Leveraging Transformers in Data Science for Advanced Natural Language Processing

ChatGPT, GPT-4, BERT, Deep Learning, Machine Learning & NLP with Hugging Face, Attention in Python, Tensorflow, PyTorch

💡 Apply transformers to real-world tasks with just a few lines of code
💻 Fine-tune transformers on your own datasets with transfer learning
📊 Sentiment analysis, spam detection, text classification
🔍 NER (named entity recognition), parts-of-speech tagging
🔄 Build your own article spinner for SEO
🤖 Generate believable human-like text
🌐 Neural machine translation and text summarization
❓ Question-answering (e.g. SQuAD)
🎯 Zero-shot classification
🔍 Understand self-attention and in-depth theory behind transformers
🔧 Implement transformers from scratch
🔧 Use transformers with both Tensorflow and PyTorch
🤖 Understand BERT, GPT, GPT-2, and GPT-3, and where to apply them
🔍 Understand encoder, decoder, and seq2seq architectures
🐍 Master the Hugging Face Python library
🧠 Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

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🧠 Foundations of AI Technologies:

AI technologies like OpenAI ChatGPT, GPT-4, Gemini Pro, Llama 3, DALL-E, Midjourney, and Stable Diffusion are explored.
Course delves into the foundational principles behind these innovations.

🔍 Introduction to Transformers:

Overview of the transformative impact of Transformers in deep learning.
Text generation capabilities akin to human writing achieved through machine learning advancements.

🌐 State-of-the-Art NLP Tasks:

Achievements in NLP tasks such as machine translation, question-answering, entailment, named entity recognition, and more.
Multi-modal models capable of generating art using text prompts.

🔧 Practical Applications:

Practical skills for applying transformers introduced.
Course covers both practical applications and detailed theory behind transformers and attention mechanisms.

📚 Course Structure:

Course divided into three major parts: Using Transformers, Fine-Tuning Transformers, and Transformers In-Depth.
Each part focuses on different aspects of transformer technology and its application.

🤖 Using Transformers:

Prebuilt models' usage explored for tasks like text classification, named entity recognition, summarization, and more.
Demonstrates practicality of prebuilt models for various real-world tasks.

🎯 Fine-Tuning Transformers:

Techniques for improving transformer performance on custom datasets explained.
Utilization of transfer learning to enhance model capabilities demonstrated.

🔬 Transformers In-Depth:

Delve into inner workings of transformers, including encoders, decoders, and BERT.
Implementation of transformers from scratch and understanding theoretical underpinnings emphasized.

📝 Prerequisites and Features:

Suggested prerequisites include Python coding skills and familiarity with deep learning concepts.
Unique features include detailed code explanations, avoidance of superficial coding exercises, and inclusion of university-level math for algorithm understanding.