Introduction to Transformer Models for NLP: Using BERT, GPT, and More to Solve Modern Natural Language Processing Tasks

Introduction to Transformer Models for NLP: Using BERT, GPT, and More to Solve Modern Natural Language Processing Tasks
Duration: 10h 13m | .MP4 1280×720, 30 fps(r) | AAC, 48000 Hz, 2ch | 2.53 GB

Overview
All lessons in the course are grounded by real-life case studies and hands-on code examples. After completing this lesson, you will be in a great position to understand and build cutting-edge NLP pipelines using transformers. You will also be provided with extensive resources and curriculum detail which can all be found at the course’s GitHub repository.

About the Instructor
Sinan Ozdemir’is currently Founder and CTO of Shiba Technologies. Sinan is a former lecturer of Data Science at Johns Hopkins University and the author of multiple textbooks on data science and machine learning. Additionally, he is the founder of the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a master’s degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco, CA.

Skill Level
Intermediate
Advanced
Learn How To
Recognize which type of transformer-based model is best for a given task
Understand how transformers process text and make predictions
Fine-tune a transformer-based model
Create pipelines using fine-tuned models
Deploy fine-tuned models and use them in production
Who Should Take This Course
Intermediate/advanced machine learning engineers with experience with ML, neural networks, and NLP
Those interested in productionizing NLP models
Those comfortable using libraries like Tensorflow or PyTorch
Those comfortable with linear algebra and vector/matrix operations
Course Requirements
Python 3 proficiency with some experience working in interactive Python environments including Notebooks (Jupyter/Google Colab/Kaggle Kernels)
Comfortable using the Pandas library and either Tensorflow or PyTorch
Understanding of ML/deep learning fundamentals including train/test splits, loss/cost functions, and gradient descent