Sources codes for: Mastering Python for Finance, Second Edition
- machine-learning
- python
- ai
Repository Index
47,133 repositories indexed
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Full tutorial of computer vision and machine learning basics with OpenCV and Keras in Python.
A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.
- feature-selection
- data-science
- python
An OCaml kernel for Jupyter (IPython) notebook
- ocaml
- functional-programming
- jupyter-kernels
- machine-learning
Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.
Materials for GWU DNSC 6279 and DNSC 6290.
Usiigaci: stain-free cell tracking in phase contrast microscopy enabled by supervised machine learning
- tensorflow
- python3
- keras
- resnet-101
ChatGPT Python Applications integrated with third party libraries and modules
⬛ Python Individual Conditional Expectation Plot Toolbox
- machine-learning
- interpretability
Optimising chemical reactions using machine learning
- chemistry
- machine-learning
- optimization
- bayesian-optimization
Big Data essentials: Hadoop, MapReduce, Spark. Explore tutorials and demos in Jupyter notebooks—most are self-contained and live, ready to run with a click.
Code repository for The Automation Ahead series, showcasing practical examples for GenAI-driven automation in investments. Each installment dives into specific tools, techniques, and use cases to help professionals assess and apply GenAI effectively.
- data-science
- deep-learning
- finance
- financial-data
Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
- epigenomics
- pattern-search
- data-visualization
- interactive-machine-learning
Machine learning for Analysis of Proteomics in Spatial biology - Nature Communications
- cell-annotation
- multiplexed-imaging
- mpif
A coding exercise: let's convert dirty machine learning code into clean code using a Pipeline - which is the Pipe and Filter Design Pattern applied to Machine Learning.
Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.