Python: Advanced Guide to Artificial Intelligence

Armando Fandango, Rajalingappaa Shanmugamani, Et al

December 2018

764 pages

22 hours 55 minutes

Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems

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Learn	Explore how an ML model can be trained, optimized, and evaluated Work with Autoencoders and Generative Adversarial Networks Explore the most important Reinforcement Learning techniques Build end-to-end deep learning (CNN, RNN, and Autoencoders) models Define and train a model for image and video classification Deploy your deep learning models and optimize them for high performance
About	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
Features	Master supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and more Build, deploy, and scale end-to-end deep neural network models in a production environment
Page Count	764
Course Length	22 hours 55 minutes
ISBN	9781789957211
Date Of Publication	21 Dec 2018

Table of contents

1 Machine Learning Model Fundamentals

Models and data
Features of a machine learning model
Loss and cost functions
Summary

2 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

Label propagation
Label spreading
Label propagation based on Markov random walks
Manifold learning
Summary

4 Bayesian Networks and Hidden Markov Models

Conditional probabilities and Bayes' theorem
Bayesian networks
Hidden Markov Models (HMMs)
Summary

5 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

Hebb's rule
Sanger's network
Rubner-Tavan's network
Self-organizing maps
Summary

7 Clustering Algorithms

k-Nearest Neighbors
K-means
Fuzzy C-means
Spectral clustering
Summary

8 Advanced Neural Models

Deep convolutional networks
Recurrent networks
Transfer learning
Summary

9 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

The perceptron
MultiLayer Perceptron
MLP for image classification
MLP for time series regression
Summary

11 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

Understanding convolution
Understanding pooling
CNN architecture pattern - LeNet
LeNet for MNIST data
LeNet for CIFAR10 Data
Summary

13 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

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

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 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

Strategies for distributed execution
TensorFlow clusters
Summary

18 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

20 Getting Started

Understanding deep learning
Deep learning for computer vision
Development environment setup
Summary

21 Image Classification

The bigger deep learning models
Training a model for cats versus dogs
Developing real-world applications
Summary

22 Image Retrieval

Understanding visual features
Model inference
Content-based image retrieval
Summary

23 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

Predicting pixels
Datasets
Algorithms for semantic segmentation
Ultra-nerve segmentation
Segmenting satellite images
Segmenting instances
Summary

25 Similarity Learning

Algorithms for similarity learning
Human face analysis
Summary

Authors

Armando Fandango

Armando Fandango creates AI empowered products by leveraging his expertise in deep learning, machine learning, distributed computing, and computational methods and has provided thought leadership roles as Chief Data Scientist and Director at startups and large enterprises. He has been advising high-tech AI-based startups. Armando has authored books titled Python Data Analysis - Second Edition and Mastering TensorFlow. He has also published research in international journals and conferences.

Rajalingappaa Shanmugamani

Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of Technology—Madras. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

Giuseppe Bonaccorso

Giuseppe Bonaccorso is an experienced manager in the fields of AI, data science, and machine learning. He has been involved in solution design, management, and delivery in different business contexts. He got his M.Sc.Eng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata, Italy, and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, neuroscience, and natural language processing.

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