I watched and am summarizing the lecture content on self-attention and Transformer architectures from CS224N.
Neural Architectures and Their Properties
Progress in NLP has been driven by general-purpose techniques like Hidden Markov Models, Conditional Random Fields, RNNs, CNNs, and Support Vector Machines. The limitations of recurrent models, especially in parallelization and capturing long-term dependencies, motivated the development of self-attention and Transformer architectures.
Limitations of RNNs
-
Parallelization Issues: RNNs process sequentially, where each step depends on the previous one. This dependency hinders efficient parallel computation. For instance, the hidden state \( h_t \)...
Read more...
Neural Machine Translation, Seq2Seq, and Attention
This summary captures my understanding of the lecture on neural machine translation, focusing on Seq2Seq models and the attention mechanism.
Neural Machine Translation (NMT)
NMT aims to produce sequential outputs like translations, conversations, or summaries. Traditional approaches relied on separate models for translation and language representation, which struggled with syntax and long-term dependencies. Seq2Seq models revolutionized this field by using neural networks to handle these limitations.
Sequence-to-Sequence (Seq2Seq)
Seq2Seq is an end-to-end framework using two recurrent neural networks (RNNs):
- Encoder: Processes the input sequence into a fixed-size context vector \(C\),...
Read more...
Dependency Parsing
I watched and am summarizing the content of Module 3 - Dependency Parsing. The focus is on the concepts and techniques involved in parsing the syntactic dependencies of sentences.
Dependency Grammar and Structure
In dependency grammar, parse trees represent the syntactic structure of sentences. Unlike constituency structures, which use nested constituents, dependency structures emphasize binary, asymmetric relationships between words. These relationships are called dependencies. Dependencies typically form tree structures where:
- Head (Governor): The superior word in the dependency relationship.
- Dependent (Modifier): The subordinate word.
Example
A dependency tree represents a sentence like:
...
Read more...
NLP with Deep Learning: Lecture 1 - Introduction & Word Vectors
These notes cover key concepts from XCS224N, including human language, word meaning, and the word2vec algorithm, along with foundational math models in NLP.
Human Language and Word Meaning
Human language is inherently complex due to its social nature. People interpret and construct language based on context, making it challenging for computers to understand and generate. Despite this complexity, deep learning has enabled impressive advancements in modeling language, specifically in representing word meaning using vectors.
Word2Vec Algorithm
One key breakthrough in NLP is the word2vec algorithm, which...
Read more...
The spelled-out intro to neural networks and backpropagation: building micrograd
I watched and am summarizing a lecture that walks through building an automatic differentiation engine and neural network from scratch, called Micrograd. The content covers how backpropagation works, how derivatives are computed, and how neural networks perform gradient-based optimization.
Neural Networks and Backpropagation
Neural networks are essentially functions that map inputs (data) to outputs (predictions). The key to training these networks lies in optimizing their parameters (weights and biases) so that the predictions match the target values as closely as possible. The process of tuning these weights relies...
Read more...
I watched and summarized the video Attention in Transformers, Visually Explained. The video breaks down the attention mechanism, a critical component of transformers, widely used in modern AI systems.
What is Attention?
Transformers, introduced in the paper Attention is All You Need, are designed to predict the next token in a sequence. The key innovation is the attention mechanism, which adjusts word embeddings based on the surrounding context. Initially, each token is associated with a high-dimensional vector, known as an embedding. But without attention, these embeddings lack context.
For example, the...
Read more...
Predictions on the Future of Large Language Models: Insights from Andrej Karpathy
In a recent conversation with Andrej Karpathy, a leading figure in AI, several fascinating predictions about the future of Large Language Models (LLMs) and AI technology were discussed. Here are some of the key takeaways:
1. Synthetic Data as the Future
Karpathy believes that synthetic data generation will be crucial for the development of future LLMs. As we near the limits of internet-sourced data, generating synthetic, diverse, and rich data will become the main way to push models forward. He warns, however, of “data collapse,” where...
Read more...
Dot Product: Key Insights
Numerical Definition
The dot product of two vectors is the sum of the products of their corresponding components:
\( \mathbf{v} \cdot \mathbf{w} = \sum_{i=1}^{n} v_i w_i \)
For example:
\( [1, 2] \cdot [3, 4] = 1 \cdot 3 + 2 \cdot 4 = 11 \)
Geometric Interpretation
The dot product can be seen as the projection of one vector onto another:
| [\mathbf{v} \cdot \mathbf{w} = | \mathbf{v} | | \mathbf{w} | \cos(\theta)] |
Where \( \theta \) is the angle between the vectors. It’s positive if they point in the...
Read more...