Introduction to Deep Learning and Neural Networks with Python™: A Practical Guide

Front-Matter_2021_Introduction-to-Deep-Learning-and-Neural-Networks-with-Pyt
	Front Matter
Copyright_2021_Introduction-to-Deep-Learning-and-Neural-Networks-with-Python
	Copyright
Dedication_2021_Introduction-to-Deep-Learning-and-Neural-Networks-with-Pytho
	Dedication
Preface_2021_Introduction-to-Deep-Learning-and-Neural-Networks-with-Python-
	Preface
Acknowledgment_2021_Introduction-to-Deep-Learning-and-Neural-Networks-with-P
	Acknowledgments
		Ahmed Fawzy Gad
		Fatima Ezzahra Jarmouni
Chapter-1---Preparing-the-devel_2021_Introduction-to-Deep-Learning-and-Neura
	Preparing the development environment
		Downloading and installing Python™ 3
		Installing required libraries
		Preparing Ubuntu® virtual machine for Kivy
		Preparing Ubuntu® virtual machine for PyPy
		Conclusion
Chapter-2---Introduction-to-artific_2021_Introduction-to-Deep-Learning-and-N
	Introduction to artificial neural networks (ANN)
		Simplest model Y  =  X
		Error calculation
		Introducing weight
			Weight as a constant
			Weight as a variable
		Optimizing the parameter
		Introducing bias
			Bias as a constant
			Bias as a variable
		Optimizing the weight and the bias
		From mathematical to graphical form of a neuron
		Neuron with multiple inputs
		Sum of products
		Activation function
		Conclusion
Chapter-3---ANN-with-1-input_2021_Introduction-to-Deep-Learning-and-Neural-N
	ANN with 1 input and 1 output
		Network architecture
		Forward pass
		Forward pass math calculations
		Backward pass
		Chain rule
		Backward pass math calculations
		Python™ implementation
			Necessary functions
			Preparing inputs and outputs
			Forward pass
			Backward pass
			Training network
		Conclusion
Chapter-4---Working-with-any-n_2021_Introduction-to-Deep-Learning-and-Neural
	Working with any number of inputs
		ANN with 2 inputs and 1 output
		Math example
		Python™ implementation
			Code changes
			Training ANN
		ANN with 10 inputs and 1 output
			Training ANN
		ANN with any number of inputs
		Inputs assignment
		Weights initialization
		Calculating the SOP
		Calculating the SOP to weights derivatives
		Calculating the weights gradients
		Updating the weights
		Conclusion
Chapter-5---Working-with-hi_2021_Introduction-to-Deep-Learning-and-Neural-Ne
	Working with hidden layers
		ANN with 1 hidden layer with 2 neurons
		Forward pass
		Forward pass math calculations
		Backward pass
		Output layer weights
		Hidden layer weights
		Backward pass math calculations
		Output layer weights gradients
		Hidden layer weights gradients
		Updating weights
		Python™ implementation
		Forward pass
		Backward pass
		Complete code
		Conclusion
Chapter-6---Using-any-number-o_2021_Introduction-to-Deep-Learning-and-Neural
	Using any number of hidden neurons
		ANN with 1 hidden layer with 5 neurons
		Forward pass
		Backward pass
		Hidden layer gradients
		Python™ implementation
			Forward pass
			Backward pass
			More iterations
		Any number of hidden neurons in 1 layer
			Weights initialization
			Forward pass
			Backward pass
		ANN with 8 hidden neurons
		Conclusion
Chapter-7---Working-with-2-h_2021_Introduction-to-Deep-Learning-and-Neural-N
	Working with 2 hidden layers
		ANN with 2 hidden layers with 5 and 3 neurons
		Editing Chapter 6 implementation to work with an additional layer
		Preparing inputs, outputs, and weights
		Forward pass
		Backward pass
		First hidden layer gradients
		ANN with 2 hidden layers with 10 and 8 neurons
		Conclusion
Chapter-8---ANN-with-3-hid_2021_Introduction-to-Deep-Learning-and-Neural-Net
	ANN with 3 hidden layers
		ANN with 3 hidden layers with 5, 3, and 2 neurons
			Required changes in the forward pass
			Required changes in the backward pass
		Editing Chapter 7 implementation to work with 3 hidden layers
			Preparing inputs, outputs, and weights
			Forward pass
				Working with any number of layers
			Backward pass
		Python™ implementation
		ANN with 10 inputs and 3 hidden layers with 8, 5, and 3 neurons
		Conclusion
Chapter-9---Working-with-any-num_2021_Introduction-to-Deep-Learning-and-Neur
	Working with any number of hidden layers
		What to do for a generic gradient descent implementation?
		Generic approach for gradients calculation
			Output layer gradients
			Hidden layer gradients
				Calculations summary
		Python™ implementation
		backward_pass() method
			Output layer
			Hidden layers
		Example: Training the network
		Making predictions
		Conclusion
Chapter-10---Generic_2021_Introduction-to-Deep-Learning-and-Neural-Networks-
	Generic ANN
		Preparing initial weights for any number of outputs
		Calculating gradients for all output neurons
			Network with 2 outputs
			Network with 3 outputs
		Working with multiple training samples
			Calculating the size of the inputs and the outputs
			Iterating through the training samples
			Calculating the network error
		Implementing ReLU
		New implementation for MLP class
		Example for training network with multiple samples
		Using bias
			Initializing the network bias
			Using bias in the forward pass
			Updating bias using gradient descent
			Complete implementation with bias
		Stochastic and batch gradient descent
			Example
		Conclusion
Chapter-11---Running-neural-net_2021_Introduction-to-Deep-Learning-and-Neura
	Running neural networks in Android
		Building the first Kivy app
		Getting started with KivyMD
			MDTextField
			MDCheckbox
			MDDropdownMenu
			MDFileManager
			MDSpinner
		Training network in a thread
		Neural network KivyMD app
			neural.kv
			main.py
			Use the app
		Building the Android app
		Conclusion
Index_2021_Introduction-to-Deep-Learning-and-Neural-Networks-with-Python-
	Index
		A
		B
		C
		D
		F
		G
		H
		I
		J
		K
		L
		M
		N
		P
		R
		S
		T
		U
		V
		W