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Machine Learning for Finance
最新章節：
Index
MachineLearningforFinanceexploresnewadvancesinmachinelearningandshowshowtheycanbeappliedacrossthefinancialsector,includingininsurance,transactions,andlending.ItexplainstheconceptsandalgorithmsbehindthemainmachinelearningtechniquesandprovidesexamplePythoncodeforimplementingthemodelsyourself.ThebookisbasedonJannesKlaas’experienceofrunningmachinelearningtrainingcoursesforfinancialprofessionals.Ratherthanprovidingready-madefinancialalgorithms,thebookfocusesontheadvancedMLconceptsandideasthatcanbeappliedinawidevarietyofways.Thebookshowshowmachinelearningworksonstructureddata,text,images,andtimeseries.Itincludescoverageofgenerativeadversariallearning,reinforcementlearning,debugging,andlaunchingmachinelearningproducts.ItdiscusseshowtofightbiasinmachinelearningandendswithanexplorationofBayesianinferenceandprobabilisticprogramming.
目錄(132章)
倒序
- 封面
- 版權頁
- Why subscribe?
- Packt.com
- Contributors
- About the author
- About the reviewer
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Get in touch
- Chapter 1. Neural Networks and Gradient-Based Optimization
- Our journey in this book
- What is machine learning?
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Setting up your workspace
- Using Kaggle kernels
- Using the AWS deep learning AMI
- Approximating functions
- A forward pass
- A logistic regressor
- Optimizing model parameters
- Measuring model loss
- A deeper network
- A brief introduction to Keras
- Tensors and the computational graph
- Exercises
- Summary
- Chapter 2. Applying Machine Learning to Structured Data
- The data
- Heuristic feature-based and E2E models
- The machine learning software stack
- The heuristic approach
- The feature engineering approach
- Preparing the data for the Keras library
- Creating predictive models with Keras
- A brief primer on tree-based methods
- E2E modeling
- Exercises
- Summary
- Chapter 3. Utilizing Computer Vision
- Convolutional Neural Networks
- Filters on color images
- The building blocks of ConvNets in Keras
- More bells and whistles for our neural network
- Working with big image datasets
- Working with pretrained models
- The modularity tradeoff
- Computer vision beyond classification
- Exercises
- Summary
- Chapter 4. Understanding Time Series
- Visualization and preparation in pandas
- Fast Fourier transformations
- Autocorrelation
- Establishing a training and testing regime
- A note on backtesting
- Median forecasting
- ARIMA
- Kalman filters
- Forecasting with neural networks
- Conv1D
- Dilated and causal convolution
- Simple RNN
- LSTM
- Recurrent dropout
- Bayesian deep learning
- Exercises
- Summary
- Chapter 5. Parsing Textual Data with Natural Language Processing
- An introductory guide to spaCy
- Named entity recognition
- Part-of-speech (POS) tagging
- Rule-based matching
- Regular expressions
- A text classification task
- Preparing the data
- Bag-of-words
- Topic modeling
- Word embeddings
- Document similarity with word embeddings
- A quick tour of the Keras functional API
- Attention
- Seq2seq models
- Exercises
- Summary
- Chapter 6. Using Generative Models
- Understanding autoencoders
- Visualizing latent spaces with t-SNE
- Variational autoencoders
- VAEs for time series
- GANs
- Using less data – active learning
- SGANs for fraud detection
- Exercises
- Summary
- Chapter 7. Reinforcement Learning for Financial Markets
- Catch – a quick guide to reinforcement learning
- Markov processes and the bellman equation – A more formal introduction to RL
- Advantage actor-critic models
- Evolutionary strategies and genetic algorithms
- Practical tips for RL engineering
- Frontiers of RL
- Exercises
- Summary
- Chapter 8. Privacy Debugging and Launching Your Products
- Debugging data
- Debugging your model
- Deployment
- Performance tips
- Exercises
- Summary
- Chapter 9. Fighting Bias
- Sources of unfairness in machine learning
- Legal perspectives
- Observational fairness
- Training to be fair
- Causal learning
- Interpreting models to ensure fairness
- Unfairness as complex system failure
- A checklist for developing fair models
- Exercises
- Summary
- Chapter 10. Bayesian Inference and Probabilistic Programming
- An intuitive guide to Bayesian inference
- Summary
- Farewell
- Further reading
- Index 更新時間：2021-06-11 13:26:50
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