AI Researcher — Channel Summaries
AI-powered summaries of 10 videos about AI Researcher.
10 summaries
KAN Practical Implementation (Kolmogorov–Arnold Networks Algorithm)
Kolmogorov–Arnold Networks (KAN) are put to work on a heart-disease classification task using a practical Python pipeline: load a Kaggle dataset,...
KAN: Kolmogorov–Arnold Networks Paper Explained
Kolmogorov–Arnold Networks (KAN) are presented as a multi-layer neural network alternative designed to represent functions with fixed activation...
Multilayer Perceptron (MLP) Neural Networks: Introduction and Implementation
Multilayer perceptron (MLP) neural networks are a foundational feedforward model built to learn nonlinear patterns for prediction tasks like...
How to Integrate RAG - Retrieval Augmented Generation into a LLM? (Practical Demo)
Retrieval-Augmented Generation (RAG) is presented as a practical way to make a language model answer questions using external, user-provided sources...
Run any LLMs locally: Ollama | LM Studio | GPT4All | WebUI | HuggingFace Transformers
Running large language models locally boils down to one trade-off: keeping data on-device and gaining control over models and prompts, while paying...
Prompt Engineering: Zero-shot, One-shot, Few-shot Techniques Explained (Practical Implementation)
Prompting lets a pre-trained language model follow tasks using only instructions and examples—no weight updates—so performance can be improved by...
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Large language models built with ultra-low-precision weights—specifically BitNet B1.58, which uses only three weight values (-1, 0, +1)—are showing a...
History of Large Language Models (LLMs) | From 1940 to 2023
Large language models didn’t arrive fully formed; they emerged through a sequence of breakthroughs that shifted computing from hand-written language...
Large Language Model Fine-Tuning with PEFT and LoRA (Practical Implementation)
Fine-tuning a large language model with LoRA (Low-Rank Adaptation) and PEFT is presented as a practical way to specialize models for tasks like...
Understanding Transformer Architecture of LLM: Attention Is All You Need
Transformer architecture became a turning point for language modeling because it replaces sequential processing with self-attention, enabling...