by Gavin Hackeling
Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model.
This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use Scikit-learn's API to extract features from categorical variables, text, and images; evaluate model performance, and develop an intuition for how to improve your model's performance.
By the end of this book, you will master all required concepts of Scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.
What you will learn
- Review fundamental concepts such as bias and variance
- Extract features from categorical variables, text, and images
- Predict the values of continuous variables using linear regression and K Nearest Neighbors
- Classify documents and images using logistic regression and support vector machines
- Create ensembles of estimators using bagging and boosting techniques
- Discover hidden structures in data using K-Means clustering
- Evaluate the performance of machine learning systems in common tasks
by Sherin Thomas
PyTorch Deep Learning Hands-On is a book for engineers who want a fast-paced guide to doing deep learning work with Pytorch. It is not an academic textbook and does not try to teach deep learning principles. The book will help you most if you want to get your hands dirty and put PyTorch to work quickly.
PyTorch Deep Learning Hands-On shows how to implement the major deep learning architectures in PyTorch. It covers neural networks, computer vision, CNNs, natural language processing (RNN), GANs, and reinforcement learning. You will also build deep learning workflows with the PyTorch framework, migrate models built-in Python to highly efficient TorchScript, and deploy to production using the most sophisticated available tools.
Each chapter focuses on a different area of deep learning. Chapters start with a refresher on how the model works, before sharing the code you need to implement them in PyTorch.
What you will learn
Use PyTorch to build:
Simple Neural Networks - build neural networks the PyTorch way, with high-level functions, optimizers, and more
Convolutional Neural Networks - create advanced computer vision systems
Recurrent Neural Networks - work with sequential data such as natural language and audio
Generative Adversarial Networks - create new content with models including SimpleGAN and CycleGAN
Reinforcement Learning - develop systems that can solve complex problems such as driving or game playing
Deep Learning workflows - move effectively from ideation to production with proper deep learning workflow using PyTorch and its utility packages
Production-ready models - package your models for high-performance production environments
by Tom Hope
Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open-source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics.
Authors Tom Hope, Yehezkel Reshef, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience-from data scientists and engineers to students and researchers. You'll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems in TensorFlow.
- Get up and running with TensorFlow, rapidly and painlessly
- Learn how to use TensorFlow to build deep learning models from the ground up
- Train popular deep learning models for computer vision and NLP
- Use extensive abstraction libraries to make development easier and faster
- Learn how to scale TensorFlow, and use clusters to distribute model training
- Deploy TensorFlow in a production setting
by Chandramani Tiwary
If you are a Java developer and want to use Mahout and machine learning to solve Big Data Analytics use cases then this book is for you. Familiarity with shell scripts is assumed but no prior experience is required.
by Antonio Gulli
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 handwritten digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of the 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
- Rapid Miner