Bibliography (18):

1. ImageNet: A Large-Scale Hierarchical Image Database

2. https://karpathy.medium.com/yes-you-should-understand-backprop-e2f06eab496b

3. ​ ‘end-to-end’ directory

4. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution (ASPP), and Fully Connected CRFs

5. Feature Pyramid Networks for Object Detection

6. Deep Residual Learning for Image Recognition

7. Grokking: Generalization Beyond Overfitting On Small Algorithmic Datasets

8. Wikipedia Bibliography:

   1. Backpropagation

   2. https://en.wikipedia.org/wiki/Backprop  :

      https://en.wikipedia.org/wiki/Backprop

   3. Stochastic gradient descent

   4. https://en.wikipedia.org/wiki/Batch_norm  :

      https://en.wikipedia.org/wiki/Batch_norm

   5. https://en.wikipedia.org/wiki/RNNs  :

      https://en.wikipedia.org/wiki/RNNs

   6. Reinforcement learning

   7. Data augmentation

   8. Autoregressive model

   9. https://en.wikipedia.org/wiki/Stochastic_gradient_descent#Adam  :

      https://en.wikipedia.org/wiki/Stochastic_gradient_descent#Adam

   10. Recurrent neural network

   11. https://en.wikipedia.org/wiki/Early_stopping  :

       https://en.wikipedia.org/wiki/Early_stopping