Machine Learning for Finance: Principles and practice for financial insiders

Machine Learning for Finance: Principles and practice for financial insiders Packt 第1张Machine Learning for Finance: Principles and practice for financial insiders
By 作者: Jannes Klaas
ISBN-10 书号: 1789136369
ISBN-13 书号: 9781789136364
Release Finelybook 出版日期: 2019-05-30
pages 页数: (456 )
Publisher Finelybook 出版社: Packt

The Book Description robot was collected from Amazon and arranged by Finelybook

A guide to advances in machine learning for financial professionals, with working Python code
Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself.
The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on the advanced ML concepts and ideas that can be applied in a wide variety of ways.
The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming.
What you will learn

Apply machine learning to structured data, natural language, photographs, and written text
How machine learning can detect fraud, forecast financial trends, analyze customer sentiments, and more
Implement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlow
Dig deep into neural networks, examine uses of GANs and reinforcement learning
Debug machine learning applications and prepare them for launch
Address bias and privacy concerns in machine learning
contents
1 Neural Networks and Gradient-Based Optimization
2 Applying Machine Learning to Structured Data
3 Utilizing Computer Vision
4 Understanding Time Series
5 Parsing Textual Data with Natural Language Processing
6 Using Generative Models
7 Reinforcement Learning for Financial Markets
8 Privacy, Debugging, and Launching Your Products
9 Fighting Bias
10 Bayesian Inference and Probabilistic Programming

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