1. Deep Learning with Python (2017)
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn’t beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications.
About the Book
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You’ll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you’ll have the knowledge and hands-on skills to apply deep learning in your own projects.
- Deep learning from first principles
- Setting up your own deep-learning environment
- Image-classification models
- Deep learning for text and sequences
- Neural style transfer, text generation, and image generation
About the Reader
Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.
About the Author
François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.
Table of Contents
- What is deep learning?
- Before we begin: the mathematical building blocks of neural networks
- Getting started with neural networks
- Fundamentals of machine learning
- Deep learning for computer vision
- Deep learning for text and sequences
- Advanced deep-learning best practices
- Generative deep learning
- appendix A – Installing Keras and its dependencies on Ubuntu
- appendix B – Running Jupyter notebooks on an EC2 GPU instance
PART 1 – FUNDAMENTALS OF DEEP LEARNING
PART 2 – DEEP LEARNING IN PRACTICE
Author(s): Francois Chollet
2. Deep Learning with Keras: Implementing deep learning models and neural networks with the power of Python (2017)
- Implement various deep learning algorithms in Keras and see how deep learning can be used in games
- See how various deep learning models and practical use cases can be implemented using Keras
- A practical, hands-on guide with real-world examples to give you a strong foundation in Keras
This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer.
Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks.
What you will learn
- Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm
- Fine-tune a neural network to improve the quality of results
- Use deep learning for image and audio processing
- Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases
- Identify problems for which Recurrent Neural Network (RNN) solutions are suitable
- Explore the process required to implement Autoencoders
- Evolve a deep neural network using reinforcement learning
Who This Book Is For
If you’re a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deep learning with Keras. A knowledge of Python is required for this book.
Table of Contents
- Neural Networks Foundations
- Keras Installation and API
- Deep Learning with ConvNets
- Generative Adversarial Networks and WaveNet
- Word Embeddings
- Recurrent Neural Networks – RNNs
- Additional Deep Learning Models
- AI Game Playing
Author(s): Antonio Gulli, Sujit Pal
- Absorb the core concepts of the reinforcement learning process
- Use advanced topics of deep learning and AI
- Work with Open AI Gym, Open AI, and Python
- Harness reinforcement learning with TensorFlow and Keras using Python
Author(s): Abhishek Nandy, Manisha Biswas
4. Deep Learning with Applications Using Python: Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras (2018)
- Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn.
- Build face recognition and face detection capabilities
- Create speech-to-text and text-to-speech functionality
- Make chatbots using deep learning
Author(s): Navin Kumar Manaswi
5. Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning (2018)
The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit from almost 200 practical recipes for building a comprehensive machine-learning pipeline—everything from data preprocessing and feature engineering to model evaluation and deep learning.
Learn from author Chris Albon, a data scientist who has written more than 500 tutorials on Python, data science, and machine learning. Each recipe in this practical cookbook includes code solutions that you can put to work right away, along with a discussion of how and why they work—making it ideal as a learning tool and reference book.
Author(s): Chris Albon
Recent developments in deep learning have put the field center stage for innovation in software engineering. New algorithms and techniques in academia hold promise for many real world problems, and new machine learning platforms are powerful, but aren’t necessarily easy to get started with.
With this hands-on cookbook, you’ll discover that deep learning doesn’t need to be intimidating. Aimed at readers who are new to deep learning, this cookbook enables you to solve problems quickly, using the most appropriate platform for each application. You’ll learn how to leverage the work of Google by reusing pre-trained networks, use non-final layers to map data, and build recommender systems out of any correlation data.
- Work with step-by-step recipes that address familiar problems in areas such as text embeddings, text labeling and generation, and image classification and generation
- Walk through a practical solution for each recipe, using modern machine learning frameworks
- Learn how your newly-trained models can be easily ported for use in production settings
- Build applications that go from interesting results to serving real users
- Use deep learning in production, including how to query embeddings with the Postgres database, and how export and serve models using TensorFlow
- Set up a microservice using Python, and run models on mobile devices
Author(s): Douwe Osinga
- Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe
- Gain the fundamentals of deep learning with mathematical prerequisites
- Discover the practical considerations of large scale experiments
- Take deep learning models to production
Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.
Author(s): Nikhil Ketkar
8. Mastering TensorFlow 1.x: Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras (2018)
Build, scale, and deploy deep neural network models using the star libraries in Python
- Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras
- Build, deploy, and scale end-to-end deep neural network models in a production environment
- Learn to deploy TensorFlow on mobile, and distributed TensorFlow on GPU, Clusters, and Kubernetes
TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs.
This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images.
You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF Clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You will see how to call TensorFlow and Keras API within the R statistical software, and learn the required techniques for debugging when the TensorFlow API-based code does not work as expected.
The book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of TensorFlow and Keras, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems.
What you will learn
- Master advanced concepts of deep learning such as transfer learning, reinforcement learning, generative models and more, using TensorFlow and Keras
- Perform supervised (classification and regression) and unsupervised (clustering) learning to solve machine learning tasks
- Build end-to-end deep learning (CNN, RNN, and Autoencoders) models with TensorFlow
- Scale and deploy production models with distributed and high-performance computing on GPU and clusters
- Build TensorFlow models to work with multilayer perceptrons using Keras, TFLearn, and R
- Learn the functionalities of smart apps by building and deploying TensorFlow models on iOS and Android devices
- Supercharge TensorFlow with distributed training and deployment on Kubernetes and TensorFlow Clusters
Table of Contents
- Tensorflow 101
- High Level Libraries For TensorFlow
- Keras 101
- Classical Machine Learning with TensorFlow
- Neural Networks and MLP with TensorFlow and Keras
- RNN with TensorFlow and Keras
- RNN for Time Series Data with TensorFlow and Keras
- NLP for Text Data with TensorFlow and Keras
- CNN with TensorFlow and Keras
- Autoencoder with TensorFlow and Keras
- TensorFlow Models in Production with TF Serving
- Transfer Learning and Pre-Trained Models
- Deep Reinforcement Learning
- Generative Adversarial Networks
- Distributed Models with TensorFlow Clusters
- TensorFlow on Mobile and Embedded Platforms
- TensorFlow and Keras in R
- Debugging TensorFlow Models
- Appendix A: TPU
Author(s): Armando Fandango
9. Practical Convolutional Neural Network Models: Enhance deep learning skills by building intelligent ConvNet models using Keras (2018)
One stop guide to practice ConvNets models from most common to recent advances in artificial intelligence field
- Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques
- Learn best practices to get the most out of the book to implement CNN models on image recognition, object classification, transfer learning, GAN and more
- Develop your skills by in-depth understanding of advanced CNN architectures such as AlexNet, VGG, GoogLeNet and more and apply them to real-world research field
Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, speech recognition and more. These advances create unprecedented opportunities and challenges to build and deploy large-scale ConvNet applications. This book aims to take you through the building blocks of CNN’s, best practices for implementing CNN models and how they can be applied to solve complex machine learning problems.
This book starts with an overview of deep neural networks with the example of image classification and walks you through building your first CNN for human face detector. As you progress further you’ll come across practical illustrations of CNN internals with interesting examples to understand different optimization and visualization techniques to build a robust model. While explaining CNN architecture, this book covers case studies of most common and award-winning CNN architectures.
Furthermore, this book examines how knowledge transfer can be achieved to train a CNN model that does need a lot of data. You will be introduced to the concept of transfer learning that helps to improve the performance of a CNN model besides training data needs. Towards the end, this book also touches upon the subject on the attention-based CNN with an example of visual question answering application. Finally, this book covers details of generative models and a novel application – getting started with generating your own hand-written MNIST digits.
By the end of this book, you will be all ready to implement CNN models in your work or projects by working with extreme datasets.
What you will learn
- From CNN basics building blocks to advanced concepts understand the practical areas they can be applied to.
- Build a simple image classifier CNN model to understand how different components interact with each other.
- Learn CNN Model Optimization and Visualization techniques
- Implement award-winning CNN Architectures like AlexNet, VGG, GoogLeNet, ResNet etc
- Practical approach to use pre-trained models and examples to understand transfer learning methodology.
- Understand the difference between GAN generators and discriminators.
- Step into the world of Artificial intelligence with adversarial training and applications of GANs
Who This Book Is For
This book is for data scientists, machine learning practitioners, deep learning and AI enthusiasts who want to move one step further in building convolutional neural network models. Get your hands on extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of machine learning and CNN is expected.
Author(s): Pradeep Pujari