{"id":20490425,"url":"https://github.com/hanzopgp/neuralnetworkfromscratch","last_synced_at":"2026-04-17T18:32:34.319Z","repository":{"id":109491636,"uuid":"321678862","full_name":"hanzopgp/NeuralNetworkFromScratch","owner":"hanzopgp","description":"This is a multi-layer perceptron build from scratch using numpy and python.","archived":false,"fork":false,"pushed_at":"2021-02-18T23:55:09.000Z","size":1217,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-05T17:25:09.409Z","etag":null,"topics":["artificial-intelligence","deep-learning","machine-learning","multi-layer-perceptron","numpy","personal"],"latest_commit_sha":null,"homepage":"","language":"Python","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/hanzopgp.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,"publiccode":null,"codemeta":null}},"created_at":"2020-12-15T13:38:30.000Z","updated_at":"2023-02-16T10:35:30.000Z","dependencies_parsed_at":"2023-07-14T21:45:18.377Z","dependency_job_id":null,"html_url":"https://github.com/hanzopgp/NeuralNetworkFromScratch","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/hanzopgp/NeuralNetworkFromScratch","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hanzopgp%2FNeuralNetworkFromScratch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hanzopgp%2FNeuralNetworkFromScratch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hanzopgp%2FNeuralNetworkFromScratch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hanzopgp%2FNeuralNetworkFromScratch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hanzopgp","download_url":"https://codeload.github.com/hanzopgp/NeuralNetworkFromScratch/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hanzopgp%2FNeuralNetworkFromScratch/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31940856,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-17T17:29:20.459Z","status":"ssl_error","status_checked_at":"2026-04-17T17:28:47.801Z","response_time":62,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 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":["artificial-intelligence","deep-learning","machine-learning","multi-layer-perceptron","numpy","personal"],"created_at":"2024-11-15T17:16:57.813Z","updated_at":"2026-04-17T18:32:34.300Z","avatar_url":"https://github.com/hanzopgp.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# NeuralNetworkFromScratch\n\n## Table of contents :\n\n1. [Presentation](#presentation-)\n3. [How to use](#how-to-use-)\n2. [How does it work](#how-does-it-work-)\n4. [Links](#links-)\n\n## Presentation\n\n\u003cp align=\"center\"\u003e\u003cimg src=\"img.png\"\u003e\u003c/p\u003e\n\n\u003eAfter coding a really simple perceptron, I wanted to build a multi-layer neural network from scratch using only numpy. I'm also currently reading the [nnfs](https://nnfs.io) book. I'm trying to go really slow on this project so I can learn and memorize all the informations. I'm reading a maximum of 2 chapters per session while making sure I understand each line of code and their theoretical context. I'm also writing a PDF file where you can find the mathematics behind that neural network, you can find it at the root of the repository.\n\n## Book's chapters \n\nChapter 1 - Introducing Neural Networks\u003cbr\u003e\nChapter 2 - Coding Our First Neurons\u003cbr\u003e\nChapter 3 - Adding Layers\u003cbr\u003e\nChapter 4 - Activation Functions\u003cbr\u003e\nChapter 5 - Loss\u003cbr\u003e\nChapter 6 - Optimization\u003cbr\u003e\nChapter 7 - Derivatives\u003cbr\u003e\nChapter 8 - Gradients, Partial Derivatives, and the Chain Rule\u003cbr\u003e\nChapter 9 - Backpropagation\u003cbr\u003e\nChapter 10 - Optimizers \u003c---------------------------------------------------------- HERE ATM\u003cbr\u003e\nChapter 11 - Testing Data\u003cbr\u003e\nChapter 12 - Validation Data\u003cbr\u003e\nChapter 13 - Training Dataset\u003cbr\u003e\nChapter 14 - L1 and L2 Regularization\u003cbr\u003e\nChapter 15 - Dropout\u003cbr\u003e\nChapter 16 - Binary Logistic Regression\u003cbr\u003e\nChapter 17 - Regression\u003cbr\u003e\nChapter 18 - Model Object\u003cbr\u003e\nChapter 19 - A Real Dataset\u003cbr\u003e\nChapter 20 - Model Evaluation\u003cbr\u003e\nChapter 21 - Saving and Loading Model Information\u003cbr\u003e\nChapter 22 - Model Predicting/Inference\n\n## How to use\n\n\u003eI'm currently thinking about interesting examples. Currently I'm testing it on different 2D vertical/spiral data plots.\n\n## How does it work\n\n\u003eYou can check the links below if you want further informations.\n \n## Links\n\n- https://nnfs.io\n- https://nnfs.io/neural_network_animations\n- https://en.wikipedia.org/wiki/Neural_network\n- https://www.youtube.com/user/sentdex\n- https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi\n- https://machinelearning.wtf/acronyms/\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhanzopgp%2Fneuralnetworkfromscratch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhanzopgp%2Fneuralnetworkfromscratch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhanzopgp%2Fneuralnetworkfromscratch/lists"}