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100-days-of-code

Fork this template for the 100 days journal - to keep yourself accountable (multiple languages available)

30-days-of-python

30 days of Python programming challenge is a step by step guide to learn Python programming language in 30 days.

auto-gpt

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

autologinbot

Auto-Login Bot with Python and Selenium

awesome-ai-data-github-repos

A collection of the most important Github repos for ML, AI & Data science practitioners

awesome-tensorflow

TensorFlow - A curated list of dedicated resources http://tensorflow.org

chatdev

Create Customized Software using Natural Language Idea (through Multi-Agent Collaboration)

clustering-with-llm

A customer segmentation project can be approached in multiple ways. In this repository, we will explore advanced techniques for defining clusters and analyzing the results.

cnn-nir-spectra

Advances in Near-infrared (NIR) spectroscopy technology led to an increase of interest in its applications in various industries due to its powerful non-destructive quantization tool. In this work, we used a one-dimensional CNN to determine simultaneously quantities of organic materials in a mixture using their NIR infrared spectra. The coefficient of determination (R2) and the root mean square error (RMSE) is used to test the performance of the model. We used six materials to make pairwise combinations with distinct quantities of each pair. We obtained 13 different pairwise mixtures, afterward, their near-infrared spectrum profiles is extracted. The model predicted for each mixture their percentage of composition with a result of 0.9955 R2 and RMSE 0.0199. Furthermore, we examined the performance of our model when predicting unseen composition percentages with unseen mixtures. To do so, two scenarios are carried out by filtering the training and testing set: the first one where we test on unseen composition percentage (UP) of mixtures, and the second one where we test on unseen composition percentage of unseen mixtures (UPM). The model achieved anR2of 0.947 and0.627 scores respectively for UP and UPM.

colab

Continual Learning tutorials and demo running on Google Colaboratory.

corey

Personal fitness app. Workout. Schedule. Your body. Your goals.

coursesera-dl

CourseseraDeeplearning

deepdream

fastbook

The fastai book, published as Jupyter Notebooks

gpt-engineer

Specify what you want it to build, the AI asks for clarification, and then builds it.

gpt_examples

healthgpt

Query your Apple Health data with natural language 💬 🩺

julius

Fast PyTorch based DSP for audio and 1D signals

learn-python

📚 Playground and cheatsheet for learning Python. Collection of Python scripts that are split by topics and contain code examples with explanations.

llama2-accessory

An Open-source Toolkit for LLM Development

metagpt

🌟 The Multi-Agent Framework: Given one line Requirement, return PRD, Design, Tasks, Repo

music-generator

Machine Learning group project

nippy

Semi-automated preprocessing of NIRS data

nirpyresearch

Snippets and data from the blog of Nirpy Research

pml2-book

Probabilistic Machine Learning: Advanced Topics

privategpt

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

python

All Algorithms implemented in Python

realtimehand

Realtime Hand Tracking Unity Package

satellite-image-deep-learning

Resources for deep learning with satellite & aerial imagery

straen

A workout tracker for iOS that includes cycling, running, as well as strength exercises. Supports Bluetooth sensors.