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Category: "Machine Learning"

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  1. Neural Networks Fundamentals with Python
    Rodrigo Girão Serrão @ Mathspp

    Ever wondered what a neural network really is, how it works, or how to implement one? Well, I did, and that is why I tried implementing one. And it was amazing! Now, I just want to help you do it yourself, so that you can take a peek behind the curtains of this world of machine learning, deep learning, and all those buzzwords.

  2. 15 Cheat Sheet Collection in Python + Git + NumPy + ML + Mindset
    Easy + Quick Learning with Finxter's Best Cheat Sheets
    Finxter

    This 15x PDF collection is a compilation of the best cheat sheets created for my free Finxter Email Academy that teaches Python in byte-sized video and cheat sheet lessons.

  3. 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。

  4. 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!

  5. 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.

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

  7. Behavior Analysis with Machine Learning and R
    A Sensors and Data Driven Approach
    Enrique Garcia Ceja

    Learn how to leverage the power of machine learning and deep learning to analyze behavioral patterns from sensors data and electronic records. This book shows you how to explore, preprocess, encode, and visualize your data. Learn introductory machine learning concepts and how to train supervised and unsupervised models using R.

  8. Hiring Data Scientists and Machine Learning Engineers is a concise, practical guide to help you hire the right people for your organization. The book will help you navigate the plethora of data science related roles and skills and help you create an effective hiring strategy to suit your organization's needs.

  9. Machine Learning Factory
    From the Idea to the Continuous Maintenance of any Machine Learning Application
    Larysa Visengeriyeva
    No Description Available
  10. Leaving the Rat Race with Python
    An Insider's Guide to Freelance Developing
    Finxter and Lukas Rieger

    Is There a Six-Figure Freelance Developer in You?Leaving the Rat Race with Python shows you how to nurture, grow, and harness your work-from-home coding business online --- and live the good life!It's an insider's guide to freelance developing. Start your new thriving coding business now!

  11. Data Science Workflow for Beginners
    Start your Data Science Journey into a Successful High Paying Career
    A.J. García

    This book takes you on an exclusive tour of Data Science, discover the workflow successful data scientists are following and download 26 datasets to start your journey right away after following the 3 data visuzalization projects on the book.

  12. No Description Available
  13. L’apprentissage automatique dans la vraie vie
    Petit guide pour la mise en production
    Nastasia Saby

    Un modèle de machine learning aussi puissant soit-il n’a aucune valeur s’il n’est pas en production. Le temps où l’apprentissage automatique était magique est dépassé. Ce qu’on veut aujourd’hui, ce sont des modèles en production qui apportent une plus-value. Ce livre est un guide pour mettre en production des modèles de machine learning.

  14. Isn't it weird that ML software is super important, yet crazy fragile? If ML software is valuable but so unstable, how come data scientists and ML engineers are rarely trained on the basics of building profitable software systems? What to do about it?

  15. Deep Learning with PyTorch Step-by-Step
    A Beginner's Guide
    Daniel Voigt Godoy

    Revised for PyTorch 2.x! In 2019, I published a PyTorch tutorial on Towards Data Science and I was amazed by the reaction from the readers! Their feedback motivated me to write this book to help beginners start their journey into Deep Learning and PyTorch. I hope you enjoy reading this book as much as I enjoy writing it.