Data Science at Home

264 Episodes

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

   Published: 11/12/2019
2. Deeplearning is easier when it is illustrated (with Jon Krohn) (Ep. 86)

   Published: 11/5/2019
3. More powerful deep learning with transformers (Ep. 84)

   Published: 10/27/2019
4. What is wrong with reinforcement learning? (Ep. 82)

   Published: 10/15/2019
5. Have you met Shannon? Conversation with Jimmy Soni and Rob Goodman about one of the greatest minds in history (Ep. 81)

   Published: 10/10/2019
6. Attacking machine learning for fun and profit (with the authors of SecML Ep. 80)

   Published: 10/1/2019
7. [RB] How to scale AI in your organisation (Ep. 79)

   Published: 9/26/2019
8. Replicating GPT-2, the most dangerous NLP model (with Aaron Gokaslan) (Ep. 78)

   Published: 9/23/2019
9. How to generate very large images with GANs (Ep. 76)

   Published: 9/6/2019
10. How to cluster tabular data with Markov Clustering (Ep. 73)

    Published: 8/20/2019
11. Waterfall or Agile? The best methodology for AI and machine learning (Ep. 72)

    Published: 8/14/2019
12. Training neural networks faster without GPU (Ep. 71)

    Published: 8/6/2019
13. Validate neural networks without data with Dr. Charles Martin (Ep. 70)

    Published: 7/23/2019
14. Complex video analysis made easy with Videoflow (Ep. 69)

    Published: 7/16/2019
15. Episode 68: AI and the future of banking with Chris Skinner [RB]

    Published: 7/9/2019
16. Episode 67: Classic Computer Science Problems in Python

    Published: 7/2/2019
17. Episode 66: More intelligent machines with self-supervised learning

    Published: 6/25/2019
18. Episode 65: AI knows biology. Or does it?

    Published: 6/23/2019
19. Episode 64: Get the best shot at NLP sentiment analysis

    Published: 6/14/2019
20. Episode 63: Financial time series and machine learning

    Published: 6/4/2019