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

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

abstractive-summarization

Implementation of abstractive summarization using LSTM with Residual Recurrent Attention in the encoder-decoder architecture with local attention.

abstractive-text-summarization

PyTorch implementation/experiments on Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond paper.

advanced-deep-learning-with-keras

Advanced Deep Learning with Keras, published by Packt

attention-is-all-you-need-keras

A Keras+TensorFlow Implementation of the Transformer: Attention Is All You Need

attention-is-all-you-need-pytorch

A PyTorch implementation of the Transformer model in "Attention is All You Need".

camera-calibration-python

camera_calibration

Camera calibration using OpenCV

chatbot

compilation_of_object_detection_models-master

conceptnet-numberbatch

crypto_currency_rate

cvat

Powerful and efficient Computer Vision Annotation Tool (CVAT)

datasciencepython

common data analysis and machine learning tasks using python

dc_tts

A TensorFlow Implementation of DC-TTS: yet another text-to-speech model

deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

deep-learning-with-keras

Code repository for Deep Learning with Keras published by Packt

devika

Devika is an Agentic AI Software Engineer that can understand high-level human instructions, break them down into steps, research relevant information, and write code to achieve the given objective. Devika aims to be a competitive open-source alternative to Devin by Cognition AI.

dron_tracking_and_distance_calculation

easy-tensorflow

Simple and comprehensive tutorials in TensorFlow

face-recognition

facerecog

Realtime Facial recognition system using Siamese neural network

fastapi_mongo_crud

handson-ml

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

image_captioning_keras

repository for image captioning with keras (deep learning)

image_segmentation

a jupyter notebook to understand image segmentation

inside_frame

learn-keras-for-deep-neural-networks

Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you’ll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.

learn_computer_vision

This is the curriculum for "Learn Computer Vision" by Siraj Raval on Youtube

learnopencv

Learn OpenCV : C++ and Python Examples