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CHAPTER ONE
ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND DEEP LEARNING: HOW THEY ALL RELATE
The History of AI and Machine Learning
Artificial Intelligence and Machine Learning
Where are we now with Artificial Intelligence?
Learning representation from data
What Deep Learning has done so far
The promise of AI
Machine Learning: Just before Deep learning
Probabilistic Modeling
Early Neural networks
Kernel Methods
Why Deep learning?
Hardware
Data
Algorithm
Making Deep Learning easy
CHAPTER TWO
UNDERSTANDING THE NEURAL NETWORK
About Keras
Keras & TensorFlow 2.0
Making your first contact with Keras
Installation & compatibility of Keras
Becoming a Keras expert
The Functional API
Training, evaluation, and inference
Custom losses
Handling losses and metrics that don't fit the standard signature
Automatically setting apart a validation holdout set
Training & evaluation from tf.data Datasets
Using a validation dataset
Other input formats supported
Deploying a keras.utils.Sequence object as input
Using class weighting and sample weighting
Class weights
Sample weights
CHAPTER THREE
Tensor Flow: Single Layer Perceptron
Single Layer Perceptron
Steps to design an algorithm for linear regression
Multi-Layer Perceptrons
Application of TensorFlow
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Passing data to multi-input, multi-output models
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