---
# System prepended metadata

title: <b>Convolutional Neural Networks</b>

---

# <b>Convolutional Neural Networks</b>

## Projects for Winter

- Visualizing the <i>feature maps</i> of a convolutional neural network for image recognition.
    - ### Resources
        - https://christophm.github.io/interpretable-ml-book/
        - https://medium.com/dataseries/visualizing-the-feature-maps-and-filters-by-convolutional-neural-networks-e1462340518e
        - Back to basics http://neuralnetworksanddeeplearning.com
        - Advanced Convolutional Neural Networks https://www.deeplearningbook.org/contents/convnets.html
        - Visualizing feature maps https://openaccess.thecvf.com/content/ICCV2021W/CVPPA/papers/Mostafa_Visualizing_Feature_Maps_for_Model_Selection_in_Convolutional_Neural_Networks_ICCVW_2021_paper.pdf
        - https://arxiv.org/pdf/1311.2901.pdf

- Music generation using LSTM models.
- Deep learning networks with weighted feedback loops and optimized weight training
    - ### Resources
        - https://proceedings.neurips.cc/paper/2014/file/19de10adbaa1b2ee13f77f679fa1483a-Paper.pdf
        - Gated Feedback Recurrent Neural Networks http://proceedings.mlr.press/v37/chung15.pdf


- Hyperparamter tuning for combined L1, L2 and dropout regularization along with gradient descent optimization
    - ### Resources
        - Gradient Descent optimizations https://ruder.io/optimizing-gradient-descent/
- Finding global minimum for deep neural networks fore small training samples
    - ### Resources
        - "Gradient Descent finds global Minima for non-convex functions??" 
        https://arxiv.org/pdf/1811.03804.pdf
        
## Building a basic CNN

- https://www.kaggle.com/prantaneeldebnath/cnn-pytorch/edit
- https://blog.paperspace.com/writing-cnns-from-scratch-in-pytorch/