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Python Deep Dive Course - Accompanying Materials

Interact privately with your documents using the power of GPT, 100% privately, no data leaks

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge.

Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks

Code Repository for Machine Learning with PyTorch and Scikit-Learn

Example code for Fluent Python, 2nd edition (O'Reilly 2022)

The simplest, fastest repository for training/finetuning medium-sized GPTs.

Code repository for O'Reilly book

Jupyter notebooks for the Natural Language Processing with Transformers book

Practical assignments for the network science course

Lab Materials for MIT 6.S191: Introduction to Deep Learning

Robust Speech Recognition via Large-Scale Weak Supervision

An experimental open-source attempt to make GPT-4 fully autonomous.

Repository for the Packt Publishing book titled "C# 10 and .NET 6 - Modern Cross-Platform Development" by Mark J. Price

Provides tools to analyze hashtags within posts scraped from TikTok.

A standard API for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

Companion webpage to the book "Mathematics For Machine Learning"

Code and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://amzn.to/3JUgR2L

Tutorials, datasets, and other material associated with textbook "A First Course in Network Science" by Menczer, Fortunato & Davis

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

Source Code for 'Pro C# 9 with .NET 5' by Andrew Troelsen and Philip Japikse

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

A Case Study Approach to Successful Data Science Projects Using Python, Pandas, and Scikit-Learn