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Blockchain Basics: A Non-Technical Introduction in 25 Steps
Blockchain Basics: A Non-Technical Introduction in 25 Steps

In 25 concise steps, you will learn the basics of blockchain technology. No mathematical formulas, program code, or computer science jargon are used. No previous knowledge in computer science, mathematics, programming, or cryptography is required. Terminology is explained through pictures, analogies, and metaphors.

This book...

Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition
Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow.

Key Features

  • Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow
  • Explore deep neural...
Hands-On Automated Machine Learning: A beginner's guide to building automated machine learning systems using AutoML and Python
Hands-On Automated Machine Learning: A beginner's guide to building automated machine learning systems using AutoML and Python

Automate data and model pipelines for faster machine learning applications

Key Features

  • Build automated modules for different machine learning components
  • Understand each component of a machine learning pipeline in depth
  • Learn to use different open source...
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource...

Linear Algebra and Probability for Computer Science Applications
Linear Algebra and Probability for Computer Science Applications

Based on the author’s course at NYU, Linear Algebra and Probability for Computer Science Applications gives an introduction to two mathematical fields that are fundamental in many areas of computer science. The course and the text are addressed to students with a very weak mathematical background. Most of the...

Machine Learning Using R: With Time Series and Industry-Based Use Cases in R
Machine Learning Using R: With Time Series and Industry-Based Use Cases in R

Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python...

Beginning Data Science with Python and Jupyter: Use powerful tools to unlock actionable insights from data
Beginning Data Science with Python and Jupyter: Use powerful tools to unlock actionable insights from data

Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction.

Key Features

  • Get up and running with the Jupyter ecosystem and some example datasets
  • ...
Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras
Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras

Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks

Key Features

  • Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision
  • Combine the power of Python, Keras, and TensorFlow to...
Mathematics and Computer Science: Algorithms, Trees, Combinatorics and Probabilities (Trends in Mathematics)
Mathematics and Computer Science: Algorithms, Trees, Combinatorics and Probabilities (Trends in Mathematics)
This is the first book where mathematics and computer science are directly confronted and joined to tackle intricate problems in computer science with deep mathematical approaches. It contains a collection of refereed papers presented at the Colloquium on Mathematics and Computer Science held at the University of Versailles-St-Quentin on...
Service-Oriented Crowdsourcing: Architecture, Protocols and Algorithms (SpringerBriefs in Computer Science)
Service-Oriented Crowdsourcing: Architecture, Protocols and Algorithms (SpringerBriefs in Computer Science)
At a fundamental level, service-oriented crowdsourcing applies the principles of service-oriented architecture (SOA) to the discovery, composition and selection of a scalable human workforce. Service-Oriented Crowdsourcing: Architecture, Protocols and Algorithms provides both an analysis of contemporary crowdsourcing systems, such as Amazon...
Architects of Intelligence: The truth about AI from the people building it
Architects of Intelligence: The truth about AI from the people building it

Financial Times Best Books of the Year 2018

TechRepublic Top Books Every Techie Should Read

Book Description

How will AI evolve and what major innovations are on the horizon? What will its impact be on the job market, economy, and society? What is the path toward human-level...

Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute...

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