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Category: "Artificial Intelligence"

Artificial Intelligence

  1. Supervised Machine Learning - Linear Regression

    The book is intended to get you acquainted with the world of Supervised Machine Learning and does not assume previous knowledge of the field. The commonly leveraged Linear Regression technique used to provide predictions that are continuous in nature is detailed in the book. SAMPLE CODE INCLUDED!

  2. The Expert Curve
    The Expert Curve
    How Experts Are Made
    Michael Keyes

    The Expert Curve is about how expertise is achieved. With a comprehensive look into the science behind expertise, talent, prodigies, and training, The Expert Curve lays out the current understanding of how people learn to operate at expert levels in their fields and gives you the best tools to achieve expertise yourself.

  3. Learn NumPy Basics in Weekend
    Learn NumPy Basics in Weekend
    Learning NumPy Basics from Weekend Series
    Hisham El-Amir

    You will learn that NumPy has very efficient arrays that are easy to use due to the powerful indexing mechanism. This book describes some of the more advanced and tricky indexing techniques.Also we will try to make an attempt to document the most essential methods that every user should know. NumPy has many methods to even mention in this book!

  4. 解剖深度学习原理
    解剖深度学习原理
    从0实现深度学习库
    hwdong

    市面上的深度学习书要么晦涩难懂,要么是平台使用说明书,只讲理论缺少实现或者只有编程缺少原理讲解,都很难让人理解深度学习的基本原理,本书采用理论讲解与代码实现的方式深入浅出地剖析了深度学习的技术原理和实现细节,教会读者如何从零编写一个深度学习库。内容包含了:梯度下降法、回归学习、前馈神经网络、卷积神经网络、循环神经网络、生成网络。

  5. Darwin ≥ Marx - Eco/logical R/evolution
    Darwin ≥ Marx - Eco/logical R/evolution
    Theory of Evolution, is greater than or equal to Historical Materialism?
    Ciprian Pater

    The battle for our collective identity, is continually waged, as no human has as of yet, been crowned as a sovereign king of all creation.

  6. Computational Model of Sense Making
    Computational Model of Sense Making
    An Ontological Approach
    Gulam Husain and Zaber Al Hassan Ayon

    Computational Sense Making is new and exciting area of Applied AI with lot of research interest. In this book, we have attempted to give our readers an introduction of the topic with an implementation approach. This will help readers understand the topic in depth and apply computational sense making in different use cases.

  7. What Just Happened: Descriptive Statistics
    What Just Happened: Descriptive Statistics
    An Explorer’s Guide to Data
    Shefali Nayak

    The book explains the core concepts and terminologies of Descriptive Statistics using real-world examples. The book contains 17 practice problems, 2 quizzes, 36 graphical representations and numerous examples to enable effective learning and understanding of the concepts. Please check the bundle options with this book for better deals!

  8. Understanding Deep Learning
    Understanding Deep Learning
    Application in Rare Event Prediction
    Chitta Ranjan

    "It is like a voyage of discovery, seeking not for new territory but new knowledge. It should appeal to those with a good sense of adventure," Dr. Frederick Sanger. I hope every reader enjoys this voyage in deep learning and find their adventure.

  9. Introduction to Machine Learning with Python

    Machine Learning (ML) and Artificial Intelligence (AI) are shaping the future—and now is the time to get started. This book teaches you the fundamentals of Machine Learning with Python through practical, beginner-friendly examples. No prior ML experience required.

  10. Learn Pandas Basics in Weekend
    Learn Pandas Basics in Weekend
    Learn Pandas in Weekend Part I.
    Hisham El-Amir

    This weekend, we will cover many fundamental operations of the Pandas. Many of the sections will be similar to those in Data Science Essentials if read.

  11. Self aware Robot´s
    Self aware Robot´s
    Adrian Vasile

    I write this book for all the people who believe that link between technology and humans can create a better world. I believe that fast advancement of the technology will help our planet to solve many problems in a more fast smart way that what human brain is capable to do.All this because of fast development of microprocessor architecture.

  12. Feature Selection in Machine Learning
    Feature Selection in Machine Learning
    Over 20 methods to select the most predictive features and build simpler, faster, and more reliable machine learning models.
    Soledad Galli, PhD

    Learn how to implement various feature selection methods in a few lines of code and train faster, simpler, and more reliable machine learning models. Using Python open-source libraries, you will learn how to find the most predictive features from your data through filter, wrapper, embedded, and additional feature selection methods.

  13. 5G Basics & Architecture

    This is a short manual to understand the 4th Industrial Revolution Technologies & 5G in very easy & understandable language.

  14. Deep Learning Computer Vision Project
    Deep Learning Computer Vision Project
    Traffic Sign Detection & Recognition
    Hisham El-Amir

    Recently, convolutional neural networks have surpassed humans in different tasks such as classification of natural objects and classification of traffic signs. After their great success, convolutional neural networks have become the first choice for learning features from training data.

  15. 4th Industrial Revolution

    This is a short manual to understand the 4th Industrial Revolution Technologies in very easy & understandable language.