Master Large Language Modelsin Malayalam & Build Production AI
Learn transformer architecture, RoPE positional encoding, Q-LoRA fine-tuning, GGUF quantization, and production deployment in Malayalam through comprehensive video lessons and hands-on Colab notebooks. All for a one-time price of 1000 Rs!
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Complete LLM Mastery Program
Everything you need to understand, build, and deploy Large Language Models in production environments.
Malayalam LLM Course
Access 12+ HD video lessons in Malayalam covering transformer architecture, RoPE encoding, attention mechanisms, Q-LoRA fine-tuning, and RAG systems.
Modern Techniques
Learn cutting-edge methods including Q-LoRA fine-tuning, GGUF quantization, AdamW optimization, and efficient model compression techniques.
Hands-on Implementation
Practice with 12+ Google Colab notebooks implementing RoPE, self-attention, feed-forward networks, and production-ready fine-tuning.
Production Deployment
Deploy quantized models using vLLM, FastAPI with JWT authentication, monitoring, and GGUF format optimization.
Master the Core of Large Language Models
Our comprehensive Malayalam LLM curriculum takes you from neural network foundations to modern techniques like RoPE, Q-LoRA, and GGUF quantization.
Module 1: Neural Networks & Optimization
Python Fundamentals
Deep dive into Python, covering advanced data structures, functional programming
Neural Network Basics & Backpropagation
Understand the core principles of neural networks, including backpropagation and gradient descent
AdamW Optimization
Master AdamW optimization technique that improves upon Adam by addressing weight decay for better generalization
Module 2: Transformer Architecture Fundamentals
Tokenization & Embeddings
Explore Byte Pair Encoding (BPE) for tokenization and embedding layers for converting tokens to vectors
Positional Encoding & Self-Attention
Learn how transformers maintain sequence order with positional encoding and process relationship between tokens with self-attention
Module 3: Transformer Components & Model Architecture
Feed-Forward Networks & Layer Normalization
Understand how feed-forward networks process tokens independently and how layer normalization stabilizes training
Final Layer & Complete Architecture
Study how the final layer generates outputs and how all components work together in models like Llama 2
Module 4: Model Fine-tuning & Optimization
Low-Rank Adaptation (LoRA) & QLoRA
Master efficient fine-tuning techniques using LoRA and quantized LoRA for adapting large models with limited resources
Stochastic Gradient Descent & Model Learning
Explore how AI models learn through examples and optimize parameters using stochastic gradient descent
Module 5: Advanced Deployment & Applications
Retrieval Augmented Generation (RAG)
Implement RAG systems that enhance LLM outputs by retrieving relevant information from external knowledge bases
vLLM Deployment & Production Setup
Deploy fine-tuned models in production using vLLM with FastAPI, JWT authentication and monitoring
...and more! Access all modules and practical Colab notebooks instantly with your Premium membership.
Transform Your AI Capabilities with Premium Access
Achieve practical skills, build portfolio projects, and leverage advanced AI tools.
Master Modern LLM Architecture
Understand RoPE positional encoding, multi-head attention, layer normalization, and transformer components in Malayalam.
Q-LoRA & GGUF Quantization
Master parameter-efficient Q-LoRA fine-tuning, 4-bit quantization, and GGUF format optimization for memory efficiency.
Production LLM Deployment
Deploy quantized models using vLLM with GGUF format, implement FastAPI endpoints, and JWT authentication.
Advanced RAG & Fine-tuning
Build RAG systems with vector databases and implement advanced fine-tuning techniques for domain adaptation.
Frequently Asked Questions
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