nihita-ujjual / deep-learning-notes

My CS231n lecture notes

Deep Learning Study Notes

[Sutdy Notes PDF]

My Deep Learning study notes.

Sources:

• CS231n course (main)
• the Deep Learning book
• some other random sources.

All credits go to L. Fei-Fei, A. Karpathy, J.Johnson teachers of the CS231n course. Thank you for this amazing course!!

Full Document

Full study notes pdf.

Individual Chapters

If you chose individidual chapters here is the list (are you sure you do not prefer the FULL DOCUMENT?):

1. Data
   1. Data Preprocessing
   2. Data Augmentation and Transfer Learning
2. Learning
   1. Neural Network
   2. Parameters Initialization
   3. Activation Function
   4. Loss function
   5. Backpropagation
   6. Parameters Update
   7. Dropout
   8. Hyper-Parameters selection and babysitting
   9. Other definitions
   10. Tricks
   11. Visualization
3. Layers
   1. Input Layer
   2. Convolutional layer
   3. Pooling layer
   4. Batch Normalization layer
   5. Upsampling Layer (“Transposed Convolution”)
   6. Fully Connected Layer
   7. Highway Layer
4. Networks
   1. Recurrent Neural Networks (RNN)
   2. Autoencoders
   3. Generative Adversarial Nets
   4. Region Based CNN (R-CNN)
   5. YOLO
   6. RNN ConvNet
   7. Attention Models
   8. Spatial Transformer Networks
   9. Famous Networks
5. Applications
6. Bibliography

Contributions

More than happy to accept contributions

Acknowledgements & Credits

All credits go to L. Fei-Fei, A. Karpathy, J.Johnson teachers of the CS231n course. Thank you for this amazing course!!