《面向金融的機器學習（影印版 英文版）》探索了機器學習的新進展，展示了如何將其應用于包括保險、交易和貸款在內(nèi)的整個金融領域。書中解釋了主要機器學習技術背后的概念和算法，并提供了用于自制模型的Python代碼示例。
《面向金融的機器學習(影印版 英文版）》基于Jannes Klaas為金融專業(yè)人士舉辦機器學習培訓課程的經(jīng)驗。書中并未提供現(xiàn)成的金融算法，而是著重介紹了能夠以多種方式應用的高級機器學習概念和思想。
書中展示了機器學習如何處理結(jié)構(gòu)化數(shù)據(jù)、文本、圖像和時間序，涵蓋了生成對抗性學習、強化學習、調(diào)試和發(fā)布機器學習產(chǎn)品等方面的內(nèi)容，討論了如何克服機器學習中的偏差，最后探究了貝葉斯推理和概率編程。
你將從《面向金融的機器學習(影印版 英文版）》中學到：
將機器學習應用于結(jié)構(gòu)化數(shù)據(jù)、自然語言、照片以及書面文本；
機器學習如何檢測詐騙、預測金融趨勢、分析客戶情緒等；
在Python、scikit-learn、Keras和TensorFlow中實現(xiàn)啟發(fā)式基線、時間序列、生成模型和增強學習；
深入挖掘神經(jīng)網(wǎng)絡，研究GAN和強化學習的應用；
調(diào)試機器學習應用并為上線做準備；
解決機器學習中的偏差和隱私問題。
Preface
Chapter 1：Neural Networks and Gradient．Based optimization
Our iourney in this book
What iS machine Iearning?
Supervised Iearning
Unsupervised learning
Reinforcement learning
The unreaS0nabIe effectiveness of data
AIl models are wrong
Setting up your workspace
Using Kaggle kernels
Running notebooks Iocally
Installing TensorFIow
Installing Keras
Using data locally
Using the AWS deep learning AMI
Approximating functions
A forward pass
A logistic regressor
Python version of our Iogistic regressor
optimizing model parameters
Measuring modelloSS
Gradient descent
Backpropaqation
Parameter updates
Putting it all together
A deeper network
A brief introduction to Keras
lmporting Keras
A two-layer modeIin Keras
Stacking layers
Compiling the model
Training the model
Keras and TensorFIow
Tensors and the computational graph
Exercises
Summary
Chapter 2：Applying Maching Learning to Structured Data
The data
Heuristic，feature．based。and E2E models
The machine Iearning software stack
The heuristic approach
Making predictions using the heuristic model
The F1 score
Evaluating with a confusion matrix
The feature engineering approach
A feature from intuition—fraudsters don’t sleep
Expeinsight—transfer．then cash out
StatisticaI quirks—errors in balances
Preparing the data for the Keras library
One-hot encoding
Entity embeddings
Tokenizing categories
Creating input models
Training the model
Creating predictive models with Keras
Extracting the target
Creating a test set
Creating a validation set
Oversampling the training data
Building the model
Creating a simple baseline
Building more complex models
A brief primer on tree-based methods
A simple decision tree
A random forest
XGBoost
E2E modeling
Exercises
Summary
Chapter 3：Utiliziting Computer Vision
……
Chapter 4：Understanding Time Series
Chapter 5：Parising Textual Data with Natural Language
Chapter 6：Using generative Models
Chapter 7：Reinforcement Learning for Financial Markets
Chapter 8：Privacy,Debugging,and Launching Your Products
Chapter 9：Fighting Bias
Chapter 10：Bayesian Infernence and Probabilisitic
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Index
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