Research & Education

MLAlgorithms provides a comprehensive collection of machine learning algorithm implementations, including deep learning models like MLP, CNN, RNN, and LSTM, as well as classical algorithms such as linear regression, logistic regression, Random Forests, SVM, K-Means, and Naive Bayes. All algorithms are implemented in Python, leveraging libraries like NumPy, SciPy, and Autograd, making the code easy to follow and experiment with. This project is ideal for those who wish to understand the core mechanics of these algorithms without the complexity of highly optimized libraries, offering a simplified approach to learning and building ML models from the ground up.

TXYZ is an AI-powered platform designed to integrate all paths to knowledge, assisting users with research, learning, and problem-solving. It offers distinct products for searching, reading, and writing, each leveraging AI to redefine knowledge discovery, revolutionize knowledge work, and streamline workflows. The platform provides AI assistance for reading, problem-solving, and writing, with features like file upload and analysis, access to a comprehensive library, and agentic workflows. TXYZ is trusted by researchers and offers an API for businesses and institutions to empower their operations with AI. It aims to enhance accuracy and reduce 'hallucination' by refining its retrieval algorithm and providing document location references.

DeepSeek-V3 is a powerful Mixture-of-Experts (MoE) language model featuring 671B total parameters, with 37B activated for each token, ensuring efficient inference and cost-effective training. Building on the DeepSeek-V2 architecture, it introduces an innovative auxiliary-loss-free strategy for load balancing and a multi-token prediction training objective for enhanced performance. The model was pre-trained on 14.8 trillion diverse tokens and further refined through Supervised Fine-Tuning and Reinforcement Learning. DeepSeek-V3 demonstrates superior performance against other open-source models and rivals top closed-source alternatives, particularly excelling in math and code tasks. It supports local deployment on various hardware and open-source community software, including SGLang, LMDeploy, and TensorRT-LLM, with options for FP8 and BF16 inference.

deeplearning-mindmap is an open-source project hosted on GitHub that offers a detailed mindmap summarizing key concepts in Deep Learning. This resource covers various architectures, the underlying mathematics, and an overview of the TensorFlow library. It serves as a valuable cheatsheet for understanding Deep Learning, which is a subset of Machine Learning focused on learning data representations, applicable in supervised, partially supervised, or unsupervised contexts. The mindmap is available as a PDF download and was built using MindNode. It also references a related mindmap on Machine Learning basics and Data Science, making it a comprehensive learning aid for students and professionals alike.

ELSA | Speech Analyzer is an AI-powered application designed to help users improve their English pronunciation and fluency. The tool offers instant and personalized feedback on spoken English, utilizing advanced speech recognition technology to evaluate and guide users. It aims to enable users to speak English more naturally. The platform focuses on accent reduction and overall speech improvement, making it a valuable resource for individuals looking to refine their spoken English skills.

micro_diffusion is an open-source repository from Sony Research that provides a minimalistic implementation for training large-scale diffusion models from scratch with an extremely low budget. Utilizing only 37 million publicly available real and synthetic images, it can train a 1.16 billion parameter sparse transformer for approximately $1,890, achieving a strong FID score on the COCO dataset. The repository includes training code, dataset code, and pre-trained model checkpoints for off-the-shelf generation. It supports progressive training from low to high resolution and incorporates patch masking for performance optimization and reduced training time.

ML2021-Spring is an official GitHub repository for the Machine Learning 2021 Spring course taught by Hung-Yi Lee at National Taiwan University. This resource offers comprehensive materials for students and self-learners, including code and slides for 15 distinct homework assignments. The assignments cover a wide range of machine learning topics, from fundamental concepts like Regression and Classification to advanced areas such as CNNs, Transformers, GANs, BERT, Autoencoders, Reinforcement Learning, and Meta Learning. The repository also provides links to the course website and lecture videos available on Hung-Yi Lee's YouTube channel, making it a valuable, self-contained learning package.

Deepdrive is an open-source simulator designed to facilitate experimentation and advancement in self-driving AI. It enables anyone with a PC to develop and test state-of-the-art autonomous driving systems within a realistic simulated environment. The simulator supports various AI agent types, including forward-agents, remote agents, and baseline agents like Mnet2 and C++ FSM/PID. Users can record training data for imitation learning, convert data to TFRecords, and train models using provided datasets or their own. Deepdrive offers detailed observation data, including vehicle dynamics, camera feeds (image, depth), and environmental information, all adhering to Unreal Engine conventions for units and rotations. It requires Linux, Python 3.6+, 10GB disk space, and 8GB RAM, with optional GPU requirements for baseline agents.

DeepExplain offers a comprehensive framework for understanding the behavior of deep neural networks through various attribution methods. It enables researchers and practitioners to interpret existing models and benchmark new attribution techniques. The tool supports both gradient-based methods like Saliency maps, Gradient * Input, Integrated Gradients, DeepLIFT, and epsilon-LRP, as well as perturbation-based methods such as Occlusion and Shapley Value sampling. DeepExplain is compatible with TensorFlow (V1) and Keras with a TensorFlow backend, providing flexibility for different development environments. Its capabilities help in identifying which input features contribute most to a network's output, aiding in debugging and model transparency.

Meshed-Memory Transformer (M²) is an open-source project that provides the reference code for the paper "Meshed-Memory Transformer for Image Captioning" presented at CVPR 2020. This tool is designed for researchers and developers working in computer vision and natural language processing. It allows users to set up a conda environment, download necessary data like COCO annotations and detection features, and then evaluate or train their own image captioning models. The repository includes scripts for both testing and training, with configurable arguments for batch size, number of memory vectors, and learning rate scheduling. It requires Python 3.6 and specific data preparation steps to function correctly.

DeepResearcher is an open-source framework designed to scale deep research by training LLM-based agents using reinforcement learning in real-world web environments. This comprehensive tool facilitates end-to-end training, allowing agents to engage in authentic web search interactions. Qualitative analysis of the framework reveals emergent cognitive behaviors, including the ability to formulate plans, cross-validate information from multiple sources, self-reflect to redirect research, and maintain honesty when definitive answers are unavailable. DeepResearcher demonstrates significant performance improvements over prompt engineering and RAG-based baselines, emphasizing the critical role of end-to-end training in real-world settings for developing robust research capabilities.

Tech Screen is an AI-powered tool designed to help job seekers excel in technical interviews by providing real-time, undetectable assistance. It operates invisibly during screen sharing on major platforms such as Zoom, Google Meet, and Microsoft Teams, ensuring interviewers see no trace of the application. Key features include lightning-fast responses, precise answers, and a conversation mode that listens to system audio to provide instant solutions. The tool is highly customizable, allowing users to tailor prompts, programming languages, and interview types. Tech Screen boasts a 100% undetectable track record and offers a clean, intuitive interface with keyboard shortcuts for seamless operation, making it an invaluable asset for anyone looking to boost their interview success.

mlbookcamp-code is a GitHub repository offering comprehensive code examples and supplementary materials directly from the Machine Learning Bookcamp book. It covers a wide range of machine learning topics, from regression and classification to neural networks, deployment, and serverless deep learning. The repository also provides code for setting up environments, an introduction to Python, NumPy, and Pandas. It serves as a practical companion to the book, allowing users to explore and implement machine learning concepts. Additionally, it links to the Machine Learning Zoomcamp, a free online course based on the book, providing further learning opportunities and community support.

DeepLOB-Deep-Convolutional-Neural-Networks-for-Limit-Order-Books is a Jupyter notebook project showcasing the application of deep convolutional neural networks to analyze limit order books. This tool is based on research published in IEEE Transactions on Signal Processing, providing a practical demonstration of the methodologies presented in the paper. It utilizes the publicly available FI-2010 dataset to illustrate how the model architecture is constructed and implemented. The project offers implementations in both TensorFlow (versions 1 and 2) and PyTorch, making it accessible to researchers and developers familiar with either framework. It serves as a valuable resource for understanding and replicating advanced deep learning techniques in financial market analysis.

obsidian-local-gpt is an Obsidian plugin designed to bring local AI assistance directly into your notes, ensuring maximum privacy and offline access. It integrates with Ollama and OpenAI-like GPT models, enabling users to perform various AI actions on selected text and even images. The plugin offers a context menu for quick actions and an Action Palette for one-time tasks. Key features include the ability to use context from links, backlinks, and PDF files (RAG), and support for community actions that can be browsed and installed directly from the plugin settings. It supports multiple languages and is available through the Obsidian plugin store or BRAT, requiring the AI Providers plugin for configuration.

DeepLearningProject offers an extensive machine learning tutorial designed to guide users through an entire machine learning pipeline from the ground up. Unlike typical short tutorials, this project focuses on a full pipeline, covering all implementation decisions and details required for real-world machine learning applications. It moves beyond standard datasets like MNIST or CIFAR, encouraging users to create their own datasets. The tutorial progresses from conventional machine learning algorithms to deep learning, providing a holistic learning experience. Originally developed as a class project for Harvard University, it has been updated to include a PyTorch version. The project emphasizes practical setup with conda environments and Docker containers, addressing common installation issues and bugs.

DLTK (Deep Learning Toolkit) is an open-source Python library designed for medical image analysis, leveraging the TensorFlow framework. It aims to facilitate rapid prototyping of deep learning models and ensure reproducibility in research applications within the medical imaging field. The toolkit provides state-of-the-art methods and models, accelerating research and development. It includes example applications and tutorial notebooks to help users understand its interface with TensorFlow, write custom read functions, and develop their own model functions. DLTK also features a Model Zoo with implementations of current research methodologies.

DriveDreamer is a pioneering world model entirely derived from real-world driving scenarios, specifically designed for autonomous driving research. Unlike other models that focus on gaming or simulated environments, DriveDreamer addresses the critical limitation of lacking real-world representation. It leverages powerful diffusion models to construct comprehensive representations of complex driving environments and employs a two-stage training pipeline. This allows DriveDreamer to first acquire an understanding of structured traffic constraints and then anticipate future states. The tool empowers precise, controllable video generation that faithfully captures real-world traffic scenarios and enables the generation of realistic and reasonable driving policies, opening avenues for interaction and practical applications in autonomous driving.

SwissCognitive is an AI advisory and research firm dedicated to advancing the application of artificial intelligence in business. They offer comprehensive expertise across AI research, advisory services, and ventures, aiming to provide clients with research-based methodologies and industry-driven insights. The firm connects key players within the AI ecosystem, catering to a diverse clientele including organizations, startups, and VC Funds. Their services are designed to help these entities navigate the complexities of AI adoption and strategic implementation, fostering innovation and growth.

DiffBIR is an open-source project providing code and pretrained models for blind image restoration, as presented in the ECCV 2024 paper. It leverages generative diffusion prior to handle various restoration tasks, including blind image super-resolution, blind face restoration (aligned and unaligned), and blind image denoising. The tool offers different model versions, including one trained on the Unsplash dataset with LLaVA-generated captions, and supports features like tiled sampling for large images on low-VRAM GPUs. Users can interact with DiffBIR via a Gradio web interface or through command-line inference scripts, making it accessible for both research and practical applications in image enhancement.

DiffEqFlux.jl is a Julia library designed for scientific machine learning (SciML), specifically focusing on neural differential equations. It integrates differential equation solvers into neural networks, enabling the addition of physical information into traditional machine learning models. The library offers pre-built implicit layer architectures with efficient O(1) backpropagation and GPU acceleration. It supports various types of neural differential equations, including Neural ODEs, Neural SDEs, Neural DAEs, and Neural DDEs, as well as Hamiltonian Neural Networks and Continuous Normalizing Flows. DiffEqFlux.jl is built upon DifferentialEquations.jl and Lux.jl, providing a robust framework for researchers and developers to explore advanced scientific machine learning methods.

DL-workshop-series is an open-source GitHub repository maintained by Machine Learning Tokyo (MLT), offering comprehensive materials for deep learning workshops. It features practical implementations and theoretical insights into convolution operations and learning processes within deep neural networks. The repository includes Colab notebooks with examples of kernel applications and functions for constructing Keras models, covering architectures like AlexNet, VGG, Inception, MobileNet, ResNet, and YOLO. It serves as a valuable resource for individuals and groups looking to learn and practice deep learning techniques, providing both code and presentation slides for a structured learning experience.

dl_tutorials is an open-source GitHub repository offering a comprehensive set of deep learning tutorials, structured into weekly modules. It guides users through fundamental concepts such as Python basics, logistic regression, and optimization methods, progressing to advanced topics like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their applications in image detection, semantic segmentation, and handwriting generation. The tutorials include practical exercises, such as implementing MLPs and CNNs on custom datasets, and cover modern architectures like AlexNet, GoogLeNet, and Residual Networks. It also delves into advanced concepts like deep reinforcement learning, adversarial attacks, and generative adversarial networks, making it a valuable resource for those looking to understand and implement deep learning techniques.

nucleotide-transformer is an open-source repository from InstaDeep AI, dedicated to advancing genomics and transcriptomics through cutting-edge deep learning models. It features a collection of transformer-based genomic language models and innovative downstream applications, including the Nucleotide Transformer (NT), Agro Nucleotide Transformer (AgroNT), SegmentNT, and ChatNT. The platform provides powerful, reproducible, and accessible tools for unlocking new insights from biological sequences, offering pre-trained weights, inference code, and research contributions. It supports various tasks such as functional-track prediction, genome annotation, controllable sequence generation, and single-cell transcriptomics, making it a central hub for AI-driven genomic research.