Jeremy Howard: fast.ai Deep Learning Courses and Research
Aug 27, 2019
auto_awesome
Jeremy Howard, founder of fast.ai and a distinguished AI researcher, discusses making deep learning accessible to all. He explores the advantages of self-funding startups, shares innovative learning strategies, and reveals his journey in programming. The conversation touches on using AI to revolutionize healthcare and the balance between privacy and data use. Howard also highlights hands-on learning in AI, success stories from his courses, and the societal implications of artificial intelligence, advocating for ethical considerations in its development.
Fast AI aims to democratize deep learning with free and accessible resources for beginners and experts.
Active learning and transfer learning hold transformative potential in deep learning for efficient advancements.
'Super convergence' in deep learning models allows accelerated training and enhanced generalization through learning rate optimization.
Deep dives
Fast AI: Making Deep Learning Accessible
Fast AI, the research institute founded by Jeremy Howard, focuses on democratizing deep learning. The institute aims to provide free, easy-to-start, insightful, and accessible resources for individuals interested in delving into deep learning. By emphasizing practical application and hands-on exploration, Fast AI caters to both beginners and experts in the field.
Exploring Programming Languages & Environments
Jeremy Howard reflects on his journey through various programming languages and environments, highlighting the pros and cons of each. He discusses his preference for Mic Access as a favorite programming environment due to its user-friendly interface. Additionally, the discussion touches on the significance of tools like Delphi and J in providing efficient and expressive programming capabilities.
Revolutionizing Active Learning and Transfer Learning
The conversation delves into the concepts of active learning and transfer learning, showcasing their transformative potential in the realm of deep learning. Jeremy discusses the underexplored areas of active learning and emphasizes the importance of leveraging transfer learning to achieve significant advancements efficiently and with minimal data.
Optimizing Training Through Learning Rate Magic
Jeremy Howard unveils the concept of 'super convergence' discovered by Leslie Smith, revolutionizing the impact of learning rates in deep learning models. By strategically adjusting learning rates during training, models can achieve accelerated training times and enhanced generalization. This innovative approach underscores the future direction of learning rate optimization in deep learning.
Evolution of Deep Learning Libraries
The evolution of deep learning libraries from Theano and TensorFlow to PyTorch and FastAI reflects the growth in the ecosystem. While Theano and TensorFlow required defining a computational graph upfront, PyTorch introduced a more user-friendly approach using normal Python. This shift enabled greater flexibility and ease of use for researchers and practitioners, leading to significant advancements in research and teaching.
Challenges and Innovations in Deep Learning
While PyTorch brought about a more accessible framework, challenges remained in writing training loops and managing gradients. To address this, the FastAI library was developed, offering a multi-layered API for training neural networks efficiently. Despite Python limitations, efforts are being made to explore Swift in deep learning to overcome performance bottlenecks. Additionally, criticisms of TensorFlow's approach and the need for accessible, efficient tools highlight the evolving landscape of deep learning libraries.
Jeremy Howard is the founder of fast.ai, a research institute dedicated to make deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, a former president of Kaggle as well a top-ranking competitor there, and in general, he’s a successful entrepreneur, educator, research, and an inspiring personality in the AI community. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on iTunes or support it on Patreon.
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
