Cursarium

Transformers

31 courses3.4M learners10 providers

Deep dive into the transformer architecture that revolutionized NLP and beyond, covering self-attention, BERT, GPT, and fine-tuning techniques.

AllSelf-AttentionBERTGPTFine-TuningHugging Face

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NLP - Natural Language Processing with Transformers in Python

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The Complete ChatGPT Guide: Learn Midjourney, ChatGPT & More

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Generative AI, LLMs - OpenAI API, LangChain, Hugging Face

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Finetuning Large Language Models

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Practical Deep Learning for Coders Part 2: Deep Learning Foundations to Stable Diffusion

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Diffusion Models Course

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Audio Course

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Natural Language Processing Nanodegree

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Working with the OpenAI API

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Prompt Engineering Specialization

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Open Source Models with Hugging Face

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Quantization Fundamentals with Hugging Face

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Building AI Agents with Hugging Face

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Prompt Engineering: How to Talk to the AIs

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Generative AI: Prompt Engineering Basics

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Microsoft Copilot Foundations

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Modern Natural Language Processing in Python

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Open-Source AI Cookbook

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LLMOps

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Introduction to LLMs in Python

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Frequently Asked Questions

What is a transformer model?
A transformer is a neural network architecture based on self-attention mechanisms. Introduced in 2017, it processes all input tokens simultaneously, enabling superior performance on language and vision tasks.
Why are transformers so important?
Transformers power virtually all modern AI breakthroughs including GPT, BERT, DALL-E, and more. Their parallel processing and attention mechanism enable unprecedented scale and capability.
What's the difference between BERT and GPT?
BERT is bidirectional and excels at understanding tasks like classification and Q&A. GPT is autoregressive and excels at text generation. They represent different approaches to transformer pre-training.
How do I get started with Hugging Face?
Start with the Hugging Face course (free). It covers using pre-trained models, tokenizers, and the Transformers library for tasks like text classification, NER, and question answering.

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