Machine Learning 機器學習
C++ Primer, 4/e (中文版)
Introduction to Algorithms, 3/e (IE-Paperback)
C++ 教學手冊, 3/e
這樣思考，才會有創意─ 10 個認知 X 5項要訣 X 10 條法則，早知道早成功的創意思考術
電腦網際網路, 6/e (國際版)(Computer Networking: A Top-Down Approach, 6/e)(附部分內容光碟)
透視 C 語言指標－深度探索記憶體管理核心技術 (Understanding and Using C Pointers)
深入淺出 PMP, 3/e (Head First PMP, 3/e)
SCRUM : 用一半的時間 做兩倍的事 (SCRUM: The Art of Doing Twice the Work in Half the Time)
精通 Python｜運用簡單的套件進行現代運算 (Introducing Python: Modern Computing in Simple Packages)
深入淺出 Android 開發 (Head First Android Development)
CISSP All-in-One Exam Guide, 7/e (Hardcover)
Deep Learning (Hardcover)
Python 自動化的樂趣｜搞定重複瑣碎 & 單調無聊的工作 (中文版) (Automate the Boring Stuff with Python: Practical Programming for Total Beginners)
TensorFlow + Keras 深度學習人工智慧實務應用
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寫程式前就該懂的演算法 ─ 資料分析與程式設計人員必學的邏輯思考術 (Grokking Algorithms: An illustrated guide for programmers and other curious people)
Deep Learning｜用 Python 進行深度學習的基礎理論實作
Visual C# 2017 程式設計經典 (附光碟)
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2/e (Paperback)
Ready to crank up a virtual server to smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?
Well, hold on there...
Before you embark on your epic journey into the world of machine learning, there is a lot of basic theory to march through first. But rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first. As a clear and concise alternative to a textbook, this short book has become a Best Seller on Amazon (in its category) with a practical and high-level introduction to machine learning. Machine Learning for Absolute Beginners has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home. This title opens with a general introduction to machine learning from a macro level. The second half of the book is more practical and dives into introducing specific algorithms applied in machine learning, including their pros and cons. At the end of the book, I share insights and advice on further learning and careers in this space. Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle deep learning and Scikit-learn, you would be well served with a long-format textbook, such as the O'Reilly Media series. I don't wish to disappoint readers with content that is too easy. If, however, you are yet to reach that Lion King moment - as a fully grown Simba looking over the Pride Lands of Africa - then this is the book to gently hoist you up and offer you a clear lay of the land.
In this step-by-step guide you will learn: - The very basics of Machine Learning that all beginners need to master - Association Analysis used in the retail and E-commerce space - Recommender Systems as you've seen online, including Amazon - Decision Trees for visually mapping and classifying decision processes - Regression Analysis to create trend lines and predict trends - Data Reduction and Principle Component Analysis to cut through the noise - k-means and k-nearest Neighbor (k-nn) Clustering to discover new data groupings - A very basic introduction to Deep Learning/Neural Networks - Bias/Variance to optimize your machine learning model - Careers in the field
If you want to learn more, please go ahead and send a free sample to your device or check out Amazon's handy 'Look Inside' feature.