A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

Use unsupervised and supervised learning to predict stocks

Top 200 deep learning Github repositories sorted by the number of stars.

NeuPy is a Tensorflow based python library for prototyping and building neural networks

Machine learning Guide. Learn all about Machine Learning Tools, Libraries, Frameworks, Large Language Models (LLMs), and Training Models.

Top 100 trending deep learning repositories sorted by the number of stars gained on a specific day.

Code for training and test machine learning classifiers on MIT-BIH Arrhyhtmia database

Evaluate three types of task shifting with popular continual learning algorithms.

Learn about the Neumorphic engineering process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures.

A Python toolkit for Reservoir Computing and Echo State Network experimentation based on pyTorch. EchoTorch is the only Python module available to easily create Deep Reservoir Computing models.

Applying Machine Learning and AI Algorithms applied to Trading for better performance and low Std.

An Architecture Combining Convolutional Neural Network (CNN) and Linear Support Vector Machine (SVM) for Image Classification

Source code and pretrained model for running pix2pix in realtime on a webcam feed.

Curated Tensorflow code resources to help you get started with Deep Learning.

Implementation of "PaLM-E: An Embodied Multimodal Language Model"

Learning to Cluster. A deep clustering strategy.

AI Talks - ChatGPT Assistant via Streamlit

AutoNode: A Neuro-Graphic Self-Learnable Engine for Cognitive GUI Automation

Always sparse. Never dense. But never say never. A Sparse Training repository for the Adaptive Sparse Connectivity concept and its algorithmic instantiation, i.e. Sparse Evolutionary Training, to boost Deep Learning scalability on various aspects (e.g. memory and computational time efficiency, representation and generalization power).

A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).