Generative AI & LLMs: Foundations to Hands-on Development

Explore how language ai processes and generates human language, from data and neural networks to transformer models, and examine challenges like bias, context, and ethics.

Explore the shift from bag of words to dense word2vec embeddings, and learn how skip gram, cbow, tf-idf, semantic similarity, and relevance drive language understanding.

Attention mechanisms empower language models to assign dynamic weights to input words, with self-attention and transformer architectures enabling parallel processing and long-range dependencies for translation, summarization, and question answering.

Compare representation models and generative models in LLMs, including BERT and GPT, and see how they encode context or generate fluent text from prompts.

Discover how large language models enable education, healthcare, and customer service, boosting learning, research, and multilingual communication. Examine ethical considerations such as bias, privacy, misinformation, accountability, and environmental impact.

Explore how the future of language AI balances efficiency, ethics, and transparency, reduces resource use, and advances multimodal capabilities across text, images, audio, and video.

Learn to build a generative ai chatbot in python using gpt-2 on Google Colab with Hugging Face transformers and PyTorch, loading a pre-trained model and tokenizer for interactive chat.

Explore building a generative ai chatbot with GPT-2 in Google Colab, using Hugging Face Transformers and PyTorch to load the model, tokenize input, and generate responses.

Master prompt engineering for generative AI by crafting precise, contextually relevant inputs to guide text, images, and code outputs, balancing model strengths and limitations with linguistic nuance and ethical considerations.