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.
AI: The Bubble That Might Pop—What’s Next? (Ep. 262)
Data Guardians: How Enterprises Can Master Privacy with MetaRouter (Ep. 261)
Low-Code Magic: Can It Transform Analytics? (Ep. 260)
Do you really know how GPUs work? (Ep. 259)
Harnessing AI for Cybersecurity: Expert Tips from QFunction (Ep. 258)
Rust in the Cosmos Part 4: What happens in space? (Ep. 257)
Rust in the Cosmos Part 3: Embedded programming for space (Ep. 256)
Rust in the Cosmos Part 2: testing software in space (Ep. 255)
Rust in the Cosmos Part 1: Decoding Communication (Ep. 254)
AI and Video Game Development: Navigating the Future Frontier (Ep. 253)
Kaggle Kommando's Data Disco: Laughing our Way Through AI Trends (Ep. 252)
Revolutionizing Robotics: Embracing Low-Code Solutions (Ep. 251)
Is SQream the fastest big data platform? (Ep. 250)
OpenAI CEO Shake-up: Decoding December 2023 (Ep. 249)
Careers, Skills, and the Evolution of AI (Ep. 248)
Open Source Revolution: AI’s Redemption in Data Science (Ep. 247)
Money, Cryptocurrencies, and AI: Exploring the Future of Finance with Chris Skinner [RB] (Ep. 246)
Debunking AGI Hype and Embracing Reality [RB] (Ep. 245)
Destroy your toaster before it kills you. Drama at OpenAI and other stories (Ep. 244)
The AI Chip Chat 🤖💻 (Ep. 243)
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