Code for Tensorflow Machine Learning Cookbook
- tensorflow
- tensorflow-cookbook
- linear-regression
- neural-network
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Code for Tensorflow Machine Learning Cookbook
Financial portfolio optimization in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
This is the official code for MobileSAM project that makes SAM lightweight for mobile applications and beyond!
YOLOv6: a single-stage object detection framework dedicated to industrial applications.
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
From the basics to slightly more interesting applications of Tensorflow
Technical Analysis Library using Pandas and Numpy
CoTracker is a model for tracking any point (pixel) on a video.
A computer science textbook
links to conference publications in graph-based deep learning
Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
Python code for common Machine Learning Algorithms
A 15TB Collection of Physics Simulation Datasets
Technical resources for AI developers to build applications, agents, and systems using Oracle AI Database and OCI services
Segment Anything in Medical Images
Probabilistic reasoning and statistical analysis in TensorFlow
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
MongoDB's Generative AI Showcase: an exhaustive collection of examples and sample applications covering Retrieval-Augmented Generation (RAG), AI agents, and industry-specific use cases.
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
Everything you need to know to build your own RAG application
A simplified implemention of Faster R-CNN that replicate performance from origin paper