Public profile
@sakibbai
Learning maps and explanations shared with the Summlit community.
- Maps
- 3
- Explanations
- 10
- Learners
- 1
- Helpful
- 0
Public maps
3- Open →
Attention is All You Need
Not ADHD
Literature quest centered on Attention is All You Need.
11 concepts · 6 explanations - Open →
LoRA: Low-Rank Adaptation of Large Language Models
Lora
Literature quest centered on LoRA: Low-Rank Adaptation of Large Language Models.
9 concepts · 4 explanations - Open →
TinySV: Speaker Verification in TinyML with On-device Learning
Quest: TinySV: Speaker Verification in TinyML with On-device Learning
Literature quest centered on TinySV: Speaker Verification in TinyML with On-device Learning.
10 concepts · 1 learner
Explanations
10foundational
Linear Algebra Basics
The study of vectors, vector spaces, matrix operations, and dot products used to represent and transform data.
3 readsfoundational
Softmax Function
An activation function that normalizes a vector of real numbers into a probability distribution summing to one.
Published explanationfoundational
Word Embeddings
Dense vector representations of discrete tokens where geometric distance reflects semantic similarity.
Published explanationcore
Residual Connections
A network design where a layer's input is added directly to its output, creating shortcut paths for gradient flow.
Published explanationcore
Dot-Product Attention
An attention mechanism computing alignment scores by taking the scalar product of query and key vectors.
Published explanationcore
Positional Encoding
A method of adding deterministic or learned order signal vectors to input embeddings.
Published explanationadvanced
Transformer Architecture
An encoder-decoder model architecture relying entirely on self-attention mechanisms, bypassing recurrence and convolutions.
Published explanationfoundational
Gradient Descent and Backpropagation
An optimization algorithm that calculates loss gradients via the chain rule to iteratively update neural network weights.
1 readscore
Intrinsic Dimensionality of Model Updates
The property stating that effective learning in high-dimensional parameter spaces can be captured within a much lower-dimensional subspace.
Published explanationfoundational
Matrix Rank and Low-Rank Decomposition
The measure of non-redundant dimensions in a matrix and its factorization into smaller matrices of reduced dimensionality.
Published explanation