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Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More
Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More

Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media—including who’s connecting with whom, what they’re...

Deep Learning with PyTorch: A practical approach to building neural network models using PyTorch
Deep Learning with PyTorch: A practical approach to building neural network models using PyTorch

Build neural network models in text, vision and advanced analytics using PyTorch

Key Features

  • Learn PyTorch for implementing cutting-edge deep learning algorithms.
  • Train your neural networks for higher speed and flexibility and learn how to implement them in various...
Deep Learning Cookbook: Practical Recipes to Get Started Quickly
Deep Learning Cookbook: Practical Recipes to Get Started Quickly

Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve...

Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python
Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. 

The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0.
...
Python Machine Learning Cookbook
Python Machine Learning Cookbook

100 recipes that teach you how to perform various machine learning tasks in the real world

About This Book

  • Understand which algorithms to use in a given context with the help of this exciting recipe-based guide
  • Learn about perceptrons and see how they are used to build neural...
R Deep Learning Essentials
R Deep Learning Essentials

Key Features

  • Harness the ability to build algorithms for unsupervised data using deep learning concepts with R
  • Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models
  • Build models relating to neural...
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you...

Docker Cookbook: Over 100 practical and insightful recipes to build distributed applications with Docker , 2nd Edition
Docker Cookbook: Over 100 practical and insightful recipes to build distributed applications with Docker , 2nd Edition

Leverage Docker to deploying software at scale

Key Features

  • Leverage practical examples to manage containers efficiently
  • Integrate with orchestration tools such as Kubernetes for controlled deployments
  • Learn to implement best practices on improving...
Introduction to Artificial Intelligence (Undergraduate Topics in Computer Science)
Introduction to Artificial Intelligence (Undergraduate Topics in Computer Science)

Artificial Intelligence (AI) has the definite goal of understanding intelligence and building intelligent systems. However, the methods and formalisms used on the way to this goal are not firmly set, which has resulted in AI consisting of a multitude of subdisciplines today. The difficulty in an introductory AI course lies in conveying...

OpenCV 3 Computer Vision with Python Cookbook: Leverage the power of OpenCV 3 and Python to build computer vision applications
OpenCV 3 Computer Vision with Python Cookbook: Leverage the power of OpenCV 3 and Python to build computer vision applications

Recipe-based approach to tackle the most common problems in Computer Vision by leveraging the functionality of OpenCV using Python APIs

Key Features

  • Build computer vision applications with OpenCV functionality via Python API
  • Get to grips with image processing, multiple...
Hands-On Deep Learning for Images with TensorFlow: Build intelligent computer vision applications using TensorFlow and Keras
Hands-On Deep Learning for Images with TensorFlow: Build intelligent computer vision applications using TensorFlow and Keras

Explore TensorFlow's capabilities to perform efficient deep learning on images

Key Features

  • Discover image processing for machine vision
  • Build an effective image classification system using the power of CNNs
  • Leverage TensorFlow's capabilities to...
Hands-On Neural Networks with Keras: Design and create neural networks using deep learning and artificial intelligence principles
Hands-On Neural Networks with Keras: Design and create neural networks using deep learning and artificial intelligence principles

Your one-stop guide to learning and implementing artificial neural networks with Keras effectively

Key Features

  • Design and create neural network architectures on different domains using Keras
  • Integrate neural network models in your applications using this highly practical...
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