Deep Learning and Keras with François Chollet
“I definitely think we can try to abstract away the first principles of intelligence and then try to go from these principles to an intelligent machine that might look nothing like the brain.”
Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. In this episode, François discusses the state of deep learning, and explains why the field is experiencing a cambrian explosion that eventually may taper off. He explains the need for Keras and why its simplicity and ease makes it a useful deep learning library for developers to experiment and build with.
François Chollet is the author of Keras and the founder of Wysp, learning platform for artists. He currently works for Google as a deep learning engineer and researcher.
- Do you try to design intelligent machines using the human brain as a blueprint?
- How has the structure of software engineering teams changed to accommodate the addition of machine learning?
- What are the best practices for deploying machine learning systems developed in production by data scientists?
- Why do neural network developers need to be able to perform fast experimentation?
- Why is modularity important to a deep learning library?
- How does Keras interface with the GPU?
- What are the interesting trends you notice in machine learning?
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