To learn about Large Language Models (LLMs) for AI/ML ( AI generated)
To learn about Large Language Models (LLMs) for AI/ML , follow these steps: 1. Prerequisites Before diving into LLMs, you should have a strong foundation in: Python (libraries like NumPy, Pandas, Matplotlib) Machine Learning (scikit-learn, linear algebra, optimization) Deep Learning (PyTorch or TensorFlow) Natural Language Processing (NLP) (Tokenization, embeddings, transformers) 2. Understanding LLM Fundamentals Learn about transformers , the core architecture behind LLMs (e.g., BERT, GPT). Study self-attention, positional encoding, and attention mechanisms . Read the original "Attention Is All You Need" paper by Vaswani et al. (2017). 3. Hands-on with LLMs Use Hugging Face Transformers to load and fine-tune pre-trained models: from transformers import pipeline generator = pipeline("text-generation", model="gpt2") print(generator("Once upon a time", max_length=50)) Train or fine-tune models using Google Colab , PyTorch ,...