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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...
Python Crash Course: A Hands-On, Project-Based Introduction to Programming
Python Crash Course: A Hands-On, Project-Based Introduction to Programming
Python Crash Course is a fast-paced, thorough introduction to Python that will have you writing programs, solving problems, and making things that work in no time.

In the first half of the book, you’ll learn about basic programming concepts, such as lists, dictionaries, classes, and loops, and practice writing
...
The Joys of Hashing: Hash Table Programming with C
The Joys of Hashing: Hash Table Programming with C
Build working implementations of hash tables, written in the C programming language. This book starts with simple first attempts devoid of collision resolution strategies, and moves through improvements and extensions illustrating different design ideas and approaches, followed by experiments to validate the choices. ...
Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python
Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python

Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.

The overall...

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...

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
  • ...
Natural Language Processing and Computational Linguistics: A practical guide to text analysis with Python, Gensim, spaCy, and Keras
Natural Language Processing and Computational Linguistics: A practical guide to text analysis with Python, Gensim, spaCy, and Keras

Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and computational linguistics algorithms.

Key Features

  • Discover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and...
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...
Test-Driven Java Development - Second Edition: Invoke TDD principles for end-to-end application development
Test-Driven Java Development - Second Edition: Invoke TDD principles for end-to-end application development

This book will teach the concepts of test driven development in Java so you can build clean, maintainable and robust code

Key Features

  • Explore the most popular TDD tools and frameworks and become more proficient in building applications
  • Create applications with better code...
Randomized Algorithms
Randomized Algorithms
For many applications, a randomized algorithm is either the simplest or the fastest algorithm available, and sometimes both. This book introduces the basic concepts in the design and analysis of randomized algorithms. The first part of the text presents basic tools such as probability theory and probabilistic analysis that are frequently used in...
Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition
Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition

The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and...

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