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 48: Coffee, Machine Learning and Blockchain
Episode 47: Are you ready for AI winter? [Rebroadcast]
Episode 46: why do machine learning models fail? (Part 2)
Episode 45: why do machine learning models fail?
Episode 44: The predictive power of metadata
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?
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