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DevOps with Kubernetes: Accelerating software delivery with container orchestrators
DevOps with Kubernetes: Accelerating software delivery with container orchestrators

Learn to implement DevOps using Docker & Kubernetes.

About This Book

  • Learning DevOps, container, and Kubernetes within one book.
  • Leverage Kubernetes as a platform to deploy, scale, and run containers efficiently.
  • A practical guide towards container management and...
Microservices Deployment Cookbook
Microservices Deployment Cookbook

Key Features

  • Adopt microservices-based architecture and deploy it at scale
  • Build your complete microservice architecture using different recipes for different solutions
  • Identify specific tools for specific scenarios and deliver immediate business results, correlate use cases, and adopt them...
Predictive Analytics For Dummies
Predictive Analytics For Dummies

Combine business sense, statistics, and computers in a new and intuitive way, thanks to Big Data Predictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in...

Mastering TensorFlow 1.x: Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras
Mastering TensorFlow 1.x: Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras

Build, scale, and deploy deep neural network models using the star libraries in Python

Key Features

  • 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...
Machine Learning, Neural and Statistical Classification (Ellis Horwood Series in Artificial Intelligence)
Machine Learning, Neural and Statistical Classification (Ellis Horwood Series in Artificial Intelligence)
The aim of this book is to provide an up-to-date review of different approaches to classification,
compare their performance on a wide range of challenging data-sets, and draw
conclusions on their applicability to realistic industrial problems.

Before describing the contents, we first need to define what we mean by
...
Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition
Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms

Key Features

  • Explore statistics and complex mathematics for data-intensive applications
  • Discover new developments in EM algorithm, PCA, and bayesian...
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or...

Advances in Artificial Intelligence for Privacy Protection and Security
Advances in Artificial Intelligence for Privacy Protection and Security

In this book, we aim to collect the most recent advances in artificial intelligence techniques (i.e. neural networks, fuzzy systems, multi-agent systems, genetic algorithms, image analysis, clustering, etc), which are applied to the protection of privacy and security. The symbiosis between these fields leads to a pool of invigorating ideas,...

Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
A classic -- offering comprehensive and unified coverage with a balance between theory and practice!

Pattern recognition is integral to a wide spectrum of scientific disciplines and technologies including image analysis, speech recognition and audio classification, communications, computer-aided diagnosis, data mining. The authors,
...
TensorFlow 2.0 Quick Start Guide: Get up to speed with the newly introduced features of TensorFlow 2.0
TensorFlow 2.0 Quick Start Guide: Get up to speed with the newly introduced features of TensorFlow 2.0

Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks.

Key Features

  • Train your own models for effective prediction, using high-level Keras API
  • Perform supervised and unsupervised machine learning and learn...
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)

Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis

 

Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely...

Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed...
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