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.
Unleashing the Force: Blending Neural Networks and Physics for Epic Predictions (Ep. 230)
AI’s Impact on Software Engineering: Killing Old Principles? [RB] (Ep. 229)
Warning! Mathematical Mayhem Ahead: Demystifying Liquid Time-Constant Networks (Ep. 228)
Efficiently Retraining Language Models: How to Level Up Without Breaking the Bank (Ep. 227)
Revolutionize Your AI Game: How Running Large Language Models Locally Gives You an Unfair Advantage Over Big Tech Giants (Ep. 226)
Rust: A Journey to High-Performance and Confidence in Code at Amethix Technologies (Ep. 225)
The Power of Graph Neural Networks: Understanding the Future of AI - Part 2/2 (Ep.224)
The Power of Graph Neural Networks: Understanding the Future of AI - Part 1/2 (Ep.223)
Leveling Up AI: Reinforcement Learning with Human Feedback (Ep. 222)
The promise and pitfalls of GPT-4 (Ep. 221)
AI’s Impact on Software Engineering: Killing Old Principles? (Ep. 220)
Edge AI applications for military and space [RB] (Ep. 219)
Prove It Without Revealing It: Exploring the Power of Zero-Knowledge Proofs in Data Science (Ep. 218)
Deep learning vs tabular models (Ep. 217)
[RB] Online learning is better than batch, right? Wrong! (Ep. 216)
Chatting with ChatGPT: Pros and Cons of Advanced Language AI (Ep. 215)
Accelerating Perception Development with Synthetic Data (Ep. 214)
Edge AI applications for military and space [RB] (Ep. 213)
From image to 3D model (Ep. 212)
Machine learning is physics (Ep. 211)
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