{"id":15158997,"url":"https://github.com/suegk/deep-learning-journey","last_synced_at":"2026-02-06T03:01:21.252Z","repository":{"id":159553546,"uuid":"523318816","full_name":"SueGK/Deep-Learning-Journey","owner":"SueGK","description":"The curated list of deep learning resources","archived":false,"fork":false,"pushed_at":"2022-08-20T14:13:13.000Z","size":44,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-11-09T20:02:55.186Z","etag":null,"topics":["awesome-list","computer-vision","deep-learning","deep-networks","deep-neural-networks","deeplearning","deeplearning-ai","pytorch-tutorial","tensorflow-tutorials"],"latest_commit_sha":null,"homepage":"","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/SueGK.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}},"created_at":"2022-08-10T11:36:11.000Z","updated_at":"2022-08-10T14:48:12.000Z","dependencies_parsed_at":"2023-05-21T19:00:42.044Z","dependency_job_id":null,"html_url":"https://github.com/SueGK/Deep-Learning-Journey","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/SueGK/Deep-Learning-Journey","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SueGK%2FDeep-Learning-Journey","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SueGK%2FDeep-Learning-Journey/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SueGK%2FDeep-Learning-Journey/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SueGK%2FDeep-Learning-Journey/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SueGK","download_url":"https://codeload.github.com/SueGK/Deep-Learning-Journey/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SueGK%2FDeep-Learning-Journey/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29147373,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-06T02:39:25.012Z","status":"ssl_error","status_checked_at":"2026-02-06T02:37:22.784Z","response_time":59,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["awesome-list","computer-vision","deep-learning","deep-networks","deep-neural-networks","deeplearning","deeplearning-ai","pytorch-tutorial","tensorflow-tutorials"],"created_at":"2024-09-26T21:01:52.307Z","updated_at":"2026-02-06T03:01:21.225Z","avatar_url":"https://github.com/SueGK.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Deep-Learning-Journey\nThe curated list of deep learning resources\n\n# TOC\n\n- [Deep Learning foundation](https://github.com/SueGK/Deep-Learning-Journey/edit/main/README.md#deep-learning-foundation)\n- [Deep Learning Visualization](#visualization)\n- [Pytorch](#pytorch)\n  * [Tutorial](#tutorial)\n  * [Code template \u0026 example](https://github.com/SueGK/Deep-Learning-Journey/edit/main/README.md#code-template--example)\n- [Tensorflow](#tensorflow)\n  * [Tutorial](#tutorial-1)\n  * [Code template \u0026 example](https://github.com/SueGK/Deep-Learning-Journey/edit/main/README.md#code-template--example-1)\n- [Paper](#paper)\n- [Computer Vision](https://github.com/SueGK/Deep-Learning-Journey/edit/main/README.md#computer-vision)\n- [Tool](#tool)\n  * [Library](#library)\n  * [Cheetsheet](#cheetsheet)\n- [MLOps](#mlops)\n  * [Docker](#docker)\n\n\n# Deep Learning foundation\n\n* [The spelled-out intro to neural networks and backpropagation: building micrograd - YouTube](https://www.youtube.com/watch?v=VMj-3S1tku0)\n  * This is the most step-by-step spelled-out explanation of backpropagation and training of neural networks. It only assumes basic knowledge of Python and a vague recollection of calculus from high school.\n  * Links:\n     - micrograd on github: https://github.com/karpathy/micrograd\n     - jupyter notebooks I built in this video: [randomfun/lectures/micrograd at master · karpathy/randomfun](https://github.com/karpathy/randomfun/tree/master/lectures/micrograd)\n     - my website: https://karpathy.ai\n     - my twitter: https://twitter.com/karpathy\n* [Deep Learning Specialization-Andrew NG](https://www.coursera.org/specializations/deep-learning)\n  * [ashishpatel26/Andrew-NG-Notes: This is Andrew NG Coursera Handwritten Notes.](https://github.com/ashishpatel26/Andrew-NG-Notes) \n\n# Visualization\n\n* [Convolution Visualizer](https://ezyang.github.io/convolution-visualizer/index.html): \n  * This interactive visualization demonstrates how various convolution parameters affect shapes and data dependencies between the input, weight and output matrices.\n  * ![CleanShot 2022-08-10 at 15 19 52](https://user-images.githubusercontent.com/71711489/183925490-abc8c619-4aab-4663-a654-facf045c28b1.gif)\n* [julrog/nn_vis: A project for processing neural networks and rendering to gain insights on the architecture and parameters of a model through a decluttered representation.](https://github.com/julrog/nn_vis)\n* [Convolution Neural Network Visualization - Made with Unity 3D and lots of Code](https://www.reddit.com/r/MachineLearning/comments/leq2kf/d_convolution_neural_network_visualization_made/) \n  * ![CleanShot 2022-08-10 at 15 00 56](https://user-images.githubusercontent.com/71711489/183921554-cb756522-f8ed-4fde-99a6-aa415aafc307.gif)\n  * [Stefan Sietzen || Visuality](https://vimeo.com/stefsietz) \n* [CNN Explainer](https://poloclub.github.io/cnn-explainer/)\n  * ![CleanShot 2022-08-10 at 15 23 11](https://user-images.githubusercontent.com/71711489/183926850-dca26c6b-781e-434d-9b91-c5dcfb5755e2.gif)\n\n\n# Pytorch\n\n## Tutorial\n\n* [Deep Learning with PyTorch: A 60 Minute Blitz — PyTorch Tutorials 1.12.0+cu102 documentation](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html): Pytorch official tutorial\n\n* [deeplizard - PyTorch - Python Deep Learning Neural Network API](https://www.youtube.com/watch?v=v5cngxo4mIg\u0026list=PLZbbT5o_s2xrfNyHZsM6ufI0iZENK9xgG)\n  * Deep explaination of tensor\n  * \u003cimg src=\"https://user-images.githubusercontent.com/71711489/183897714-9e8b8508-2bc3-4bfd-b61e-600c4bd47711.png\" width=\"400\"\u003e\n  * [My Course Notes](https://github.com/SueGK/Deep-Learning-Journey/blob/main/DeepLizard.md)\n\n* [Practical Deep Learning for Coders - Practical Deep Learning](https://course.fast.ai/)\n  * A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.\n  * After finishing this course you will know:\n    - How to train models that achieve state-of-the-art results in:\n    - Computer vision, including image classification (e.g., classifying pet photos by breed)\n    - Natural language processing (NLP), including document classification (e.g., movie review sentiment analysis) and phrase similarity\n    - Tabular data with categorical data, continuous data, and mixed data\n    - Collaborative filtering (e.g., movie recommendation)\n    - How to turn your models into web applications, and deploy them\n    - Why and how deep learning models work, and how to use that knowledge to improve the accuracy, speed, and reliability of your models\n    - The latest deep learning techniques that really matter in practice\n    - How to implement stochastic gradient descent and a complete training loop from scratch\n    \n  * [![FastAPI](https://img.shields.io/badge/FastAPI-0.63.0-009688.svg?style=flat\u0026logo=FastAPI\u0026logoColor=white)](https://fastapi.tiangolo.com) [![pytorch](https://img.shields.io/badge/PyTorch-1.6.0-EE4C2C.svg?style=flat\u0026logo=pytorch)](https://pytorch.org)\n\n* [datawhalechina/thorough-pytorch: PyTorch入门教程](https://github.com/datawhalechina/thorough-pytorch): Chinese pytorch tutorial\n\n## Code template \u0026 example\n* [jcjohnson/pytorch-examples: Simple examples to introduce PyTorch](https://github.com/jcjohnson/pytorch-examples)\n * \u003cimg src=\"https://user-images.githubusercontent.com/71711489/183907356-d696c286-3666-4718-a870-f70519eab1d9.png\" width=\"400\"\u003e\n\n# Tensorflow\n\n## Tutorial\n\n## Code template \u0026 example\n\n* [MrGemy95/Tensorflow-Project-Template: A best practice for tensorflow project template architecture.](https://github.com/Mrgemy95/Tensorflow-Project-Template#project-architecture): a tensorflow project template that combines simplcity, best practice for folder structure and good OOP design. \n\n# Paper\n\n* [Browse the State-of-the-Art in Machine Learning | Papers With Code](https://paperswithcode.com/sota)\n* [The latest in Machine Learning | Papers With Code](https://paperswithcode.com/)\n* [Stateoftheart AI](https://www.stateoftheart.ai/): An open-data and free platform built by the research community to facilitate the collaborative development of AI\n* [labmlai/annotated_deep_learning_paper_implementations: 🧑‍🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝](https://github.com/labmlai/annotated_deep_learning_paper_implementations)\n* [DengBoCong/nlp-paper: 自然语言处理领域下的相关论文（附阅读笔记），复现模型以及数据处理等（代码含TensorFlow和PyTorch两版本）](https://github.com/DengBoCong/nlp-paper)\n\n# Computer Vision\n* [dmlc/gluon-cv: Gluon CV Toolkit](https://github.com/dmlc/gluon-cv): \n  * GluonCV provides implementations of the state-of-the-art (SOTA) deep learning models in computer vision.\n  * ![short_demo](https://user-images.githubusercontent.com/71711489/183931315-ceb4c332-3a47-471b-8c5b-da64b18c2f7d.gif)\n  \n# Tool\n\n## Library\n\n[ml-tooling/best-of-ml-python: 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.](https://github.com/ml-tooling/best-of-ml-python)\n\n## Cheetsheet\n\n* [wzchen/probability_cheatsheet: A comprehensive 10-page probability cheatsheet that covers a semester's worth of introduction to probability.](https://github.com/wzchen/probability_cheatsheet)\n\n* [The Ultimate Docker Cheat Sheet | dockerlabs](https://dockerlabs.collabnix.com/docker/cheatsheet/)\n\n# MLOps\n\n## MLOps Course\n* [DataTalksClub/mlops-zoomcamp: Free MLOps course from DataTalks.Club](https://github.com/DataTalksClub/mlops-zoomcamp)\n* [Course 2022 - Full Stack Deep Learning](https://fullstackdeeplearning.com/course/2022/)\n\n## Docker\n\n* [collabnix/dockerlabs: Docker - Beginners | Intermediate | Advanced](https://github.com/collabnix/dockerlabs)\n\n* [docker/awesome-compose: Awesome Docker Compose samples](https://github.com/docker/awesome-compose)\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsuegk%2Fdeep-learning-journey","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsuegk%2Fdeep-learning-journey","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsuegk%2Fdeep-learning-journey/lists"}