From NLP to LLMs – A Hands-On Guide for Beginners

Explore natural language processing, its traditional limits, and the transformer revolution, including the architecture of transformers and a comparison of Bert and GPT.

Explore natural language processing and the limitations of traditional approaches, then learn core concepts like tokenization, stemming, lemmatization, stopwords, and pos tagging.

Trace the evolution of language models from rule-based systems to large language models, highlighting Eliza, Shrdlu, n-gram models, word embeddings, transformers, GPT, BERT, and multimodal llms.

Explore why transformers revolutionized AI with self-attention, multi-head attention, and positional encodings, enabling parallel processing and long-range context across NLP, vision, and biology, from BERT and GPT to AlphaFold.

Explore transformer architecture, including self-attention, positional encodings, and multi-head attention, with encoder and decoder roles, embeddings, residuals, and layer normalization, plus Bert and GPT variants and related challenges.

Compare Bert and the generative pre-trained transformer, highlighting bidirectional masked language modeling and next-token prediction, their training differences, and their strengths in natural language understanding versus text generation.

Explore pre-trained LLMs by examining models like Llama, Bert, and GPT, compare open source and proprietary options, and distinguish training from fine-tuning, with a Hugging Face demo.

Explore popular LLMs like Llama, BERT, and GPT, with Llama’s decoder-only, open-source architecture, public data training, and multilingual capabilities, contrasting encoder versus decoder designs and practical limitations.

Compare open source and proprietary LLMs, highlighting access to architecture, weights, and training data. Explain licensing, local deployment, and customization with examples like llama, Mistral, Falcon versus GPT, Claude, Gemini.

Compare fine-tuning pre-trained language models for specific tasks with training large language models from scratch, illustrated by fine-tuning llama two for physics content and the transformer-based training process.

Demonstrate hugging face through a hands-on workflow: install transformers and torch, download and load Distilbert base uncased locally, tokenize inputs, and run a sentiment analysis pipeline.

Explore prompt engineering and design, zero-shot and few-shot learning, and best practices for prompts, plus integrating models with real-world apps and deployment cost and latency optimization.

Discover prompt engineering: design and refine input prompts to maximize ai accuracy, control tone and style, and apply to customer support, content creation, coding, data analysis, and education.

Learn how to craft specific, context-rich prompts with clear formatting and examples. Explore prompt anatomy, advanced techniques, and how good design avoids unclear or biased AI responses.

Compare zero-shot and few-shot learning to see how models generalize with limited labeled data. Utilize zero-shot with semantic attributes and textual descriptions; few-shot adapts with a small labeled support set.

Design prompts with clear keywords, precise instructions, defined constraints, and well-structured formats to guide AI, then test with zero-shot, few-shot, and chain-of-thought and knowledge-injection techniques.

Deploy a llama model and integrate it with an application using the Olama open-source tool. Run locally via the http API and customize the model with Python code.

Balance deployment cost and latency by applying strategies like rightsizing compute, autoscaling, and serverless services. Leverage CDNs, multi-region deployments, caching, and cost-monitoring tools to optimize performance while controlling expenses.

Explore the risks and limitations of large language models, including bias, toxicity, and fairness, and outline mitigation strategies to improve safety and the future scope of LLMs.

Explore the risks and limitations of LLMs, including hallucinations, data privacy concerns, bias, and toxicity, along with computational demands and domain knowledge limits.

Explore bias, toxicity, and fairness challenges in large language models and learn how training data from the internet shapes outputs and how risk mitigation improves safety and reliability.

Mitigate risks in llms by improving training data quality to reduce hallucinations and bias, and apply robust evaluation, human oversight, explainable AI, and differential privacy to boost safety.

Explore how future llms tailor content, enable natural, emotionally intelligent conversations, and deliver domain-specific insights across healthcare, finance, and law, with real-time adaptation and ethical safeguards.

Create a local llm agent with a llama two 7b model in a python flask app by setting up a virtual environment, dependencies, and a chat interface.

Explore the journey from natural language processing to large language models, covering transformer architectures, pre-trained versus fine-tuned models, prompt engineering, and responsible deployment in real world applications.