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开源软件名称(OpenSource Name):PacktPublishing/Machine-Learning-for-Finance开源软件地址(OpenSource Url):https://github.com/PacktPublishing/Machine-Learning-for-Finance开源编程语言(OpenSource Language):Jupyter Notebook 100.0%开源软件介绍(OpenSource Introduction):Machine Learning for FinanceThis is the code repository for Machine Learning for Finance, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish. About the bookMachine Learning for Finance explores new advances in machine learning and shows how they can be applied in the financial sector. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. How to run this codeThe code in this repository is quite compute heavy and best run on a GPU enabled machine. The datascience platform Kaggle offers free GPU recourses together with free online Jupyter notebooks. To make edits on the Kaggle notebooks, click 'Fork' to create a new copy of the notebook. You will need a Kaggle account for this. Alternatively you can just view the notebooks on NB Viewer or download the code and run it locally. Chapter 1 - A neural Network from ScratchA neural network from Scratch & Intro to Keras: Run on Kaggle, View Online Excercise excel sheet: Download Chapter 2 - Structured DataCredit card fraud detection: Run On Kaggle, View Online Chapter 3 - Computer Vision Building BlocksClassifying MNIST digits: Run On Kaggle, View Online Chapter 4 - Practical Computer VisionClassifying Plants: View Online, Run On Colab Intro to Python Generators: Run On Kaggle Keras Generator with Logistic Regression: Run On Kaggle Stacking VGG: Run On Kaggle Preprocessing and Saving VGG Outputs: Run On Kaggle Rule Based Preprocessing and Augmentation: Run On Kaggle Visualizing ConvNets: Run On Kaggle Chapter 5 - Time SeriesForecasting Web Traffic: Classic Methods: Run On Kaggle, View Online Forecasting Web Traffic: Time Series Neural Nets: Run On Kaggle, View Online Expressing Uncertainty with Bayesian Deep Learning: Run On Kaggle, View Online Chapter 6 - Natural Language processingAnalyzing the News: Run On Kaggle, View Online Classifying Tweets: Run On Kaggle, View Online Topic modeling with LDA: Run On Kaggle, View Online Sequence to Sequence models: Run On Kaggle, View Online Chapter 7 - Generative Models(Variational) Autoencoder for MNIST: Run On Kaggle, View Online (Variational) Autoencoder for Fraud Detection: Run On Kaggle, View Online MNIST DCGAN: Run On Kaggle, View Online Semi Supervised Generative Adversarial Network for Fraud Detection: Run On Kaggle, View Online Chapter 8 - Reinforcement LearningQ-Learning: View Online A2C Pole Balancing: View Online A2C for Trading: Run On Kaggle View Online Chapter 9 - Debugging ML SystemsUnit Testing Data: Run On Kaggle, View Online Hyperparameter Optimization: View Online Learning Rate Search: View Online Using Tensorboard: View Online Converting Keras Models to TF Estimators: View Online Faster Python with Cython: Download Part 1, Download Part 2 Chapter 10 - Fighting Bias in Machine LearningUnderstanding Parity in Excel: Download Learning How to Pivot: View Online Interpretability with SHAP: View Online |
2023-10-27
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