Episode 19: How to completely change your data analytics strategy with deep learning
Over the past few years, neural networks have re-emerged as powerful machine-learning models, reaching state-of-the-art results in several fields like image recognition and speech processing. More recently, neural network models started to be applied also to textual data in order to deal with natural language, and there too with promising results. In this episode I explain why is deep learning performing the way it does, and what are some of the most tedious causes of failure.
Episode 43: Applied Text Analysis with Python (interview with Rebecca Bilbro)
Episode 42: Attacking deep learning models (rebroadcast)
Episode 41: How can deep neural networks reason
Episode 40: Deep learning and image compression
Episode 39: What is L1-norm and L2-norm?
Episode 38: Collective intelligence (Part 2)
Episode 38: Collective intelligence (Part 1)
Episode 37: Predicting the weather with deep learning
Episode 36: The dangers of machine learning and medicine
Episode 35: Attacking deep learning models
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