Published Mar 1, 2017
Deep Learning: Modular in Theory, Inflexible in Practice with Diogo Almeida - #8
Diogo Almeida, a senior data scientist, delves into the practical challenges of deep learning, including insights from Kaggle competitions and the complexities of Spatial Transformer Networks, addressing the balance between theoretical potential and real-world inflexibility in model applications.
Topics covered
Popular Clips
Episode Highlights
Related Episodes
Geometric Deep Learning with Joan Bruna & Michael Bronstein - #90
Answers 383 questions
Invariance, Geometry and Deep Neural Networks with Pavan Turaga - #386 (Video)
Answers 383 questions
Deep Neural Nets for Visual Recognition with Matt Zeiler - #22
Answers 383 questionsInvariance, Geometry and Deep Neural Networks with Pavan Turaga - #386
Answers 383 questions
Deep Learning, Transformers, and the Consequences of Scale with Oriol Vinyals - #546
Answers 383 questions
Training Large-Scale Deep Nets with RL with Nando de Freitas - TWiML Talk #213
Answers 383 questions
Engineering Practical Machine Learning Systems with Xavier Amatriain - #3
Answers 383 questions
Deep Learning is Eating 5G. Here’s How, w/ Joseph Soriaga - #525
Answers 383 questions
Deep Learning for Warehouse Operations with Calvin Seward - #38
Answers 383 questions
Deep Learning in Optics with Aydogan Ozcan - TWiML Talk #237
Answers 383 questions
Composing Graphical Models With Neural Networks, w/ David Duvenaud - #96
Answers 383 questions
Engineering a Less Artificial Intelligence with Andreas Tolias - #379
Answers 383 questions
Spiking Neural Networks: A Primer with Dr. Terrence Sejnowski - #317
Answers 383 questions
AI Trends 2024: Machine Learning & Deep Learning with Thomas Dietterich - 666
Answers 383 questions
