Book Image

Python: Advanced Guide to Artificial Intelligence

By : Giuseppe Bonaccorso, Rajalingappaa Shanmugamani
Book Image

Python: Advanced Guide to Artificial Intelligence

By: Giuseppe Bonaccorso, Rajalingappaa Shanmugamani

Overview of this book

This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: • Mastering Machine Learning Algorithms by Giuseppe Bonaccorso • Mastering TensorFlow 1.x by Armando Fandango • Deep Learning for Computer Vision by Rajalingappaa Shanmugamani

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Python: Advanced Guide to Artificial Intelligence
Giuseppe Bonaccorso | Rajalingappaa Shanmugamani
Dec, 2018 | 12 hours 44 minutes
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Table of Contents (31 chapters)
Title Page
Title Page
About Packt
About Packt
Contributors
Contributors
Preface
Preface
Free Chapter
1
Machine Learning Model Fundamentals
Machine Learning Model Fundamentals
Models and data
Features of a machine learning model
Loss and cost functions
Summary
2
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Semi-supervised scenario
Generative Gaussian mixtures
Contrastive pessimistic likelihood estimation
Semi-supervised Support Vector Machines (S3VM)
Transductive Support Vector Machines (TSVM)
Summary
3
Graph-Based Semi-Supervised Learning
Graph-Based Semi-Supervised Learning
Label propagation
Label spreading
Label propagation based on Markov random walks
Manifold learning
Summary
4
Bayesian Networks and Hidden Markov Models
Bayesian Networks and Hidden Markov Models
Conditional probabilities and Bayes' theorem
Bayesian networks
Hidden Markov Models (HMMs)
Summary
5
EM Algorithm and Applications
EM Algorithm and Applications
MLE and MAP learning
EM algorithm
Gaussian mixture
Factor analysis
Principal Component Analysis
Independent component analysis
Addendum to HMMs
Summary
6
Hebbian Learning and Self-Organizing Maps
Hebbian Learning and Self-Organizing Maps
Hebb's rule
Sanger's network
Rubner-Tavan's network
Self-organizing maps
Summary
7
Clustering Algorithms
Clustering Algorithms
k-Nearest Neighbors
K-means
Fuzzy C-means
Spectral clustering
Summary
8
Advanced Neural Models
Advanced Neural Models
Deep convolutional networks
Recurrent networks
Transfer learning
Summary
9
Classical Machine Learning with TensorFlow
Classical Machine Learning with TensorFlow
Simple linear regression
Multi-regression
Regularized regression
Classification using logistic regression
Binary classification
Multiclass classification
Summary
10
Neural Networks and MLP with TensorFlow and Keras
Neural Networks and MLP with TensorFlow and Keras
The perceptron
MultiLayer Perceptron
MLP for image classification
MLP for time series regression
Summary
11
RNN with TensorFlow and Keras
RNN with TensorFlow and Keras
Simple Recurrent Neural Network
RNN variants
LSTM network
GRU network
TensorFlow for RNN
Keras for RNN
Application areas of RNNs
RNN in Keras for MNIST data
Summary
12
CNN with TensorFlow and Keras
CNN with TensorFlow and Keras
Understanding convolution
Understanding pooling
CNN architecture pattern - LeNet
LeNet for MNIST data
LeNet for CIFAR10 Data
Summary
13
Autoencoder with TensorFlow and Keras
Autoencoder with TensorFlow and Keras
Autoencoder types
Stacked autoencoder in TensorFlow
Stacked autoencoder in Keras
Denoising autoencoder in TensorFlow
Denoising autoencoder in Keras
Variational autoencoder in TensorFlow
Variational autoencoder in Keras
Summary
14
TensorFlow Models in Production with TF Serving
TensorFlow Models in Production with TF Serving
Saving and Restoring models in TensorFlow
Saving and restoring Keras models
TensorFlow Serving
TF Serving in the Docker containers
TensorFlow Serving on Kubernetes
Summary
15
Deep Reinforcement Learning
Deep Reinforcement Learning
OpenAI Gym 101
Applying simple policies to a cartpole game
Reinforcement learning 101
Naive Neural Network policy for Reinforcement Learning
Implementing Q-Learning
Summary
16
Generative Adversarial Networks
Generative Adversarial Networks
Generative Adversarial Networks 101
Best practices for building and training GANs
Simple GAN with TensorFlow
Simple GAN with Keras
Deep Convolutional GAN with TensorFlow and Keras
Summary
17
Distributed Models with TensorFlow Clusters
Distributed Models with TensorFlow Clusters
Strategies for distributed execution
TensorFlow clusters
Summary
18
Debugging TensorFlow Models
Debugging TensorFlow Models
Fetching tensor values with tf.Session.run()
Printing tensor values with tf.Print()
Asserting on conditions with tf.Assert()
Debugging with the TensorFlow debugger (tfdbg)
Summary
19
Tensor Processing Units
Tensor Processing Units
20
Getting Started
Getting Started
Understanding deep learning
Deep learning for computer vision
Development environment setup
Summary
21
Image Classification
Image Classification
The bigger deep learning models
Training a model for cats versus dogs
Developing real-world applications
Summary
22
Image Retrieval
Image Retrieval
Understanding visual features
Model inference
Content-based image retrieval
Summary
23
Object Detection
Object Detection
Detecting objects in an image
Exploring the datasets
Localizing algorithms 
Detecting objects
Object detection API
The YOLO object detection algorithm 
Summary
24
Semantic Segmentation
Semantic Segmentation
Predicting pixels
Datasets
Algorithms for semantic segmentation
Ultra-nerve segmentation
Segmenting satellite images
Segmenting instances
Summary
25
Similarity Learning
Similarity Learning
Algorithms for similarity learning
Human face analysis
Summary
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Index
Index

Bayesian networks

A Bayesian network is a probabilistic model represented by a direct acyclic graph G = {V, E}, where the vertices are random variables Xi, and the edges determine a conditional dependence among them. In the following diagram, there's an example of simple Bayesian networks with four variables:

Example of Bayesian network

The variable x4 is dependent on x3, which is dependent on x1 and x2. To describe the network, we need the marginal probabilities P(x1) and P(x2) and the conditional probabilities P(x3|x1,x2) and P(x4|x3). In fact, using the chain rule, we can derive the full joint probability as:

The previous expression shows an important concept: as the graph is direct and acyclic, each variable is conditionally independent of all other variables that are not successors given its predecessors. To formalize this concept, we can define the function Predecessors(xi), which returns the set of nodes that influence xi directly, for example, Predecessors(x3) = {x1,x2} (we are using...

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