Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition

Cover
Copyright
Packt Page
Contributors
Table of Contents
Preface
Chapter 1: Welcome to the Robot World
	Beginning the AI journey
	Four different AI models
		The models in practice
			Fundamentals
			Thompson Sampling
			Q-learning
			Deep Q-learning
			Deep convolutional Q-learning
	Where can learning AI take you?
		Energy
		Healthcare
		Transport and logistics
		Education
		Security
		Employment
		Smart homes and robots
		Entertainment and happiness
		Environment
		Economy, business, and finance
	Summary
Chapter 2: Discover Your AI Toolkit
	The GitHub page
	Colaboratory
	Summary
Chapter 3: Python Fundamentals – Learn How to Code in Python
	Displaying text
		Exercise
	Variables and operations
		Exercise
	Lists and arrays
		Exercise
	if statements and conditions
		Exercise
	for and while loops
		Exercise
	Functions
		Exercise
	Classes and objects
		Exercise
	Summary
Chapter 4: AI Foundation Techniques
	What is Reinforcement Learning?
	The five principles of Reinforcement Learning
		Principle #1 – The input and output system
		Principle #2 – The reward
		Principle #3 – The AI environment
		Principle #4 – The Markov decision process
		Principle #5 – Training and inference
			Training mode
			Inference mode
	Summary
Chapter 5: Your First AI Model – Beware the Bandits!
	The multi-armed bandit problem
	The Thompson Sampling model
		Coding the model
		Understanding the model
		What is a distribution?
		Tackling the MABP
		The Thompson Sampling strategy in three steps
		The final touch of shaping your Thompson Sampling intuition
		Thompson Sampling against the standard model
	Summary
Chapter 6: AI for Sales and Advertising – Sell like the Wolf of AI Street
	Problem to solve
	Building the environment inside a simulation
		Running the simulation
		Recap
	AI solution and intuition refresher
		AI solution
		Intuition
	Implementation
		Thompson Sampling vs. Random Selection
			Performance measure
		Let\'s start coding
		The final result
	Summary
Chapter 7: Welcome to Q-Learning
	The Maze
		Beginnings
		Building the environment
			The states
			The actions
			The rewards
		Building the AI
			The Q-value
			The temporal difference
			The Bellman equation
			Reinforcement intuition
	The whole Q-learning process
		Training mode
		Inference mode
	Summary
Chapter 8: AI for Logistics – Robots in a Warehouse
	Building the environment
		The states
		The actions
		The rewards
		AI solution refresher
			Initialization (first iteration)
			Next iterations
	Implementation
		Part 1 – Building the environment
		Part 2 – Building the AI Solution with Q-learning
		Part 3 – Going into production
		Improvement 1 – Automating reward attribution
		Improvement 2 – Adding an intermediate goal
	Summary
Chapter 9: Going Pro with Artificial Brains – Deep Q-Learning
	Predicting house prices
		Uploading the dataset
		Importing libraries
		Excluding variables
		Data preparation
			Scaling data
		Building the neural network
		Training the neural network
		Displaying results
	Deep learning theory
		The neuron
			Biological neurons
			Artificial neurons
		The activation function
			The threshold activation function
			The sigmoid activation function
			The rectifier activation function
		How do neural networks work?
		How do neural networks learn?
		Forward-propagation and back-propagation
		Gradient Descent
			Batch gradient descent
			Stochastic gradient descent
			Mini-batch gradient descent
	Deep Q-learning
		The Softmax method
		Deep Q-learning recap
		Experience replay
		The whole deep Q-learning algorithm
	Summary
Chapter 10: AI for Autonomous Vehicles – Build a Self-Driving Car
	Building the environment
		Defining the goal
		Setting the parameters
		The input states
		The output actions
		The rewards
	AI solution refresher
	Implementation
		Step 1 – Importing the libraries
		Step 2 – Creating the architecture of the neural network
		Step 3 – Implementing experience replay
		Step 4 – Implementing deep Q-learning
	The demo
		Installing Anaconda
		Creating a virtual environment with Python 3.6
		Installing PyTorch
		Installing Kivy
	Summary
Chapter 11: AI for Business – Minimize Costs with Deep Q-Learning
	Problem to solve
	Building the environment
		Parameters and variables of the server environment
		Assumptions of the server environment
			Assumption 1 – We can approximate the server temperature
			Assumption 2 – We can approximate the energy costs
		Simulation
		Overall functioning
		Defining the states
		Defining the actions
		Defining the rewards
		Final simulation example
	AI solution
		The brain
		Implementation
		Step 1 – Building the environment
		Step 2 – Building the brain
			Without dropout
			With dropout
		Step 3 – Implementing the deep reinforcement learning algorithm
		Step 4: Training the AI
			No early stopping
			Early stopping
		Step 5 – Testing the AI
	The demo
	Recap – The general AI framework/Blueprint
	Summary
Chapter 12: Deep Convolutional Q-Learning
	What are CNNs used for?
	How do CNNs work?
		Step 1 – Convolution
		Step 2 – Max pooling
		Step 3 – Flattening
		Step 4 – Full connection
	Deep convolutional Q-learning
	Summary
Chapter 13: AI for Games – Become the Master at Snake
	Problem to solve
	Building the environment
		Defining the states
		Defining the actions
		Defining the rewards
	AI solution
		The brain
		The experience replay memory
	Implementation
		Step 1 – Building the environment
		Step 2 – Building the brain
		Step 3 – Building the experience replay memory
		Step 4 – Training the AI
		Step 5 – Testing the AI
	The demo
		Installation
		The results
	Summary
Chapter 14: Recap and Conclusion
	Recap – The general AI framework/blueprint
	Exploring what\'s next for you in AI
		Practice, practice, and practice
		Networking
		Never stop learning
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