Ementorhub

What you'll learn

Differentiate between generative AI architectures and models, such as RNNs, transformers, VAEs, GANs, and diffusion models

Describe how LLMs, such as GPT, BERT, BART, and T5, are applied in natural language processing tasks

Implement tokenization to preprocess raw text using NLP libraries like NLTK, spaCy, BertTokenizer, and XLNetTokenizer

Create an NLP data loader in PyTorch that handles tokenization, numericalization, and padding for text datasets

Skills you'll gain

Text Mining

Data Processing

Artificial Intelligence and Machine Learning (AI/ML)

Generative AI

Natural Language Processing

Large Language Modeling

Artificial Neural Networks

PyTorch (Machine Learning Library)

There are 2 modules in this course

Designed for data scientists, ML engineers, and AI enthusiasts, you’ll learn to differentiate between various generative AI architectures and models, such as recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. You’ll also discover how LLMs, such as generative pretrained transformers (GPT) and bidirectional encoder representations from transformers (BERT), power real-world language tasks. Get hands-on with tokenization techniques using NLTK, spaCy, and Hugging Face, and build efficient data pipelines with PyTorch data loaders to prepare models for training. A basic understanding of Python, PyTorch, and familiarity with machine learning and neural networks are helpful but not mandatory. Enroll today and get ready to launch your journey into generative AI!

Data Preparation for LLMs