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Data Science at Home
https://datascienceat...
Belgium

Technology, machine learning and algorithms

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Let's talk about federated learning (Ep. 122)
Durée : 30min 10s
How to test machine learning in production (Ep. 121)
Durée : 28min 48s
Why synthetic data cannot boost machine learning (Ep. 120)
Durée : 23min 23s
Machine learning in production: best practices [LIVE from twitch.tv] (Ep. 119)
Durée : 37min 31s
Testing in machine learning: checking deeplearning models (Ep. 118)
Durée : 18min 17s
Testing in machine learning: generating tests and data (Ep. 117)
Durée : 20min 18s
Why you care about homomorphic encryption (Ep. 116)
Durée : 18min 50s
Test-First machine learning (Ep. 115)
Durée : 19min 44s
GPT-3 cannot code (and never will) (Ep. 114)
Durée : 19min 6s
Make Stochastic Gradient Descent Fast Again (Ep. 113)
Durée : 20min 35s
What data transformation library should I use? Pandas vs Dask vs Ray vs Modin vs Rapids (Ep. 112)
Durée : 21min 10s
[RB] It’s cold outside. Let’s speak about AI winter (Ep. 111)
Durée : 36min 54s
Rust and machine learning #4: practical tools (Ep. 110)
Durée : 24min 18s
Rust and machine learning #3 with Alec Mocatta (Ep. 109)
Durée : 23min 58s
Rust and machine learning #2 with Luca Palmieri (Ep. 108)
Durée : 27min 2s
Rust and machine learning #1 (Ep. 107)
Durée : 22min 27s
Protecting workers with artificial intelligence (with Sandeep Pandya CEO Everguard.ai)(Ep. 106)
Durée : 16min 20s
Compressing deep learning models: rewinding (Ep.105)
Durée : 15min 31s
Compressing deep learning models: distillation (Ep.104)
Durée : 22min 19s
Pandemics and the risks of collecting data (Ep. 103)
Durée : 20min 9s
Why average can get your predictions very wrong (ep. 102)
Durée : 14min 47s
Activate deep learning neurons faster with Dynamic RELU (ep. 101)
Durée : 22min 18s
WARNING!! Neural networks can memorize secrets (ep. 100)
Durée : 24min 16s
Attacks to machine learning model: inferring ownership of training data (Ep. 99)
Durée : 19min 39s
Don't be naive with data anonymization (Ep. 98)
Durée : 13min 41s
Why sharing real data is dangerous (Ep. 97)
Durée : 10min 35s
Building reproducible machine learning in production (Ep. 96)
Durée : 14min 20s
Bridging the gap between data science and data engineering: metrics (Ep. 95)
Durée : 13min 25s
A big welcome to Pryml: faster machine learning applications to production (Ep. 94)
Durée : 9min 26s
It's cold outside. Let's speak about AI winter (Ep. 93)
Durée : 36min 48s
The dark side of AI: bias in the machine (Ep. 92)
Durée : 20min 26s
The dark side of AI: metadata and the death of privacy (Ep. 91)
Durée : 23min 0s
The dark side of AI: recommend and manipulate (Ep. 90)
Durée : 20min 33s
The dark side of AI: social media and the optimization of addiction (Ep. 89)
Durée : 22min 45s
More powerful deep learning with transformers (Ep. 84) (Rebroadcast)
Durée : 37min 44s
How to improve the stability of training a GAN (Ep. 88)
Durée : 28min 16s
How to improve the stability of training a GAN (Ep. 88)
Durée : 28min 20s
What if I train a neural network with random data? (with Stanisław Jastrzębski) (Ep. 87)
Durée : 19min 37s
Deeplearning is easier when it is illustrated (with Jon Krohn) (Ep. 86)
Durée : 44min 53s
[RB] How to generate very large images with GANs (Ep. 85)
Durée : 14min 41s
[RB] Replicating GPT-2, the most dangerous NLP model (with Aaron Gokaslan) (Ep. 83)
Durée : 37min 47s
What is wrong with reinforcement learning? (Ep. 82)
Durée : 21min 48s
Have you met Shannon? Conversation with Jimmy Soni and Rob Goodman about one of the greatest minds …
Durée : 32min 21s
Attacking machine learning for fun and profit (with the authors of SecML Ep. 80)
Durée : 34min 4s
[RB] How to scale AI in your organisation (Ep. 79)
Durée : 13min 21s
Training neural networks faster without GPU [RB] (Ep. 77)
Durée : 22min 21s
[RB] Complex video analysis made easy with Videoflow (Ep. 75)
Durée : 30min 42s
How to cluster tabular data with Markov Clustering (Ep. 73)
Durée : 20min 43s
Waterfall or Agile? The best methodology for AI and machine learning (Ep. 72)
Durée : 14min 26s
Validate neural networks without data with Dr. Charles Martin (Ep. 70)
Durée : 44min 46s
Episode 68: AI and the future of banking with Chris Skinner [RB]
Durée : 41min 42s
Episode 67: Classic Computer Science Problems in Python
Durée : 28min 35s
Episode 66: More intelligent machines with self-supervised learning
Durée : 18min 56s
Episode 65: AI knows biology. Or does it?
Durée : 12min 14s
Episode 64: Get the best shot at NLP sentiment analysis
Durée : 12min 54s
Episode 64: Get the best shot at NLP sentiment analysis
Durée : 12min 58s
Episode 63: Financial time series and machine learning
Durée : 21min 8s
Episode 62: AI and the future of banking with Chris Skinner
Durée : 42min 3s
Episode 61: The 4 best use cases of entropy in machine learning
Durée : 21min 35s
Episode 60: Predicting your mouse click (and a crash course in deeplearning)
Durée : 39min 50s
Episode 59: How to fool a smart camera with deep learning
Durée : 24min 11s
Episode 58: There is physics in deep learning!
Durée : 19min 55s
Episode 57: Neural networks with infinite layers
Durée : 16min 19s
Episode 56: The graph network
Durée : 16min 34s
Episode 55: Beyond deep learning
Durée : 17min 23s
Episode 54: Reproducible machine learning
Durée : 11min 50s
Episode 53: Estimating uncertainty with neural networks
Durée : 15min 8s
Episode 52: why do machine learning models fail? [RB]
Durée : 15min 58s
Episode 51: Decentralized machine learning in the data marketplace (part 2)
Durée : 23min 8s
Episode 50: Decentralized machine learning in the data marketplace
Durée : 24min 17s
Episode 49: The promises of Artificial Intelligence
Durée : 21min 0s
Episode 48: Coffee, Machine Learning and Blockchain
Durée : 28min 48s
Episode 47: Are you ready for AI winter? [Rebroadcast]
Durée : 56min 55s
Episode 46: why do machine learning models fail? (Part 2)
Durée : 17min 12s
Episode 45: why do machine learning models fail?
Durée : 16min 21s
Episode 44: The predictive power of metadata
Durée : 21min 8s
Episode 43: Applied Text Analysis with Python (interview with Rebecca Bilbro)
Durée : 36min 32s
Episode 42: Attacking deep learning models (rebroadcast)
Durée : 29min 4s
Episode 41: How can deep neural networks reason
Durée : 18min 4s
Episode 40: Deep learning and image compression
Durée : 17min 20s
Episode 39: What is L1-norm and L2-norm?
Durée : 21min 55s
Episode 38: Collective intelligence (Part 2)
Durée : 46min 36s
Episode 38: Collective intelligence (Part 1)
Durée : 30min 58s
Episode 37: Predicting the weather with deep learning
Durée : 26min 25s
Episode 36: The dangers of machine learning and medicine
Durée : 22min 7s
Episode 35: Attacking deep learning models
Durée : 29min 13s
Episode 34: Get ready for AI winter
Durée : 59min 4s
Episode 33: Decentralized Machine Learning and the proof-of-train
Durée : 17min 40s
Episode 32: I am back. I have been building fitchain
Durée : 23min 14s
Founder Interview – Francesco Gadaleta of Fitchain
Durée : 31min 4s
Episode 31: The End of Privacy
Durée : 39min 3s
Episode 30: Neural networks and genetic evolution: an unfeasible approach
Durée : 22min 19s
Episode 29: Fail your AI company in 9 steps
Durée : 14min 27s
Episode 28: Towards Artificial General Intelligence: preliminary talk
Durée : 20min 34s
Episode 27: Techstars accelerator and the culture of fireflies
Durée : 17min 42s
Episode 26: Deep Learning and Alzheimer
Durée : 54min 2s
Episode 25: How to become data scientist [RB]
Durée : 16min 16s
Episode 24: How to handle imbalanced datasets
Durée : 21min 21s
Episode 23: Why do ensemble methods work?
Durée : 18min 59s
Episode 22: Parallelising and distributing Deep Learning
Durée : 19min 42s

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