Deep Learning By Example: A hands-on guide to implementing advanced machine learning algorithms and neural networks

Cover
Copyright and Credits
Packt Upsell
Contributors
Table of Contents
Preface
Chapter 1: Data Science - A Birds\' Eye View
	Understanding data science by an example
	Design procedure of data science algorithms
		Data pre-processing
			Data cleaning
			Data pre-processing
		Feature selection
		Model selection
		Learning process
		Evaluating your model
	Getting to learn
		Challenges of learning
			Feature extraction – feature engineering
			Noise
			Overfitting
			Selection of a machine learning algorithm
			Prior knowledge
			Missing values
	Implementing the fish recognition/detection model
		Knowledge base/dataset
		Data analysis pre-processing
		Model building
			Model training and testing
			Fish recognition – all together
	Different learning types
		Supervised learning
		Unsupervised learning
		Semi-supervised learning
		Reinforcement learning
	Data size and industry needs
	Summary
Chapter 2: Data Modeling in Action - The Titanic Example
	Linear models for regression
		Motivation
		Advertising – a financial example
			Dependencies
			Importing data with pandas
			Understanding the advertising data
			Data analysis and visualization
			Simple regression model
				Learning model coefficients
				Interpreting model coefficients
				Using the model for prediction
	Linear models for classification
		Classification and logistic regression
	Titanic example – model building and training
		Data handling and visualization
		Data analysis – supervised machine learning
	Different types of errors
	Apparent (training set) error
	Generalization/true error
	Summary
Chapter 3: Feature Engineering and Model Complexity – The Titanic Example Revisited
	Feature engineering
		Types of feature engineering
			Feature selection
			Dimensionality reduction
			Feature construction
		Titanic example revisited
			Missing values
				Removing any sample with missing values in it
				Missing value inputting
				Assigning an average value
				Using a regression or another simple model to predict the values of missing variables
			Feature transformations
				Dummy features
				Factorizing
				Scaling
				Binning
			Derived features
				Name
				Cabin
				Ticket
			Interaction features
	The curse of dimensionality
		Avoiding the curse of dimensionality
	Titanic example revisited – all together
	Bias-variance decomposition
	Learning visibility
		Breaking the rule of thumb
	Summary
Chapter 4: Get Up and Running with TensorFlow
	TensorFlow installation
		TensorFlow GPU installation for Ubuntu 16.04
			Installing NVIDIA drivers and CUDA 8
			Installing TensorFlow
		TensorFlow CPU installation for Ubuntu 16.04
		TensorFlow CPU installation for macOS X
		TensorFlow GPU/CPU installation for Windows
	The TensorFlow environment
	Computational graphs
	TensorFlow data types, variables, and placeholders
		Variables
		Placeholders
		Mathematical operations
	Getting output from TensorFlow
	TensorBoard – visualizing learning
	Summary
Chapter 5: TensorFlow in Action - Some Basic Examples
	Capacity of a single neuron
		Biological motivation and connections
	Activation functions
		Sigmoid
		Tanh
		ReLU
	Feed-forward neural network
	The need for multilayer networks
		Training our MLP – the backpropagation algorithm
		Step 1 – forward propagation
		Step 2 – backpropagation and weight updation
	TensorFlow terminologies – recap
		Defining multidimensional arrays using TensorFlow
		Why tensors?
		Variables
		Placeholders
		Operations
	Linear regression model – building and training
		Linear regression with TensorFlow
	Logistic regression model – building and training
		Utilizing logistic regression in TensorFlow
			Why use placeholders?
			Set model weights and bias
			Logistic regression model
			Training
			Cost function
	Summary
Chapter 6: Deep Feed-forward Neural Networks - Implementing Digit Classification
	Hidden units and architecture design
	MNIST dataset analysis
		The MNIST data
	Digit classification – model building and training
		Data analysis
		Building the model
		Model training
	Summary
Chapter 7: Introduction to Convolutional Neural Networks
	The convolution operation
	Motivation
		Applications of CNNs
	Different layers of CNNs
		Input layer
		Convolution step
		Introducing non-linearity
		The pooling step
		Fully connected layer
			Logits layer
	CNN basic example – MNIST digit classification
		Building the model
			Cost function
			Performance measures
		Model training
	Summary
Chapter 8: Object Detection – CIFAR-10 Example
	Object detection
	CIFAR-10 – modeling, building, and training
		Used packages
		Loading the CIFAR-10 dataset
		Data analysis and preprocessing
		Building the network
		Model training
		Testing the model
	Summary
Chapter 9: Object Detection – Transfer Learning with CNNs
	Transfer learning
		The intuition behind TL
		Differences between traditional machine learning and TL
	CIFAR-10 object detection – revisited
		Solution outline
		Loading and exploring CIFAR-10
		Inception model transfer values
		Analysis of transfer values
		Model building and training
	Summary
Chapter 10: Recurrent-Type Neural Networks - Language Modeling
	The intuition behind RNNs
		Recurrent neural networks architectures
		Examples of RNNs
			Character-level language models
				Language model using Shakespeare data
		The vanishing gradient problem
		The problem of long-term dependencies
	LSTM networks
		Why does LSTM work?
	Implementation of the language model
		Mini-batch generation for training
		Building the model
			Stacked LSTMs
			Model architecture
			Inputs
			Building an LSTM cell
			RNN output
			Training loss
			Optimizer
			Building the network
			Model hyperparameters
		Training the model
			Saving checkpoints
			Generating text
	Summary
Chapter 11: Representation Learning - Implementing Word Embeddings
	Introduction to representation learning
	Word2Vec
		Building Word2Vec model
	A practical example of the skip-gram architecture
	Skip-gram Word2Vec implementation
		Data analysis and pre-processing
		Building the model
		Training
	Summary
Chapter 12: Neural Sentiment Analysis
	General sentiment analysis architecture
		RNNs – sentiment analysis context
		Exploding and vanishing gradients - recap
	Sentiment analysis – model implementation
		Keras
		Data analysis and preprocessing
		Building the model
		Model training and results analysis
	Summary
Chapter 13: Autoencoders – Feature Extraction and Denoising
	Introduction to autoencoders
	Examples of autoencoders
	Autoencoder architectures
	Compressing the MNIST dataset
		The MNIST dataset
		Building the model
		Model training
	Convolutional autoencoder
		Dataset
		Building the model
		Model training
	Denoising autoencoders
		Building the model
		Model training
	Applications of autoencoders
		Image colorization
		More applications
	Summary
Chapter 14: Generative Adversarial Networks
	An intuitive introduction
	Simple implementation of GANs
		Model inputs
		Variable scope
		Leaky ReLU
		Generator
		Discriminator
		Building the GAN network
			Model hyperparameters
			Defining the generator and discriminator
			Discriminator and generator losses
			Optimizers
		Model training
			Generator samples from training
		Sampling from the generator
	Summary
Chapter 15: Face Generation and Handling Missing Labels
	Face generation
		Getting the data
		Exploring the Data
		Building the model
			Model inputs
			Discriminator
			Generator
			Model losses
			Model optimizer
			Training the model
	Semi-supervised learning with Generative Adversarial Networks (GANs)
		Intuition
		Data analysis and preprocessing
		Building the model
			Model inputs
			Generator
			Discriminator
			Model losses
			Model optimizer
		Model training
	Summary
Appendix: Implementing Fish Recognition
	Code for fish recognition
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