{"id":16562788,"url":"https://github.com/charmve/numpycnn","last_synced_at":"2026-04-22T05:31:21.484Z","repository":{"id":106310070,"uuid":"336831383","full_name":"Charmve/NumPyCNN","owner":"Charmve","description":"Building Convolutional Neural Networks From Scratch using NumPy","archived":false,"fork":false,"pushed_at":"2021-02-07T16:29:27.000Z","size":19,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-04-15T09:05:26.796Z","etag":null,"topics":["cnn","computer-vision","deep-learning","deep-learning-tutorial","machine-learning","numpy","project","python","tutotrial"],"latest_commit_sha":null,"homepage":"https://gitbook.cn/new/gitchat/activity/5fddbca4201c01667e62c3c4","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Charmve.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2021-02-07T16:19:56.000Z","updated_at":"2023-04-01T12:21:02.000Z","dependencies_parsed_at":"2023-06-28T09:56:03.001Z","dependency_job_id":null,"html_url":"https://github.com/Charmve/NumPyCNN","commit_stats":{"total_commits":5,"total_committers":1,"mean_commits":5.0,"dds":0.0,"last_synced_commit":"f049a808c9a37d82ab034921240b394a3d03ae3e"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Charmve%2FNumPyCNN","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Charmve%2FNumPyCNN/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Charmve%2FNumPyCNN/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Charmve%2FNumPyCNN/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Charmve","download_url":"https://codeload.github.com/Charmve/NumPyCNN/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241975135,"owners_count":20051428,"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":["cnn","computer-vision","deep-learning","deep-learning-tutorial","machine-learning","numpy","project","python","tutotrial"],"created_at":"2024-10-11T20:37:09.637Z","updated_at":"2026-04-22T05:31:21.438Z","avatar_url":"https://github.com/Charmve.png","language":"Python","readme":"# NumPyCNN\nNumPyCNN is a Python implementation for convolutional neural networks (CNNs) from scratch using NumPy. \n\n![Building CNN in Python](https://user-images.githubusercontent.com/16560492/82431022-6c3a1200-9a8e-11ea-8f1b-b055196d76e3.png)\n\n**IMPORTANT** *If you are coming for the code of the gitchat tutorial titled [Building Convolutional Neural Network using NumPy from Scratch (手把手带你开始计算机视觉项目——使用NumPy亲手搭建CNN，熟悉网络结结构)](https://gitbook.cn/gitchat/activity/5fddbca4201c01667e62c3c4), then you would just use the python code `NumPyCNN.py`.\n\nThe project has a single module named `cnn.py` which implements all classes and functions needed to build the CNN.\n\nIt is very important to note that the project only implements the **forward pass** of training CNNs and there is **no learning algorithm used**. Just the learning rate is used to make some changes to the weights after each epoch which is better than leaving the weights unchanged.\n\n# Tutorials\n\nThere are different resources that can be used to get started with the building CNN and its Python implementation. \n\n## 1. Build Neural Networks in Python\n\nRead about building neural networks in Python through the tutorial titled [**Artificial Neural Network Implementation using NumPy and Classification of the Fruits360 Image Dataset**](https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad) available at these links:\n\n- [Towards Data Science](https://towardsdatascience.com/artificial-neural-network-implementation-using-numpy-and-classification-of-the-fruits360-image-3c56affa4491)\n- [KDnuggets](https://www.kdnuggets.com/2019/02/artificial-neural-network-implementation-using-numpy-and-image-classification.html)\n\n[![Building Neural Networks Python](https://user-images.githubusercontent.com/16560492/82078281-30472b80-96e1-11ea-8017-6a1f4383d602.jpg)](https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad)\n\n## 2. NumPyCNN: Building CNN in Python\n\nTo start with coding the genetic algorithm, you can check the tutorial titled [**Building Convolutional Neural Network using NumPy from Scratch**](https://gitbook.cn/new/gitchat/activity/5fddbca4201c01667e62c3c4) available at these links:\n\n- [Towards Data Science](https://towardsdatascience.com/building-convolutional-neural-network-using-numpy-from-scratch-b30aac50e50a)\n- [GitChat](https://gitbook.cn/gitchat/activity/5fddbca4201c01667e62c3c4)\n\n[![Building CNN in Python](https://user-images.githubusercontent.com/16560492/82431022-6c3a1200-9a8e-11ea-8f1b-b055196d76e3.png)](https://gitbook.cn/new/gitchat/activity/5fddbca4201c01667e62c3c4)\n\n## 3. Derivation of CNN from FCNN\n\nGet started with the genetic algorithm by reading the tutorial titled [**Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step**](https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad) which is available at these links:\n\n- [Towards Data Science](https://towardsdatascience.com/derivation-of-convolutional-neural-network-from-fully-connected-network-step-by-step-b42ebafa5275)\n- [KDnuggets](https://www.kdnuggets.com/2018/04/derivation-convolutional-neural-network-fully-connected-step-by-step.html)\n\n[![Derivation of CNN from FCNN](https://user-images.githubusercontent.com/16560492/82431369-db176b00-9a8e-11ea-99bd-e845192873fc.png)](https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad)\n\n## Book: Practical Computer Vision Applications Using Deep Learning with CNNs\n\nYou can also check my book cited as [**Ahmed Fawzy Gad 'Practical Computer Vision Applications Using Deep Learning with CNNs'. Dec. 2018, Apress, 978-1-4842-4167-7**](https://www.amazon.com/Practical-Computer-Vision-Applications-Learning/dp/1484241665) which discusses neural networks, convolutional neural networks, deep learning, genetic algorithm, and more.\n\n- [Springer](https://link.springer.com/book/10.1007/978-1-4842-4167-7)\n- [O'Reilly](https://www.oreilly.com/library/view/practical-computer-vision/9781484241677)\n- [Google Books](https://books.google.com.eg/books?id=xLd9DwAAQBAJ)\n\n![Fig04](https://user-images.githubusercontent.com/16560492/78830077-ae7c2800-79e7-11ea-980b-53b6bd879eeb.jpg)\n\n## Acknowledgement\n\n@GitChat, @ahmedfgad\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcharmve%2Fnumpycnn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcharmve%2Fnumpycnn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcharmve%2Fnumpycnn/lists"}