Dr. Jon Krohn

Dr. Jon Krohn

Chief Data Scientist at Nebula.io

    Jon Krohn is Co-Founder and Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the data science industry’s most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at leading universities and conferences, as well as via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010.

    All Sessions by Dr. Jon Krohn

    Day 2 04/24/2024
    11:00 am - 4:30 pm

    Generative A.I. with Open-Source LLMs: From Training to Deployment with Hugging Face and PyTorch Lightning

    <span class="etn-schedule-location"> <span class="firstfocus">Generative AI</span> </span>

    At an unprecedented pace, Large Language Models (like the GPT, Llama, Gemini and Gemma series) are transforming the world in general and the field of data science in particular. This training introduces deep learning transformer architectures, covering how LLMs are used for natural language processing, with a special focus on generative A.I. applications. Brought to life via hands-on code demos that leverage the Hugging Face Transformers and PyTorch Lightning Python libraries, this training covers the latest best-practices across the full lifecycle of LLM development, from training to production deployment. Session Outline: Module 1: Introduction to Large Language Models - Transformer Architectures Module 2: The Breadth of LLM Capabilities - OpenAI APIs, including GPT-4 Module 3: Training and Deploying LLMs - Hugging Face models - Training with PyTorch Lightning - Streaming data sets - Deployment considerations - Parameter-efficient fine-tuning (PEFT) with low-rank adaptation (LoRA) - Single-GPU models - Multiple GPUs Module 4: Getting Commercial Value from LLMs - Tasks that can be Automated - Tasks that can be Augmented - Guidance for Successful A.I. Teams and Projects Parts of this training will be accessible to anyone who would like to understand how to develop commercially-successful data products in the new paradigm unleashed by LLMs like GPT-4. To make the most of this training, attendees should be proficient in deep learning and Python programming.

    Day 1 04/23/2024
    11:00 am - 4:30 pm

    Deep Learning with PyTorch and TensorFlow

    <span class="etn-schedule-location"> <span class="firstfocus">Deep Learning</span> </span>

    Deep Learning is ubiquitous today across data-driven applications as diverse as generative A.I., natural language processing, machine vision, and superhuman game-playing. This workshop is an introduction to Deep Learning that brings high-level theory to life with interactive examples featuring PyTorch, TensorFlow and Keras — all three of the principal Python libraries for Deep Learning. Essential theory will be covered in a manner that provides students with a complete intuitive understanding of Deep Learning’s underlying foundations. Paired with hands-on code demos in Jupyter notebooks as well as strategic advice for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of artificial neural networks to train Deep Learning models following all of the latest best-practices. Session Outline: Lesson 1: The Unreasonable Effectiveness of Deep Learning Training Overview Introduction to Neural Networks and Deep Learning The Deep Learning Families and Libraries Lesson 2: Essential Deep Learning Theory The Cart Before the Horse: A Shallow Neural Network Learning with Artificial Neurons TensorFlow Playground—Visualizing a Deep Net in Action Lesson 3: Deep Learning with PyTorch and TensorFlow Revisiting our Shallow Neural Network Deep Nets Convolutional Neural Networks

    Open Data Science




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    Cambridge, MA 02142

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