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Deep Learning: Advice on Getting Started with fast.ai - Jeremy Howard | AI Podcast Clips
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This is a clip from a conversation with Jeremy Howard on the Artificial Intelligence podcast. You can watch the full conversation here: http://bit.ly/2NG4qwr If you enjoy these, consider subscribing, sharing, and commenting below. Full episode: http://bit.ly/2NG4qwr Full episodes playlist: http://bit.ly/2EcbaKf Clips playlist: http://bit.ly/2JYkbfZ Podcast website: https://lexfridman.com/ai 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. Subscribe to this YouTube channel or connect on: - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - Medium: https://medium.com/@lexfridman - Support on Patreon: https://www.patreon.com/lexfridman
Segments Timeline
Train Models to Learn Deep Learning
Jeremy Howard emphasizes the importance of training models as a fundamental way to learn deep learning. He encourages learners to experiment with their own datasets, fine-tune models, and analyze inputs and outputs to gain an intuitive understanding of the technology. Howard highlights the success of the fast.ai course, which recently won an award for being the best AI course globally.
"so what advice do you have for someone who wants to get started in deep learning train lots of models that's that's how you that's how you learn it so like so I would you know I think it's not just me..."
Creating Your Own Dataset
In this segment, Jeremy Howard discusses the process of creating a custom dataset from scratch using Google Image Search. He shares a practical example of building a web application that differentiates between bear species with high accuracy. Howard illustrates how students have successfully applied these techniques to create innovative projects, showcasing the effectiveness of hands-on learning.
"that's that's that's the critical thing because at that point you now have a model that's in your domain area so there's there's there's no point running somebody else's model because it's not your mo..."
Becoming an Expert in Your Domain
Jeremy Howard advises aspiring deep learning practitioners to focus on their specific domain of interest. He stresses the need for experts who can apply deep learning to real-world problems, such as diagnosing diseases or analyzing media bias. Howard encourages individuals to combine their passion with deep learning to create impactful solutions in their fields.
"our share your work here thread of students saying here's the thing I built and so those people who like and a lot of them are state of the art like somebody said oh I tried looking at different gary ..."
Solving Real Problems with Deep Learning
In this insightful discussion, Jeremy Howard highlights the significance of addressing real-world problems through deep learning research. He argues that understanding the context of the problem is crucial for evaluating the effectiveness of models and results. Howard emphasizes that the most interesting research stems from solving practical issues, encouraging learners to focus on meaningful applications of deep learning.
"eventually become an expert train lots of models train lots of models in your domain area so an expert what right we don't need more expert like create slightly evolutionary research in areas that eve..."