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Understand How LLMs work behind the scene, checkout - https://www.youtube.com/playlist?list=PL2IsFZBGM_IEsVQ1ryZw-4l0XZI6VjX0D #genai #llm #ai
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Tutorials✅ How Transformers Work - Attention Explained Step by Step | Chapter 06
The video explains the Transformer architecture, detailing how it processes text input through tokenization, embedding, and a stack of Transformer blocks to generate the next token. It breaks down the attention mechanism, multi-head attention, and feed-forward layers within a Transformer block, highlighting the differences between encoders and decoders.
NewsHow LLMs Understand your Prompts: Tokenization & Embeddings | Chapter 05
The video explains how Large Language Models (LLMs) understand text by converting it into numerical representations through tokenization and embeddings. It demonstrates how text is broken into tokens, assigned unique IDs, and then transformed into dense vectors (embeddings) that capture semantic meaning and positional information for LLM processing.
TutorialsHow Large Language Models (LLMs) Work - Full Explanation | Chapter 04
Large Language Models (LLMs) are text-based neural networks trained on massive data to predict the next word (token), operating through tokenization, vector embeddings, and a transformer architecture. LLMs undergo pre-training, supervised fine-tuning, and reinforcement learning from human feedback to become helpful, safe, and aligned, with concepts like context length, knowledge cut-off, and hallucination defining their capabilities and limitations.
NewsHow Neural Network works | Weights and Bias #dataengineering #neuralnetworks #genai
A neural network's neuron processes input signals by assigning weights to each, reflecting its importance (e.g., monthly income has a high positive weight, outstanding debts a negative weight). These weighted inputs are summed with a bias, and the result is passed through an activation function to produce an output decision.
TutorialsNeural Networks Explained - How They Work & Are Trained | Chapter 03
This video explains how artificial neural networks (ANNs) work, detailing the components of a neuron (inputs, weights, bias, activation function) and how they form layers in a network. It also covers the training process, including forward propagation, loss calculation, and backpropagation using gradient descent to adjust weights and biases.
TutorialsMachine Learning Explained - END to END | Chapter 02
The video explains core machine learning concepts, including supervised, unsupervised, and reinforcement learning, along with the workflow for building and evaluating models. It details classification and regression models, their applications, and essential data preparation techniques like feature engineering and handling the curse of dimensionality.