Technical insights, tutorials, and stories from the world of technology
Master token embeddings from scratch! Learn why random token IDs fail, how vectors capture semantic meaning (King - Man + Woman = Queen!), build embedding layers in PyTorch, understand Word2Vec, implement lookup tables, and prepare embeddings for GPT training. Discover why embeddings are the secret sauce of LLMs.
Read moreLearn how to create input-target pairs for LLM training. Master context windows, sliding windows, batch processing, and PyTorch DataLoaders. Understand auto-regressive training, why next-word prediction needs special data preparation, and implement efficient data pipelines from scratch.
Read moreMaster Byte Pair Encoding (BPE) - the tokenization algorithm used in GPT-2, GPT-3, and ChatGPT. Learn why it's superior to word and character tokenization, build BPE from scratch, understand subword tokenization, and use tiktoken library. Complete Python implementation with real examples.
Read moreStart coding! Learn tokenization from scratch - the first step in building LLMs. Break text into tokens, create vocabulary, build encoder/decoder, handle special tokens (UNK, EOS), and understand why GPT uses Byte Pair Encoding. Complete Python implementation included.
Read moreYour complete roadmap for building LLMs from scratch. Learn the 3-stage process: Data Preparation & Architecture (Stage 1), Pre-training (Stage 2), and Fine-tuning & Deployment (Stage 3). Understand tokenization, attention mechanisms, training loops, and building real applications like spam classifiers and chatbots.
Read moreDeep dive into GPT architecture. Learn the evolution from GPT-1 to GPT-4, zero-shot vs few-shot learning, auto-regressive models, unsupervised learning, emergent behavior, and why training costs $4.6 million - explained for absolute beginners.
Read moreDiscover how Transformers revolutionized AI. Learn the encoder-decoder architecture, self-attention mechanism, tokenization, embeddings, and differences between GPT and BERT - all explained with simple examples for absolute beginners.
Read moreLearn the two critical stages of building Large Language Models: Pre-training and Fine-tuning. Understand why ChatGPT works so well, how companies customize LLMs, and the complete lifecycle from raw data to production-ready AI.
Read moreUnderstand Large Language Models from absolute basics. Learn what makes them 'large', how they differ from old NLP models, their secret sauce (Transformers), and real-world applications with simple examples.
Read more
