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AI/day03/1 - Convolutional Neural Network/1. Convolutional_Neural_Network.ipynb
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# ~ PoC AI Pool 2024 ~ | ||
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- ## Day 2: Neural Networks from Scratch | ||
- ### Module 1: Linear Regression | ||
- **folder:** [1 - Convolutional Neural Network](1%20-%20Convolutional%20Neural%20Network) | ||
- ### Module 2: Logistic Regression | ||
- **folder:** [2 - My nn Torch](2%20-%20My%20nn%20Torch) | ||
- ### Module 3: Deep Learning | ||
- **folder:** [3 - VAE-GAN](3%20-%20VAE-GAN) | ||
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--- | ||
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**Hooray : You've made it to AI !** | ||
On today's menu, we'll enter the wonderful world of machine learning with two major algorithms : Linear and Logistic Regression, followed by the modern approach of Deep Learning using PyTorch ! | ||
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> Here's a list of resources that we believe can be useful to follow along (and that we've ourselves used to learn these topics before being able to write the subjects): | ||
## Module 1 | ||
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- [CNN](https://towardsdatascience.com/convolutional-neural-networks-explained-9cc5188c4939) | ||
- [Custom nn module](https://pytorch.org/tutorials/beginner/nn_tutorial.html) | ||
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## Module 2 | ||
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- [nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) | ||
- [nn.Conv2d](https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html) | ||
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## Module 3 | ||
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- [VAE](https://towardsdatascience.com/understanding-variational-autoencoders-vaes-f70510919f73) | ||
- [GAN](https://towardsdatascience.com/gan-by-example-using-keras-on-tensorflow-backend-1a6d515a60d0) | ||
- [VAE-GAN](https://wandb.ai/shambhavicodes/vae-gan/reports/An-Introduction-to-VAE-GANs--VmlldzoxMTcxMjM5) |