{"id":21259835,"url":"https://github.com/roscibely/neural_networks","last_synced_at":"2025-07-25T00:35:32.096Z","repository":{"id":128717680,"uuid":"572196734","full_name":"roscibely/neural_networks","owner":"roscibely","description":"Repository for PEX0023 Neural Network subject/course on Computer Engineering - UFERSA  🧠","archived":false,"fork":false,"pushed_at":"2024-10-10T17:12:06.000Z","size":3862,"stargazers_count":14,"open_issues_count":1,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-05T19:41:29.413Z","etag":null,"topics":["cnn","collaborate","deep-learning","github","lstm","machine-learning","neural-network","neural-networks","python","recurrent-neural-networks","rnn"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/roscibely.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","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":"2022-11-29T18:55:44.000Z","updated_at":"2025-01-02T12:20:30.000Z","dependencies_parsed_at":"2024-09-07T19:28:26.217Z","dependency_job_id":"fd53c856-304f-4ec8-9760-e84ca6314ac1","html_url":"https://github.com/roscibely/neural_networks","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/roscibely/neural_networks","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/roscibely%2Fneural_networks","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/roscibely%2Fneural_networks/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/roscibely%2Fneural_networks/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/roscibely%2Fneural_networks/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/roscibely","download_url":"https://codeload.github.com/roscibely/neural_networks/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/roscibely%2Fneural_networks/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":264721284,"owners_count":23653911,"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","collaborate","deep-learning","github","lstm","machine-learning","neural-network","neural-networks","python","recurrent-neural-networks","rnn"],"created_at":"2024-11-21T04:15:30.116Z","updated_at":"2025-07-11T03:30:47.116Z","avatar_url":"https://github.com/roscibely.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv\u003e\n\n  \u003cimg src=\"https://github.com/roscibely/algorithms-and-data-structure/blob/main/root/Ufersa.png\" width=\"70\" height=\"100\"\u003e\n   \u003cimg src=\"https://josecastillolema.github.io/assets/images/posts/2020-07-09-aws-academy/01.png\" width=\"250\" height=\"100\"\u003e\n\u003c/div\u003e\n\n\n#  Artificial Neural Network (ANN)\n\n###### Professor: [Rosana Rego](https://github.com/roscibely)\n\n### PEX0023 - REDES NEURAIS ARTIFICIAIS\n#### Bacharelado em Engenharia de Computação - UFERSA\n---\n## Part 01: [Introduction to Neural Network](https://github.com/roscibely/neural_networks/tree/develop/unidadeI) \n1. [Perceptron Network](https://github.com/roscibely/neural_networks/tree/develop/unidadeI/perceptron)\n2. [Adaline Network](https://github.com/roscibely/neural_networks/blob/develop/unidadeI/adaline.py)\n3. [Feedforward Multilayer Perceptron (MLP)](https://github.com/roscibely/neural_networks/tree/develop/unidadeI/mlp)\n4. [Backpropagation](https://github.com/roscibely/neural_networks/blob/develop/unidadeI/backpropagation.md)\n5. [Least mean squares (LMS)]()\n6. [Metrics](https://github.com/roscibely/neural_networks/tree/develop/unidadeI/metricas) \n7. [Feedforward Radial Basis Function (RBF)](https://github.com/roscibely/neural_networks/blob/develop/unidadeI/radial_basis_function.py)\n\n### [Project I: AWS DeepRacer](https://github.com/roscibely/neural_networks/blob/main/unidadeI/racer.md)\n---\n## Part 02: _Deep Learning_ \n1. [_Deep Feedforward Networks_](https://github.com/roscibely/neural_networks/tree/develop/unidadeII)\n- 1.1 [Regularização (L1, L2)](https://github.com/roscibely/neural_networks/tree/develop/unidadeII/regularizacao)\n- 1.2 [_Early Stopping_](https://github.com/roscibely/neural_networks/tree/develop/unidadeII/otmizacao)\n- 1.3 [_Dropout_](https://github.com/roscibely/neural_networks/blob/main/unidadeII/otmizacao/dropout.md) \n2. [_Recurrent neural network_ (RNN)](https://github.com/roscibely/neural_networks/tree/develop/unidadeII/rnn)\n---\n## Part 03\n1. [_Long short-term memory_ (LSTM)](https://github.com/roscibely/neural_networks/blob/develop/unidadeII/rnn/lstm.md)\n2. [_Gated Recurrent Unit_ (GRU)](https://github.com/roscibely/neural_networks/blob/develop/unidadeII/rnn/gru.md)\n3. [_Convolutional neural network_ (CNN)](https://github.com/roscibely/neural_networks/tree/develop/unidadeII/cnn)\n4. [Final Project](https://github.com/roscibely/neural_networks/blob/develop/projetos.md)\n---\n🤜 Dataquest Academic Program [Link](https://www.dataquest.io/course/deep-learning-fundamentals/)\n---\n### 🦾 Frameworks \n\n* [TensorFlow](https://www.tensorflow.org/)\n* [Keras](https://keras.io/)\n* [PyTorch](https://pytorch.org/)\n* [Caffe](http://caffe.berkeleyvision.org/)\n* [Theano](http://deeplearning.net/software/theano/)\n* [CNTK](https://docs.microsoft.com/en-us/cognitive-toolkit/)\n* [MXNet](https://mxnet.apache.org/)\n* [Chainer](https://chainer.org/)\n* [Torch](http://torch.ch/)\n* [PaddlePaddle](http://www.paddlepaddle.org/)\n* [Apache SINGA](http://singa.apache.org/)\n* [Apache SystemML](https://systemml.apache.org/)\n---\n### ⚙️ Sites legais \n---\n* [Neural Network Zoo](http://www.asimovinstitute.org/neural-network-zoo/)\n* [Neural Network Playground](https://playground.tensorflow.org/)\n* [Neural Network Visualizer](http://alexlenail.me/NN-SVG/index.html)\n* [Neural Network Design](http://www.heatonresearch.com/aifh/vol1/v1_3_1_neural_network_design.html)\n---\n### Ferramentas implementadas com modelos redes neurais\n\n* [Google Lens](https://lens.google.com/)\n* [ChatGPT](https://openai.com/blog/chatgpt/)\n* [DeepFace](https://research.fb.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/)\n* [DeepDream](https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html)\n* [DeepFake](https://www.youtube.com/watch?v=QH9t00Tg0EA)\n* [DeepText](https://deep-text.readthedocs.io/en/latest/)\n\n\n\n\n### Banco de datasets\n\n* [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php)\n* [Kaggle Datasets](https://www.kaggle.com/datasets)\n* [Google Dataset Search](https://toolbox.google.com/datasetsearch)\n\n\n### Livros \n\n* 📚  [Deep Learning](http://www.deeplearningbook.org/) - Ian Goodfellow, Yoshua Bengio, Aaron Courville\n* 📚  [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) - Michael Nielsen\n* 📚  [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python) - François Chollet\n* 📚  [Deep Learning for Coders with fastai and PyTorch](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527) - Jeremy Howard, Sylvain Gugger\n* 📚  [Deep Learning with Keras](https://www.amazon.com/Deep-Learning-Keras-Powerful-Python/dp/178646294X) - Antonio Gulli, Sujit Pal\n* 📚  [Deep Learning with PyTorch](https://www.amazon.com/Deep-Learning-PyTorch-Applications-Production/dp/1491989386) - Eli Stevens, Luca Antiga, Thomas Viehmann\n* 📚  [Deep Learning with TensorFlow](https://www.amazon.com/Deep-Learning-TensorFlow-Scalable-Implementations/dp/1491989386) - Tom Hope, Bharath Ramsundar, Brian McMahan, Arvind Ramanathan, Quoc V. Le\n* 📚  [Deep Learning with CNTK](https://www.amazon.com/Deep-Learning-CNTK-Scalable-Implementations/dp/1491989386) - Tom Hope, Bharath Ramsundar, Brian McMahan, Arvind Ramanathan, Quoc V. Le\n* 📚  [Deep Learning with Apache MXNet](https://www.amazon.com/Deep-Learning-Apache-MXNet-Scalable/dp/1491989386) - Thomas Viehmann, Thomas Viehmann, Thomas Viehmann, Thomas Viehmann, Thomas Viehmann\n\n\n\n\n\n\n\u003cdiv\u003e\n  \u003cimg src=\"https://github.com/roscibely/algorithms-and-data-structure/blob/develop/root/ufersa.jpg\" width=\"700\" height=\"250\"\u003e\n\u003c/div\u003e\n\u003ci\u003eUFERSA - Campus Pau dos Ferros\u003c/i\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Froscibely%2Fneural_networks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Froscibely%2Fneural_networks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Froscibely%2Fneural_networks/lists"}