https://github.com/roscibely/neural_networks
Repository for PEX0023 Neural Network subject/course on Computer Engineering - UFERSA 🧠
https://github.com/roscibely/neural_networks
cnn collaborate deep-learning github lstm machine-learning neural-network neural-networks python recurrent-neural-networks rnn
Last synced: 11 months ago
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Repository for PEX0023 Neural Network subject/course on Computer Engineering - UFERSA 🧠
- Host: GitHub
- URL: https://github.com/roscibely/neural_networks
- Owner: roscibely
- License: gpl-3.0
- Created: 2022-11-29T18:55:44.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-10-10T17:12:06.000Z (over 1 year ago)
- Last Synced: 2025-04-05T19:41:29.413Z (about 1 year ago)
- Topics: cnn, collaborate, deep-learning, github, lstm, machine-learning, neural-network, neural-networks, python, recurrent-neural-networks, rnn
- Language: Python
- Homepage:
- Size: 3.68 MB
- Stars: 14
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# Artificial Neural Network (ANN)
###### Professor: [Rosana Rego](https://github.com/roscibely)
### PEX0023 - REDES NEURAIS ARTIFICIAIS
#### Bacharelado em Engenharia de Computação - UFERSA
---
## Part 01: [Introduction to Neural Network](https://github.com/roscibely/neural_networks/tree/develop/unidadeI)
1. [Perceptron Network](https://github.com/roscibely/neural_networks/tree/develop/unidadeI/perceptron)
2. [Adaline Network](https://github.com/roscibely/neural_networks/blob/develop/unidadeI/adaline.py)
3. [Feedforward Multilayer Perceptron (MLP)](https://github.com/roscibely/neural_networks/tree/develop/unidadeI/mlp)
4. [Backpropagation](https://github.com/roscibely/neural_networks/blob/develop/unidadeI/backpropagation.md)
5. [Least mean squares (LMS)]()
6. [Metrics](https://github.com/roscibely/neural_networks/tree/develop/unidadeI/metricas)
7. [Feedforward Radial Basis Function (RBF)](https://github.com/roscibely/neural_networks/blob/develop/unidadeI/radial_basis_function.py)
### [Project I: AWS DeepRacer](https://github.com/roscibely/neural_networks/blob/main/unidadeI/racer.md)
---
## Part 02: _Deep Learning_
1. [_Deep Feedforward Networks_](https://github.com/roscibely/neural_networks/tree/develop/unidadeII)
- 1.1 [Regularização (L1, L2)](https://github.com/roscibely/neural_networks/tree/develop/unidadeII/regularizacao)
- 1.2 [_Early Stopping_](https://github.com/roscibely/neural_networks/tree/develop/unidadeII/otmizacao)
- 1.3 [_Dropout_](https://github.com/roscibely/neural_networks/blob/main/unidadeII/otmizacao/dropout.md)
2. [_Recurrent neural network_ (RNN)](https://github.com/roscibely/neural_networks/tree/develop/unidadeII/rnn)
---
## Part 03
1. [_Long short-term memory_ (LSTM)](https://github.com/roscibely/neural_networks/blob/develop/unidadeII/rnn/lstm.md)
2. [_Gated Recurrent Unit_ (GRU)](https://github.com/roscibely/neural_networks/blob/develop/unidadeII/rnn/gru.md)
3. [_Convolutional neural network_ (CNN)](https://github.com/roscibely/neural_networks/tree/develop/unidadeII/cnn)
4. [Final Project](https://github.com/roscibely/neural_networks/blob/develop/projetos.md)
---
🤜 Dataquest Academic Program [Link](https://www.dataquest.io/course/deep-learning-fundamentals/)
---
### 🦾 Frameworks
* [TensorFlow](https://www.tensorflow.org/)
* [Keras](https://keras.io/)
* [PyTorch](https://pytorch.org/)
* [Caffe](http://caffe.berkeleyvision.org/)
* [Theano](http://deeplearning.net/software/theano/)
* [CNTK](https://docs.microsoft.com/en-us/cognitive-toolkit/)
* [MXNet](https://mxnet.apache.org/)
* [Chainer](https://chainer.org/)
* [Torch](http://torch.ch/)
* [PaddlePaddle](http://www.paddlepaddle.org/)
* [Apache SINGA](http://singa.apache.org/)
* [Apache SystemML](https://systemml.apache.org/)
---
### ⚙️ Sites legais
---
* [Neural Network Zoo](http://www.asimovinstitute.org/neural-network-zoo/)
* [Neural Network Playground](https://playground.tensorflow.org/)
* [Neural Network Visualizer](http://alexlenail.me/NN-SVG/index.html)
* [Neural Network Design](http://www.heatonresearch.com/aifh/vol1/v1_3_1_neural_network_design.html)
---
### Ferramentas implementadas com modelos redes neurais
* [Google Lens](https://lens.google.com/)
* [ChatGPT](https://openai.com/blog/chatgpt/)
* [DeepFace](https://research.fb.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/)
* [DeepDream](https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html)
* [DeepFake](https://www.youtube.com/watch?v=QH9t00Tg0EA)
* [DeepText](https://deep-text.readthedocs.io/en/latest/)
### Banco de datasets
* [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php)
* [Kaggle Datasets](https://www.kaggle.com/datasets)
* [Google Dataset Search](https://toolbox.google.com/datasetsearch)
### Livros
* 📚 [Deep Learning](http://www.deeplearningbook.org/) - Ian Goodfellow, Yoshua Bengio, Aaron Courville
* 📚 [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) - Michael Nielsen
* 📚 [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python) - François Chollet
* 📚 [Deep Learning for Coders with fastai and PyTorch](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527) - Jeremy Howard, Sylvain Gugger
* 📚 [Deep Learning with Keras](https://www.amazon.com/Deep-Learning-Keras-Powerful-Python/dp/178646294X) - Antonio Gulli, Sujit Pal
* 📚 [Deep Learning with PyTorch](https://www.amazon.com/Deep-Learning-PyTorch-Applications-Production/dp/1491989386) - Eli Stevens, Luca Antiga, Thomas Viehmann
* 📚 [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
* 📚 [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
* 📚 [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
UFERSA - Campus Pau dos Ferros