View profile weekly - Issue #28: The limits of Deep Learning, The state of ML frameworks in 2019, and more...


nibble dispatch

October 17 · Issue #28 · View online

Curated essays about the future of Data Science. Production Data Science and learning resources for continuous learning. Covers Data Science, Data Engineering, MLOps & DataOps. Curated by people at

Hello everyone,
There is a recurring theme in this week’s edition: the limits of deep neural networks. With so much hype around deep learning, it’s good to take a step back and see both their strengths and weaknesses. If that’s something you’re interested in, all these resources are tagged with a ⭐️.
On a side note, I’m thrilled to receive such great support from all of you. You can help us push this bigger. If you like what you read, forward this issue to a friend so that they can enjoy it as well.

Why Deep Learning models are so easy to fool (Illustration by Edgar Bąk)
Why Deep Learning models are so easy to fool (Illustration by Edgar Bąk)
⭐️ Why Deep Learning models are so easy to fool
⭐️ Yoshua Bengio says deep learning needs to be fixed
The State of Machine Learning Frameworks in 2019
How to Manage Machine Learning Products
Considerations for producing the best AI and Machine Learning models
Best practices for data modeling
⭐️ Toward a Hybrid of Deep Learning and Symbolic AI
⭐️ What BERT is Not
Facebook Debuts PyTorch 1.3 With PyTorch Mobile, Quantization, TPU Support and More
Cool New Features in Python 3.8
Learning resources
An Introduction to the Bootstrap Method
Data Structures Easy to Advanced Course
Analyzing Your MLflow Data with DataFrames
End notes
Call for speakers in Paris 🇫🇷
We’re looking for speakers for community events in Paris to share good practices about operationalizing data science.
If you’re working on improving the lifecycle of data science project within your organization and want to share your experience, reply to this email so we can set something up.
Have a great week!
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