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The Hacker Playbook 3: Practical Guide to Penetration Testing by Peter Kim is an advanced guide for ethical hackers, cybersecurity professionals, and IT security enthusiasts. This book provides step-by-step penetration testing methodologies, practical tools, and real-world scenarios to help readers identify and exploit vulnerabilities ethically.

The book covers topics such as:

• Red team operations and attack simulation

• Network penetration testing

• Exploit development and post-exploitation techniques

• Security assessments and countermeasures

Ideal for students, IT professionals, and security enthusiasts who want hands-on experience in ethical hacking and penetration testing.

Book Details:

• Title: The Hacker Playbook 3: Practical Guide to Penetration Testing

• Author: Peter Kim

• Binding: Hardcover / Paperback

• Language: English

• Condition: Brand New

• Genre: Cybersecurity, IT, Technology, Ethical Hacking

• Edition: 3rd Edition

• Publisher: Original Publisher

Key Features:

• Step-by-step practical penetration testing guide

• Real-world hacking scenarios and exercises

• Suitable for ethical hackers, cybersecurity students, and IT professionals

• Updated strategies, tools, and techniques for modern security threats

• Widely used in cybersecurity training and professional certification

Title: An Introduction to Statistical Learning: With Applications in R
Authors: Gareth James, Daniela Witten, Trevor Hastie & Robert Tibshirani
Publisher: Springer (Springer Texts in Statistics)
Edition: 2nd Edition (2021 / 2022 print)
Language: English
Format: Paperback / Hardcover
Description:
An Introduction to Statistical Learning is an accessible and practical textbook on statistical learning and data analysis. It explains key modern modeling and prediction techniques used to analyze complex datasets. The book blends clear theoretical foundations with real-world applications and code examples in R programming language — making it useful for students, researchers, and professionals in statistics, data science, machine learning, and related fields. Chapters cover methods such as linear regression, classification, resampling techniques, shrinkage approaches, tree-based models, support vector machines, clustering, and more. Each chapter contains R code and tutorials to help readers implement these methods on real data using R. This book is suitable for beginners as well as intermediate learners who want a strong practical grasp of statistical learning.

Typical Uses (as described by suppliers):
✔ University and college curricula in statistics and data science
✔ Self-study for statisticians, analysts, and programmers
✔ Reference for applied machine learning practitioners

Category: Statistics / Data Science / Machine Learning / R Programming Textbook

Learn how to uncover the hints and hidden details in a question, discover how to break down a problem into manageable chunks, develop techniques to unstick yourself when stuck, learn (or re-learn) core computer science concepts, and practice on 189 interview questions and solutions.

The second edition of DAMA International's Guide to the Data Management Body of Knowledge (DAMA-DMBOK2) updates and augments the highly successful DMBOK1. An accessible, authoritative reference book written by leading thinkers in the field and extensively reviewed by DAMA members, DMBOK2 brings together materials that comprehensively describe the challenges of data management and how to meet them by:

Capturing a wealth of experience about the design of object-oriented software, four top-notch designers present a catalog of simple and succinct solutions to commonly occurring design problems. Previously undocumented, these 23 patterns allow designers to create more flexible, elegant, and ultimately reusable designs without having to rediscover the design solutions themselves.

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.

Kevin Huo is currently a Data Scientist at a Hedge Fund, and previously was a Data Scientist at Facebook working on Facebook Groups. He holds a degree in Computer Science from the University of Pennsylvania and a degree in Business from Wharton. In college he interned at Facebook, Bloomberg, and on Wall Street.

Deep Learning is a comprehensive textbook that introduces the concepts, theories, and practical applications of modern deep learning techniques. Written by leading researchers Ian Goodfellow, Yoshua Bengio, and Aaron Courville, the book provides a detailed overview of neural networks, machine learning algorithms, and artificial intelligence systems.

The book explains the mathematical and conceptual foundations of deep learning and how these technologies are used in areas such as computer vision, speech recognition, natural language processing, and robotics. It is widely used by university students, researchers, and professionals working in the fields of artificial intelligence and data science.

This book is an essential resource for anyone interested in understanding the technology behind modern AI systems and advanced machine learning models.

Product Details

 

• Book Name: Deep Learning

• Series: Adaptive Computation and Machine Learning

• Authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville

• Language: English

• Genre: Artificial Intelligence / Machine Learning / Computer Science

• Publisher: MIT Press

• Publication Year: 2016

• Pages: Approx. 775

• Format: Paperback / Hardcover

• Condition: New

• Key Features

   

  • Comprehensive guide to deep learning and neural networks

  • Written by leading AI researchers

  • Covers mathematics, algorithms, and practical applications

  • Ideal for students, engineers, and data scientists

  • Widely used as a university-level AI textbook

  • • Deep Learning Book

    • Ian Goodfellow Book

    • Machine Learning Book

    • Artificial Intelligence Book

    • Computer Science Educational Books

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 for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.