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30-days-of-javascript

30 days of JavaScript programming challenge is a step-by-step guide to learn JavaScript programming language in 30 days. This challenge may take more than 100 days, please just follow your own pace.

500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code

500 AI Machine learning Deep learning Computer vision NLP Projects with code

amazon-food-review

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

chatmhm

chhabii

code

Compilation of R and Python programming codes on the Data Professor YouTube channel.

codestackr

codingwithmitch-chat

Real-time chat server with Django

data_ai_trends_predictions-2024

deep_learning_for_biologists_with_keras

tutorials made for biologists to learn deep learning

deeplearning

#100 days of Deep learning Challenge.

deploydjangoonaws

django

The Web framework for perfectionists with deadlines.

django-channels-chat

A simple Django-Channels web chat!

document_conversion_chatbot

ecoinsights

enhancing-nepalihealth-qa-systems-with-rag

farmmarket

fine-tune-your-own-llama-2-model-in-a-colab-notebook-with-custom-nepali-datasets

finetunemt5-nepali-health-chat

first-contributions

🚀✨ Help beginners to contribute to open source projects

free-programming-books

:books: Freely available programming books

getting-things-done-with-pytorch

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.

homemade-machine-learning

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

inspiringdocs

inspiringpdfs

insuranceproject

machine_learning_examples

A collection of machine learning examples and tutorials.

made-with-ml

Learn how to responsibly develop, deploy and maintain production machine learning applications.