Deep Learning Mastry

[JyotsnaT]

Good understanding of deep learning architectures like Multi-Layer Perceptron, Recurrent Neural Networks (RNNs), Long Short Term Memory models (LSTMs), Gated Recurrent Units (GRUs), and Convolutional Neural Networks (CNNs).

• [ ] Notes on theoretical revision
  • [ ] CNN
  • [ ] RNN
• [ ] Have hands-on experience with at least one of the deep learning frameworks like Keras, Tensorflow, Pytorch, or FastAI. Be able to train deep learning models using any of the deep learning frameworks mentioned above.
  • [ ] Train Multi-Layer Perceptron on the tabular datasets.
  • [ ] #23
  • [ ] Build RNNs and CNNs for unstructured data, i.e., text and image.
• [ ] Knowledge of pretrained models for image data and their different types. For instance, know how to finetune them on the downstream tasks.
• [ ] Learn about language models and build them with LSTMs/GRUs.
• [ ] Gain knowledge of Attention Mechanisms and know the limitations of using LSTM for working with longer sequences.
• [ ] Understand the architectures of Autoencoders and GANs.

[JyotsnaT]

neural network practical aspects - https://karpathy.github.io/2019/04/25/recipe/

[JyotsnaT]

Building neural network from scratch - https://colab.research.google.com/drive/1LmWvkumWDymXJ7a4Qj5PLMo5CGCyFSij?usp=sharing