學會使用機器學習Python健康管理的應用

Learn to use machine learning Python for health management applications

Deep Learning：用Python進行深度學習的基礎理論實作
作者： 斎藤康毅
譯者： 吳嘉芳
出版社：歐萊禮



利用最少的外部函式庫，使用Python，從零開始實際執行深度學習的程式。
　　．說明Python 的用法，讓Python 的初學者也能理解。
　　．實際執行Python 的原始碼，同時提供讀者手邊可以進行實驗的學習環境。
　　．從簡單的機器學習問題開始，到最後執行精密辨識影像的系統。
　　．以淺顯易懂的方式說明深度學習與神經網路理論。
　　．針對看似複雜的技術，如誤差反向傳播與卷積運算等，利用實際操作方式說明，幫助理解。
　　．介紹在執行深度學習時，有幫助且實用的技巧，包括決定學習率的方法、權重的預設值等。
　　．說明Batch Normalization、Dropout、Adam 等最近的趨勢與操作。
　　．為什麼深度學習很優秀，為什麼加深層數，就能提高辨識準確度，為什麼隱藏層很重要，仔細說明這些「為什麼」。
　　．介紹自動運作、產生影像、強化學習等深度學習的應用範例。

Deep Learning: Basic theoretical implementation of deep learning using Python
Author: Yasuki Saito
Translator: Wu Jiafang
Publisher: O'Reilly



Utilize minimal external libraries and use Python to actually execute deep learning programs from scratch.
． Explain the usage of Python so that even beginners of Python can understand it.
． It actually executes the Python source code and provides readers with a learning environment where they can conduct experiments.
． Starting from simple machine learning problems, and finally implementing systems for precise image recognition.
． Explain deep learning and neural network theory in an easy-to-understand way.
． For seemingly complex technologies, such as error backpropagation and convolution operations, practical operation methods are used to explain and help understand.
． Introduces helpful and practical techniques when performing deep learning, including methods for determining learning rates, preset values ​​for weights, etc.
． Explain the recent trends and operations of Batch Normalization, Dropout, Adam, etc.
． Why deep learning is excellent, why deeper layers can improve recognition accuracy, why hidden layers are important, explain these "whys" in detail.
． Introduces application examples of deep learning such as automatic operation, image generation, and reinforcement learning.