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Deep Learning

Before starting to learn Deep Learning, make sure to first learn basics of Machine Learning

ANN basics Part 1 ANN basics Part 2 ANN basics Part 3 ANN basics Part 4 Advanced ANN Part 1 Advanced ANN Part 2 Advanced ANN Part 3 Advanced ANN Part 4 Advanced ANN Playlist Advanced ANN Part 5 Advanced ANN Part 6

Boosting Algorithms Part 1 Boosting Algorithms Part 2 Boosting Algorithms Part 3 Boosting Algorithms Part 4

Basic CNN Model Part 1 Basic CNN Model Part 2 Basic CNN Model Part 3 Basic CNN Model Part 4 Backprop in CNN

CNN Architectures Part 1

Autoencoders Part 1 GANs Part 1

Practical Considerations Part 1

RNN & LSTM Theory Part 1

RNN & LSTM Practical Part 1

Transformer Architecture and LLMs Part 1

LLM Pre-Training Part 1 LLM Pre-Training Part 2 LLM Pre-Training Part 3 LLM Pre-Training Part 4 LLM Pre-Training Part 5

Fine Tuning LLMs Part 1 Fine Tuning LLMs Part 2

RAG Part 1 RAG Part 2

Image Captioning, Text to Image Part 1 Image Captioning, Text to Image Part 2

Deployment Part 1

RNN & LSTM Theory Part 1

What You Will Learn

RNN and LSTM theory

Content Covered

Covers the theoretical concepts of RNNs and LSTMs.

Resources

Understanding LSTMs (Colah's Blog)
The Unreasonable Effectiveness of RNNs (Karpathy)
WCUNPb-5EYI video
Exploring LSTMs (Echen blog)
Dropout in RNNS
RNN Regularization (arxiv)
RNN Regularization (Stack Overflow)

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