Episode 391: Jeremy Howard on Deep Learning and fast.ai

Filed in Episodes by on December 6, 2019 0 Comments

Jeremy HowardJeremy Howard from fast.ai explains deep learning from concept to implementation. With transfer learning, individuals and small organizations can quickly get to work on machine learning problems using the open source fastai library and desktop graphics hardware. Jeremy and host Nate Black discuss neural network architecture and deep learning models, using pre-trained models from a “model zoo,” why coding ability and tenacity are key skills for success in deep learning, which tools are essential for practicing deep learning, creating a language model for natural language processing, and cool things you can do with validation sets. Jeremy also answers: What can you learn with the fast.ai course? Which parts of the pipeline will you learn to implement from scratch? Why are some portions of the fast.ai course and the fastai library moving from Python to Swift?

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SE Radio theme music: “Broken Reality” by Kevin MacLeod (incompetech.com)
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