Data Science at Home

264 Folgen

1. What if I train a neural network with random data? (with Stanisław Jastrzębski) (Ep. 87)

   Vom: 12.11.2019
2. Deeplearning is easier when it is illustrated (with Jon Krohn) (Ep. 86)

   Vom: 5.11.2019
3. More powerful deep learning with transformers (Ep. 84)

   Vom: 27.10.2019
4. What is wrong with reinforcement learning? (Ep. 82)

   Vom: 15.10.2019
5. Have you met Shannon? Conversation with Jimmy Soni and Rob Goodman about one of the greatest minds in history (Ep. 81)

   Vom: 10.10.2019
6. Attacking machine learning for fun and profit (with the authors of SecML Ep. 80)

   Vom: 1.10.2019
7. [RB] How to scale AI in your organisation (Ep. 79)

   Vom: 26.9.2019
8. Replicating GPT-2, the most dangerous NLP model (with Aaron Gokaslan) (Ep. 78)

   Vom: 23.9.2019
9. How to generate very large images with GANs (Ep. 76)

   Vom: 6.9.2019
10. How to cluster tabular data with Markov Clustering (Ep. 73)

    Vom: 20.8.2019
11. Waterfall or Agile? The best methodology for AI and machine learning (Ep. 72)

    Vom: 14.8.2019
12. Training neural networks faster without GPU (Ep. 71)

    Vom: 6.8.2019
13. Validate neural networks without data with Dr. Charles Martin (Ep. 70)

    Vom: 23.7.2019
14. Complex video analysis made easy with Videoflow (Ep. 69)

    Vom: 16.7.2019
15. Episode 68: AI and the future of banking with Chris Skinner [RB]

    Vom: 9.7.2019
16. Episode 67: Classic Computer Science Problems in Python

    Vom: 2.7.2019
17. Episode 66: More intelligent machines with self-supervised learning

    Vom: 25.6.2019
18. Episode 65: AI knows biology. Or does it?

    Vom: 23.6.2019
19. Episode 64: Get the best shot at NLP sentiment analysis

    Vom: 14.6.2019
20. Episode 63: Financial time series and machine learning

    Vom: 4.6.2019