Python Deep Learning

▶About this book
⦁ Explore and create intelligent systems using cutting-edge deep learning techniques
⦁ Implement deep learning algorithms and work with revolutionary libraries in Python
⦁ Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and more
▶Who This Book Is For
⦁ This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired.

▶What You Will Learn
⦁ Get a practical deep dive into deep learning algorithms
⦁ Explore deep learning further with Theano, Caffe, Keras, and TensorFlow
⦁ Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines
⦁ Dive into Deep Belief Nets and Deep Neural Networks
⦁ Discover more deep learning algorithms with Dropout and Convolutional Neural Networks
⦁ Get to know device strategies so you can use deep learning algorithms and libraries in the real world

▶Style and approach
Python Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world projects.

▶What this book covers
⦁ Chapter 1, Machine Learning –- An Introduction, presents different machine learning approaches and techniques and some of their applications to real-world problems. We will introduce one of the major open source packages available in Python for machine learning, scikit-learn.
⦁ Chapter 2, Neural Networks, formally introduces what neural networks are. We will thoroughly describe how a neuron works and will see how we can stack many layers to create and use deep feed-forward neural networks.
⦁ Chapter 3, Deep Learning Fundamentals, walks you toward an understanding of what deep learning is and how it is related to deep neural networks.
⦁ Chapter 4, Unsupervised Feature Learning, covers two of the most powerful and often-used architectures for unsupervised feature learning: auto-encoders and restricted Boltzmann machines.
⦁ Chapter 5, Image Recognition, starts from drawing an analogy with how our visual cortex works and introduces convolutional layers, followed up with a descriptive intuition of why they work.
⦁ Chapter 6, Recurrent Neural Networks and Language Models, discusses powerful methods that have been very promising in a lot of tasks, such as language modeling and speech recognition.
⦁ Chapter 7, Deep Learning for Board Games, covers the different tools used for solving board games such as checkers and chess.
⦁ Chapter 8, Deep Learning for Computer Games, looks at the more complex problem of training AI to play computer games.
⦁ Chapter 9, Anomaly Detection, starts by explaining the difference and similarities of concepts between outlier detection and anomaly detection. You will be guided through an imaginary fraud case study, followed by examples showing the danger of having anomalies in real-world applications and the importance of automated and fast detection systems.
⦁ Chapter 10, Building a Production-Ready Intrusion Detection System, leverages H2O and general common practices to build a scalable distributed system ready for deployment in production. You will learn how to train a deep learning network using Spark and MapReduce, how to use adaptive learning techniques for faster convergence and very important how to validate a model and evaluate the end to end pipeline.