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Yoshua Bengio

Showing 16–17 of 17 transcripts.

  1. Lex Fridman1h 32m

    Deep Learning for Speech Recognition (Adam Coates, Baidu)

    Adam Coates, Hugo Larochelle, Andrej Karpathy, Richard Socher, Sherry Moore, Ruslan Salakhutdinov, Andrew Ng, John Schulman, Pascal Lamblin, Alex Wiltschko, Quoc Le, Yoshua Bengio, Shubho Sengupta

    Deep learning has revolutionized speech recognition by replacing traditional, error-prone pipeline architectures with end-to-end neural networks that map raw audio directly to text, achieving character error rates below 6% in Mandarin. This shift utilizes techniques such as Connectionist Temporal Classification and advanced data augmentation to overcome historical limitations in accuracy and scalability, enabling systems to match human transcriber performance while significantly increasing user productivity. As researchers address computational bottlenecks through optimized training strategies like dynamic batching, these models are transitioning from experimental benchmarks to production-ready tools for consumer applications ranging from real-time captioning to hands-free vehicle control.

  2. Lex Fridman1h 1m

    Foundations of Deep Learning (Hugo Larochelle, Twitter)

    Hugo Larochelle, Andrej Karpathy, Richard Socher, Sherry Moore, Ruslan Salakhutdinov, Andrew Ng, John Schulman, Pascal Lamblin, Adam Coates, Alex Wiltschko, Quoc Le, Yoshua Bengio, Shubho Sengupta

    This lecture by Hugo Lavachel delivers a comprehensive technical analysis of feedforward neural networks, detailing the mathematical foundations of backpropagation, universal approximation, and modern optimization algorithms like Adam and stochastic gradient descent. The presentation critically evaluates architectural choices, contrasting activation functions such as ReLU against sigmoid and tanh while explaining how dropout and batch normalization mitigate overfitting and stabilize training in deep architectures. By bridging theoretical constraints with practical debugging strategies, the course equips practitioners with the necessary tools to implement, tune, and validate deep learning models effectively.