep Learning Approaches
● Deep Learning Pipeline
o Data
oGoals
oModels
oFeatures
oModel Evaluation
● Fast preview of the TensorFlow pipeline
● Summary
Chapter 3: Build Your First Toy TensorFlow App
To make sure that we don't drop the audience into the middle things without setup, we will show them a small example using TensorFlow that quickly introduces each step of the deep learning pipeline. And make sure that the audience knows each step of the pipeline, how it is important, and how to use it.
- TensorFlow Basics for Development
- XOR Implementation Using TensorFlow
- Linear Regression in TensorFlow
- Summary
Part Two: Data
Covers everything about data. From data collection to understanding intuition to data processing and preparation.
Chapter 4: Defining Data
This chapter as its name suggests is about defining data. Readers should know the type of data they are dealing with so they can choose the right approach for preparing that data.
- Defining Data
- Why should you read this chapter?
- Structured, semi-structured, and unstructured data
- Divide and Conquer
- The types of data you will deal with
o Tabular (Numerical and Categorical)
- Quantitative versus Qualitative data
- The four levels of data
- Nominal level
- Ordinal level
- Interval level
- Ratio level
- Example - Titanic
o Text
- Example - Classifying IMDB Movie Reviews o Images
- Type of images (2D, 3D, 4D)
- Example - CIFAR-10
- Quick recap and check
- Summary
Chapter 5: Data Wrangling and Preprocessing
After understanding the data, readers now choose the approaches and methodologies for preparing it.
- The deep learning pipeline revisited
- Data loading and preprocessing
o Data Loading with Numpy
o Data Loading with Pandas
- Missing and Noisy Data
- Dealing with big datasets
- Accessing other data formats
- Data preprocessing
- Data a
About the Author: Hisham Elamir is a data scientist with expertise in machine learning, deep learning, and statistics. He currently lives and works in Cairo, Egypt. In his work projects, he faces challenges ranging from natural language processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meetups, conferences, and other events.
Mahmoud Hamdy is a machine learning engineer who works in Egypt and lives in Egypt, His primary area of study is the overlap between knowledge, logic, language, and learning. He works helping train machine learning, and deep learning models to distil large amounts of unstructured, semi-structured, and structured data into new knowledge about the world by using methods ranging from deep learning to statistical relational learning. He applies strong theoretical and practical skills in several areas of machine learning to finding novel and effective solutions for interesting and challenging problems in such interconnections
