Skip to main content
grasp.study
About
Explore
Pricing
Log in
Try it for free
Back to course
👋
Welcome to a Grasp lesson
Create a free account
to keep studying and create your own study path.
Elite ML Engineer from Day 1
·
Module 1
Foundations of Sequence Modeling: From RNNs to Attention
1
From N-grams to Neural Language Models
Trace the evolution from statistical n-gram models to neural language models.
2
Word Embeddings: Capturing Semantic Meaning
Implement a simple word embedding layer and explain its role in capturing semantics.
3
Sequence-to-Sequence Architecture with RNNs
Describe the sequence-to-sequence architecture and its encoder-decoder paradigm using RNNs.
4
Understanding Vanishing and Exploding Gradients in RNNs
Explain the vanishing and exploding gradient problems in recurrent neural networks.
5
LSTMs and GRUs: Solving RNN Gradient Problems
Describe how LSTMs and GRUs were developed to mitigate gradient flow issues in RNNs.
6
Limitations of Recurrent Architectures
Explain the remaining limitations of recurrent architectures, such as the sequential processing bottleneck.
7
Attention and the Sequential Bottleneck
Describe how the attention mechanism addresses the sequential processing bottleneck.
8
RNN vs. Attention: Computational Complexity
Compare the computational complexity of recurrent versus attention-based architectures.
Previous module
Next module
Transformer Architecture Fundamentals