{"id":20081497,"url":"https://github.com/ahkarami/great-deep-learning-tutorials","last_synced_at":"2025-03-02T13:40:50.474Z","repository":{"id":41476596,"uuid":"177964300","full_name":"ahkarami/Great-Deep-Learning-Tutorials","owner":"ahkarami","description":"A Great Collection of Deep Learning Tutorials and Repositories","archived":false,"fork":false,"pushed_at":"2024-04-12T22:21:03.000Z","size":997,"stargazers_count":164,"open_issues_count":0,"forks_count":41,"subscribers_count":6,"default_branch":"master","last_synced_at":"2024-04-13T09:47:42.233Z","etag":null,"topics":["computer-vision","deep-learning","deep-learning-tutorial","deep-neural-networks","gan","machine-learning","nlp"],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ahkarami.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2019-03-27T09:47:11.000Z","updated_at":"2024-04-14T22:41:01.826Z","dependencies_parsed_at":"2023-10-22T07:24:26.966Z","dependency_job_id":"a043f9e8-42a2-408a-ac2a-fa7f2e595b6a","html_url":"https://github.com/ahkarami/Great-Deep-Learning-Tutorials","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ahkarami%2FGreat-Deep-Learning-Tutorials","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ahkarami%2FGreat-Deep-Learning-Tutorials/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ahkarami%2FGreat-Deep-Learning-Tutorials/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ahkarami%2FGreat-Deep-Learning-Tutorials/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ahkarami","download_url":"https://codeload.github.com/ahkarami/Great-Deep-Learning-Tutorials/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241515904,"owners_count":19975139,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","deep-learning","deep-learning-tutorial","deep-neural-networks","gan","machine-learning","nlp"],"created_at":"2024-11-13T15:39:18.034Z","updated_at":"2025-03-02T13:40:50.445Z","avatar_url":"https://github.com/ahkarami.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Great-Deep-Learning-Tutorials\nA Great Collection of Deep Learning Tutorials and Repositories\n\n## General Deep Learning Tutorials:\n- [Browse state-of-the-art Deep Learning based Papers with their associated codes](https://paperswithcode.com/sota) [_Extremely Fantastic_]\n- [Deep-Learning-Roadmap](https://github.com/astorfi/Deep-Learning-Roadmap)  \n- [DeepLizard](https://deeplizard.com/) [_Good Tutorials for Deep Learning_]  \n- [Sebastian Ruder - Blog](https://ruder.io/) [_Great NLP \u0026 Deep Learning Posts_]  \n- [Jeremy Jordan - Blog](https://www.jeremyjordan.me/author/jeremy/)  \n- [Excellent Blog](https://lilianweng.github.io/lil-log/)  \n- [Torchvision Release Notes](https://github.com/pytorch/vision/releases)  [_Important_]\n- [The 6 most useful Machine Learning projects of the past year (2018)](https://towardsdatascience.com/the-10-most-useful-machine-learning-projects-of-the-past-year-2018-5378bbd4919f)  \n- [ResNet Review](https://towardsdatascience.com/review-resnet-winner-of-ilsvrc-2015-image-classification-localization-detection-e39402bfa5d8)  \n- [Receptive Field Estimation](https://github.com/fornaxai/receptivefield)  [_Great_]  \n- [An overview of gradient descent optimization algorithms](https://ruder.io/optimizing-gradient-descent/) [_Useful_]  \n- [How to decide on learning rate](https://towardsdatascience.com/how-to-decide-on-learning-rate-6b6996510c98)  \n- [Overview of State-of-the-art Machine Learning Algorithms per Discipline per Task](https://towardsdatascience.com/overview-state-of-the-art-machine-learning-algorithms-per-discipline-per-task-c1a16a66b8bb)  \n- [Practical Machine Learning](https://github.com/youssefHosni/Practical-Machine-Learning)  \n- [Awesome Machine Learning and AI Courses](https://github.com/luspr/awesome-ml-courses)  \n- [UVA Deep Learning II Course](https://uvadl2c.github.io/)  \n- [PyTorch Book](https://github.com/chenyuntc/pytorch-book)  \n- [Fast.ai Course: Practical Deep Learning for Coders](https://course.fast.ai/Lessons/lesson1.html) [**Great**]  \n- [Neuromatch Deep Learning Course](https://deeplearning.neuromatch.io/tutorials/intro.html) [**Great**]  \n- [labmlai: 59 Implementations/tutorials of deep learning papers with side-by-side notes](https://github.com/labmlai/annotated_deep_learning_paper_implementations) [**Great**]  \n- [labml.ai](https://nn.labml.ai/index.html)  \n- [FightingCV-Paper-Reading: understand the most advanced research work in an easier way](https://github.com/xmu-xiaoma666/FightingCV-Paper-Reading)  \n- [Learn PyTorch for Deep Learning: Zero to Mastery Course](https://github.com/mrdbourke/pytorch-deep-learning) [**Excellent**]  \n- [ML Papers Explained](https://github.com/dair-ai/ML-Papers-Explained) [**Excellent**]  \n- [Alpha Signal: Latest Research in Machine Learning](https://alphasignal.ai/)  \n- [Harvard CS197: AI Research Experiences - The Course Book](https://docs.google.com/document/u/0/d/1uvAbEhbgS_M-uDMTzmOWRlYxqCkogKRXdbKYYT98ooc/mobilebasic#heading=h.bko37p9m9o8g) [**Excellent**]\n- [Deep learning jupyter notebook book](https://udlbook.github.io/udlbook/)  \n- [A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT](https://arxiv.org/abs/2302.09419)\n- [interconnects.ai: Great AI Blog Posts \u0026 Podcasts](https://www.interconnects.ai/)\n- [The Fundamental of Modern Deep Learning with PyTorch (short Course)](https://www.linkedin.com/posts/sebastianraschka_github-rasbtpycon2024-tutorial-materials-activity-7196468139289677827-qUlf?utm_source=share\u0026utm_medium=member_android)\n- [Google ML Crash Course](https://www.linkedin.com/posts/neilhoyne_google-lifeatgoogle-ai-activity-7262238201635868675-va5Q?utm_source=share\u0026utm_medium=member_desktop)\n- [Intel AI Developer Course](https://www.linkedin.com/posts/eric-vyacheslav-156273169_want-to-become-a-top-ai-developer-intel-activity-7264293889837051904-nS1t?utm_source=share\u0026utm_medium=member_desktop)  \n- [EfficientML Course](https://www.youtube.com/playlist?list=PL80kAHvQbh-pT4lCkDT53zT8DKmhE0idB) [Great]\n- [Andrej Karpathy's Neural Networks: Zero to Hero Course](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)  \n\n## Deep Learning Useful Resources for Computer Vision:  \n- [Great Deep Learning Resources for Computer Vision Tasks](https://github.com/ahkarami/Great-Deep-Learning-Tutorials/blob/master/ComputerVision.md) [_Excellent_]  \n\n## Deep Learning Useful Resources for Natural Language Processing (NLP):  \n- [Great Deep Learning Resources for NLP Tasks](https://github.com/ahkarami/Great-Deep-Learning-Tutorials/blob/master/NLP.md) [_Excellent_]  \n\n## Deep Learning Useful Resources for Spoken Language Processing (Speech Processing):  \n- [Great Deep Learning Resources for Speech Processing Tasks](https://github.com/ahkarami/Great-Deep-Learning-Tutorials/blob/master/Speech.md) [_Excellent_]  \n\n## Deep Learning \u0026 Machine Learning Useful Resources for General Data Science Tasks:  \n- [Great Deep Learning Resources for Data Science Tasks](https://github.com/ahkarami/Great-Deep-Learning-Tutorials/blob/master/DataScience.md) [_Excellent_]  \n\n## General Notes about Generative AI:\n- [Generative AI in action: real-world applications and examples](https://lablab.ai/blog/generative-ai-in-action-real-world-applications-and-examples)  \n\n## Quantization \u0026 Distillation of Deep Learning Models:\n- [Quantization](https://nervanasystems.github.io/distiller/quantization/)  \n- [Neural Network Distiller](https://github.com/NervanaSystems/distiller/)  \n- [Introduction to Quantization on PyTorch](https://pytorch.org/blog/introduction-to-quantization-on-pytorch/) [_Excellent_]  \n- [Dynamic Quantization in PyTorch](https://pytorch.org/tutorials/advanced/dynamic_quantization_tutorial.html)  \n- [Static Quantization in PyTorch](https://pytorch.org/tutorials/advanced/static_quantization_tutorial.html)  \n- [Intel(R) Math Kernel Library - Intel MKL-DNN](https://github.com/intel/mkl-dnn)  \n- [Intel MKL-Dnn](https://01.org/mkl-dnn)  \n- [ONNX Float32 to Float16](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/converter_scripts/float32_float16_onnx.ipynb)  \n- [Neural Network Quantization Introduction](https://jackwish.net/neural-network-quantization-introduction.html) [_Tutorial_]  \n- [Quantization in Deep Learning](https://medium.com/@joel_34050/quantization-in-deep-learning-478417eab72b) [_Tutorial_]  \n- [Speeding up Deep Learning with Quantization](https://towardsdatascience.com/speeding-up-deep-learning-with-quantization-3fe3538cbb9) [_Tutorial_]  \n- [Knowledge Distillation in Deep Learning](https://medium.com/analytics-vidhya/knowledge-distillation-dark-knowledge-of-neural-network-9c1dfb418e6a)  \n- [Model Distillation Techniques for Deep Learning](https://heartbeat.fritz.ai/research-guide-model-distillation-techniques-for-deep-learning-4a100801c0eb)  \n- [MMRazor: model compression toolkit](https://github.com/open-mmlab/mmrazor) [Great]  \n- [FP8 Quantization: The Power of the Exponent](https://github.com/Qualcomm-AI-research/FP8-quantization)\n- [Quanto: a pytorch quantization toolkit](https://huggingface.co/blog/quanto-introduction) [**Great**]  \n\n## AutoML:\n- [Auto Gluon AI](https://auto.gluon.ai/stable/index.html#)  \n- [AWS Auto Gluon](https://github.com/awslabs/autogluon)  \n\n## Diffusion Models:\n- [Diffusion Models via lilianweng](https://lilianweng.github.io/posts/2021-07-11-diffusion-models/)  \n- [Diffusion Models Papers Survey Taxonomy](https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy)  \n- [Phenaki: a text-to-video model](https://github.com/LAION-AI/phenaki)  \n\n## Multimodal Deep Learning:\n- [Multimodal Deep Learning Book](https://arxiv.org/abs/2301.04856)\n- [Understanding MultiModal LLMs](https://www.linkedin.com/posts/sebastianraschka_there-has-been-a-lot-of-new-research-on-the-activity-7258836067129139200-QJkr?utm_source=share\u0026utm_medium=member_desktop)  \n\n## Deep Reasoning:\n- [What’s Next For AI? Enter: Deep Reasoning](https://towardsdatascience.com/whats-next-for-ai-enter-deep-reasoning-fae8b131962a)  \n- [Deep Learning approaches to understand Human Reasoning](https://towardsdatascience.com/deep-learning-approaches-to-understand-human-reasoning-46f1805d454d)  \n\n## Deep Reinforcement Learning (Great Courses \u0026 Tutorials):\n- [A Free course in Deep Reinforcement Learning from beginner to expert](https://simoninithomas.github.io/Deep_reinforcement_learning_Course/) [_Great_] \n- [Deep Reinforcement Learning Algorithms with PyTorch](https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch)  \n- [Deep Reinforcement Learning - CS 285 Berkeley Course](rail.eecs.berkeley.edu/deeprlcourse/)  \n- [solutions to UC Berkeley CS 285](https://github.com/xuanlinli17/CS285_Fa19_Deep_Reinforcement_Learning)  \n- [Reinforcement Learning: An Introduction - main book in this field](http://www.incompleteideas.net/book/the-book-2nd.html)  \n- [CS234: Reinforcement Learning Course](https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u)  \n- [Introduction to Reinforcement Learning Course - by DeepMind](https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u)  \n\n## Graph Neural Networks:\n- [An Introduction to Graph Neural Networks](https://towardsdatascience.com/an-introduction-to-graph-neural-networks-e23dc7bdfba5)  \n- [How to Train Graph Convolutional Network Models in a Graph Database](https://towardsdatascience.com/how-to-train-graph-convolutional-network-models-in-a-graph-database-5c919a2f95d7)  \n- [A comprehensive survey on graph neural networks](https://arxiv.org/pdf/1901.00596)  \n- [Graph Neural Networks: A Review of Methods and Applications](https://arxiv.org/abs/1812.08434)  \n\n### Graph Neural Networks Frameworks:\n- [Spektral](https://github.com/danielegrattarola/spektral)  \n- [Deep Graph Library - DGL](https://www.dgl.ai/)  \n- [PyTorch Geometric - PyG](https://github.com/rusty1s/pytorch_geometric)  \n- [ptgnn: A PyTorch GNN Library](https://github.com/microsoft/ptgnn)  \n- [Graph Data Augmentation Papers](https://github.com/zhao-tong/graph-data-augmentation-papers)  \n- [Neo4j: Graph Data Platform](https://neo4j.com/)  \n\n## Best Practices for Training Deep Models:\n\n### General Notes for Training Deep Models:\n- [Deep Learning Tuning Playbook](https://github.com/google-research/tuning_playbook)  \n\n### PyTorch Lightening Notes \u0026 Accumulate Gradients:\n- [PyTorch Lightening: Effective Training Techniques](https://pytorch-lightning.readthedocs.io/en/latest/advanced/training_tricks.html)  \n- [Gradient Accumulation in PyTorch](https://kozodoi.me/python/deep%20learning/pytorch/tutorial/2021/02/19/gradient-accumulation.html)  \n  \n### Loss Functions:\n- [Loss Functions Explained](https://medium.com/deep-learning-demystified/loss-functions-explained-3098e8ff2b27)  \n\n### Imbalanced Dataset Handling:\n- [deal with an imbalanced dataset using weightedrandomsampler](https://androidkt.com/deal-with-an-imbalanced-dataset-using-weightedrandomsampler-in-pytorch/)  \n- [imbalanced-dataset-sampler](https://github.com/ufoym/imbalanced-dataset-sampler) [Great]  \n- [demystifying pytorchs weightedrandomsampler](https://towardsdatascience.com/demystifying-pytorchs-weightedrandomsampler-by-example-a68aceccb452)  \n- [weighted random sampler oversample or undersample](https://stackoverflow.com/questions/67799246/weighted-random-sampler-oversample-or-undersample)  \n\n### Weight Initialization:\n- [Deep Learning Best Practices (1) - Weight Initialization](https://medium.com/usf-msds/deep-learning-best-practices-1-weight-initialization-14e5c0295b94)  \n\n### Batch Normalization:\n- [Batch Normalization in Neural Networks](https://towardsdatascience.com/batch-normalization-in-neural-networks-1ac91516821c)  \n- [Batch Normalization and Dropout in Neural Networks](https://towardsdatascience.com/batch-normalization-and-dropout-in-neural-networks-explained-with-pytorch-47d7a8459bcd)  \n- [Difference between Local Response Normalization and Batch Normalization](https://towardsdatascience.com/difference-between-local-response-normalization-and-batch-normalization-272308c034ac)  \n\n### Learning Rate Scheduling \u0026 Initialization:\n- [Automated Learning Rate Suggester](https://forums.fast.ai/t/automated-learning-rate-suggester/44199)  \n- [Learning Rate Finder - fastai](https://fastai1.fast.ai/callbacks.lr_finder.html)  \n- [Cyclical Learning Rates for Training Neural Networks](https://arxiv.org/abs/1506.01186)  \n- [ignite - Example of FastaiLRFinder](https://github.com/pytorch/ignite/blob/master/examples/notebooks/FastaiLRFinder_MNIST.ipynb)   \n- [Find Learning Rate - a gist code](https://gist.github.com/colllin/738cd2a9f0abec9be5e8b9becc23a812)    \n- [Learning rate finder - PyTorch Lightning](https://pytorch-lightning.readthedocs.io/en/1.1.3/lr_finder.html)  \n- [RAdam - On the Variance of the Adaptive Learning Rate and Beyond](https://github.com/LiyuanLucasLiu/RAdam)  \n\n### Early Stopping:\n- [Early Stopping in PyTorch - Bjarten](https://github.com/Bjarten/early-stopping-pytorch)  \n- [Catalyst - Early Stopping](https://catalyst-team.github.io/catalyst/faq/early_stopping.html)  \n- [ignite - Early Stopping](https://github.com/pytorch/ignite/blob/master/ignite/handlers/early_stopping.py)  \n- [PyTorch High-Level Training Sample](https://github.com/ncullen93/torchsample/blob/master/README.md)    \n- [PyTorch Discussion about Early Stopping](https://discuss.pytorch.org/t/early-stopping-in-pytorch/18800)  \n\n### Tuning Guide Recipes:\n- [PyTorch Tuning Guide Tutorial](https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html)  \n- [PyTorch memory leak with dynamic size tensor input](https://github.com/pytorch/pytorch/issues/29893)   \n- [Karpathy: A Recipe for Training Neural Networks](http://karpathy.github.io/2019/04/25/recipe/)  \n\n### Training Optimizer:\n- [What is gradient accumulation in deep learning](https://towardsdatascience.com/what-is-gradient-accumulation-in-deep-learning-ec034122cfa)  \n\n### PyTorch running \u0026 training on TPU (colab):\n- [PyTorch XLA](https://github.com/pytorch/xla)   \n- [PyTorch XLA Colab](https://github.com/pytorch/xla/tree/master/contrib/colab)  \n\n### Evaluation Metrics:\n- [Performance Metrics for Classification Problems in ML](https://medium.com/@MohammedS/performance-metrics-for-classification-problems-in-machine-learning-part-i-b085d432082b)  \n\n### Validating ML Models:\n- [Deepchecks: Validating ML Models \u0026 Data](https://github.com/deepchecks/deepchecks)  \n\n### Optimizing models when run on GPU:\n- [Tips for reducing vram of gpu memories](https://www.linkedin.com/posts/pauliusztin_machinelearning-mlops-datascience-activity-7137704771905277953-G8Qt?utm_source=share\u0026utm_medium=member_desktop)  \n\n## Conferences News:\n- [Latest Computer Vision Trends from CVPR 2019](https://towardsdatascience.com/latest-computer-vision-trends-from-cvpr-2019-c07806dd570b)  \n- [Interesting 2019 CVPR papers](https://medium.com/@mattmiesnieks/interesting-2019-cvpr-papers-865e303db5ca)  \n- [Summaries of CVPR papers on ShortScience.org](https://www.shortscience.org/venue?key=conf/cvpr)\n- [Summaries of ICCV papers on ShortScience.org](https://www.shortscience.org/venue?key=conf/iccv)\n- [Summaries of ECCV papers on ShortScience.org](https://www.shortscience.org/venue?key=conf/eccv)\n- [Meta ICLR 2024 Top Papers](https://www.linkedin.com/posts/aiatmeta_iclr2024-activity-7194398361943171074-XiVG?utm_source=share\u0026utm_medium=member_android)  \n\n## Deep Learning Frameworks and Infrustructures:\n- [set-up a Paperspace GPU Server](https://towardsdatascience.com/how-to-set-up-a-powerful-and-cost-efficient-gpu-server-for-deep-learning-aa1de0d4ea56)  \n- [Distributed ML with OpenMPI](https://clusterone.com/tutorials/openmpi-introduction)  \n- [Tensorflow 2.0 vs Mxnet](https://medium.com/@mouryarishik/tensorflow-2-0-vs-mxnet-41edd3b7574f)  \n- [TensorFlow is dead, long live TensorFlow!](https://hackernoon.com/tensorflow-is-dead-long-live-tensorflow-49d3e975cf04)  \n\n## Great Libraries:\n- [The Unified Machine Learning Framework](https://github.com/unifyai/ivy)  \n- [Skorch - A scikit-learn compatible neural network library that wraps PyTorch](https://github.com/skorch-dev/skorch)  \n- [Hummingbird - traditional ML models into tensor computations via PyTorch](https://github.com/microsoft/hummingbird)  \n- [BoTorch - Bayesian Optimization in PyTorch](https://botorch.org/)  \n- [torchvision 0.3: segmentation, detection models, new datasets and more](https://pytorch.org/blog/torchvision03/)  \n- [TorchAudio: an audio library for PyTorch](https://github.com/pytorch/audio)  \n- [AudTorch](https://github.com/audeering/audtorch)  \n- [TorchAudio-Contrib](https://github.com/keunwoochoi/torchaudio-contrib) \n- [fastText - Facebook AI Research (FAIR)](https://fasttext.cc/)  \n- [Fairseq - Facebook AI Research (FAIR)](https://github.com/pytorch/fairseq)  \n- [ParlAI - dialogue models - Facebook AI Research (FAIR)](https://parl.ai/)  \n- [DALI - highly optimized engine for data pre-processing](https://github.com/NVIDIA/DALI)  \n- [Netron - GitHub](https://github.com/lutzroeder/netron) [_Visualizer for deep learning Models (Excellent)_]\n- [Netron - Web Site](https://www.lutzroeder.com/ai)  \n- [JupyterLab GPU Dashboards](https://github.com/rapidsai/jupyterlab-nvdashboard) [_Good_]  \n- [PyTorch Hub](https://pytorch.org/hub)  \n- [Neural Structured Learning (NSL) in TensorFlow](https://github.com/tensorflow/neural-structured-learning)  \n- [Pywick - High-Level Training framework for Pytorch](https://github.com/achaiah/pywick)  \n- [torchbearer: A model fitting library for PyTorch](https://github.com/pytorchbearer/torchbearer)  \n- [torchlayers - Shape inference for PyTorch (like in Keras)](https://github.com/szymonmaszke/torchlayers)  \n- [torchtext - GitHub](https://github.com/pytorch/text)  \n- [torchtext - Doc](https://torchtext.readthedocs.io/en/latest/)   \n- [Optuna - hyperparameter optimization framework](https://optuna.org/)  \n- [PyTorchLightning](https://github.com/PyTorchLightning/pytorch-lightning)  \n- [Nvidia - runx - An experiment management tool](https://github.com/NVIDIA/runx)  \n- [MLogger: a Machine Learning logger](https://github.com/oval-group/mlogger)  \n- [ClearML - ML/DL development and production suite](https://github.com/allegroai/clearml)  \n- [Lime: Explaining the predictions of any ML classifier](https://github.com/marcotcr/lime)   \n- [Microsoft UniLM AI](https://github.com/microsoft/unilm) [Great]   \n- [mlnotify: No need to keep checking your training](https://github.com/aporia-ai/mlnotify)  \n- [NVIDIA NeMo -  toolkit for creating Conversational AI (ASR, TTS, and NLP)](https://github.com/NVIDIA/NeMo)  \n- [Microsoft DeepSpeed](https://github.com/microsoft/DeepSpeed)  \n- [Mojo: a new programming language for AI developers](https://www.modular.com/mojo)\n- [MLX: An array framework for Apple silicon](https://github.com/ml-explore/mlx)  \n\n## Great Models:\n- [ResNext WSL](https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/) [_Great Pretrained Model_]  \n- [Semi-Weakly Supervised (SWSL) ImageNet Models](https://pytorch.org/hub/facebookresearch_semi-supervised-ImageNet1K-models_resnext/) [_Great Pretrained Model_]  \n- [Deep High-Resolution Representation Learning (HRNet)](https://jingdongwang2017.github.io/Projects/HRNet/)  \n\n## Deep Model Conversion:\n- [Convert Full ImageNet Pre-trained Model from MXNet to PyTorch](https://blog.paperspace.com/convert-full-imagenet-pre-trained-model-from-mxnet-to-pytorch/) [_Great_] \n- [ONNX Runtime](https://github.com/microsoft/onnxruntime)  \n\n## Great Deep Learning Repositories (for learning DL-based programming):\n- [deeplearning-models - PyTorch \u0026 TensorFlow Learning](https://github.com/rasbt/deeplearning-models)  [_Very Excellent Repository_]  \n- [PyTorch Image Models](https://github.com/rwightman/pytorch-image-models) [_Great_] \n- [5 Advanced PyTorch Tools to Level up Your Workflow](https://towardsdatascience.com/5-advanced-pytorch-tools-to-level-up-your-workflow-d0bcf0603ad5) [_Interesting_]  \n\n## PyTorch High-Level Libraries:  \n- [Catalyst - PyTorch framework for Deep Learning research and development](https://github.com/catalyst-team/catalyst) [_Great_]  \n- [PyTorch Lightning - GitHub](https://github.com/PyTorchLightning/pytorch-lightning) [_Great_]    \n- [PyTorch Lightning - Web Page](https://pytorchlightning.ai/)  \n- [Ignite - GitHub](https://github.com/pytorch/ignite) [_Great_]    \n- [Ignite - Web Page](https://pytorch.org/ignite/)  \n- [TorchMetrics](https://torchmetrics.readthedocs.io/en/latest/)  \n- [Ludwig AI: Data-centric declarative deep learning framework](https://github.com/ludwig-ai/ludwig) [**Great**]  \n- [PyTorch Kineto: CPU+GPU Profiling library](https://github.com/pytorch/kineto/)  \n- [PyTorch Profiler](https://pytorch.org/docs/master/profiler.html)  \n- [PyTorch Benchmarks](https://github.com/pytorch/benchmark)  \n\n## Annotation Tools:  \n- [label-studio](https://github.com/heartexlabs/label-studio)   \n- [label-studio with RTL Support (for Persian)](https://github.com/mmaghajani/label-studio)   \n\n## Other:\n- [Clova AI Research - NAVER \u0026 LINE](https://github.com/clovaai)  \n- [Exploring Weight Agnostic Neural Networks](https://ai.googleblog.com/2019/08/exploring-weight-agnostic-neural.html)  \n- [Weight Agnostic Neural Networks](https://weightagnostic.github.io/)  \n- [Weight Agnostic Neural Networks - GitHub](https://github.com/google/brain-tokyo-workshop/tree/master/WANNRelease)  \n- [SAM: Sharpness-Aware Minimization for Efficiently Improving Generalization](https://github.com/google-research/sam)  \n- [Qualcomm Discusses Secret Dataset Generation Data](https://www.qualcomm.com/news/onq/2021/09/16/qa-ai-researcher-roland-memisevic-discusses-secret-dataset-generation-data)   \n- [State of AI Report 2021](https://www.stateof.ai/)\n- [State of AI Report 2024](https://www.stateof.ai/)  \n- [Project Blink: AI-powered video editing on the web](https://labs.adobe.com/projects/blink/)  \n- [PyTorch Incremental Learning](https://github.com/yaoyao-liu/class-incremental-learning)  \n- [Google Research, 2022 \u0026 Beyond: Language, Vision and Generative Models](https://ai.googleblog.com/2023/01/google-research-2022-beyond-language.html?m=1\u0026fbclid=PAAabtVizCEKhFC2kttHKozuEz4FX1cphjNDQVVL-kFZHA11GP9AVJ6rl9W-k)  \n- [Elicit: Ask a research question](https://elicit.org/) [Interesting]  \n- [Google People + AI Research (PAIR)](https://pair.withgoogle.com/) [Interesting business based AI topics]  \n- [Google Illuminate](https://illuminate.google.com/home) [Great]\n- [Google Learn About](https://learning.google.com/experiments/learn-about/signup) [Great]  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fahkarami%2Fgreat-deep-learning-tutorials","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fahkarami%2Fgreat-deep-learning-tutorials","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fahkarami%2Fgreat-deep-learning-tutorials/lists"}