Am Neumarkt 😱

https://github.com/soumith/ganhacks Training GAN can be baffling. For example, the generator and the discriminator just don't "learn" at the same scale sometimes. Would you try to balance the generator loss and discriminator loss by hand? Soumith Chintala ( @ FAIR ) put together this list of tips for training GAN. "Don't balance loss via statistics" is one of the 17 tips by Chintala. The list is quite inspiring.

I have downloaded the file so you don't need to. Anaconda-2021-SODS-Report-Final.pdf

#DS This is an interesting report by anaconda. We can kind of confirm from this that Python is still the king of languages for data science. SQL is right following Python. Quote from the report: > Between March 2020 to February 2021, the pandemic economic period, we saw 4.6 billion package downloads, a 48% increase from the previous year. We have no data for other languages so no predictions can be made but it is interesting to see Python growing so fast. The roadblocks different data professionals facing are quite different. If the professional is a cloud engineer or mlops, then they do not mention that skills gap in the organization that many times. But for data scientists/analysts, skills gaps (e.g., data engineering, docker, k8s) is mentioned a lot. This might be related to the cases when the organization doesn't even have cloud engineers/ops or mlops. See the next message for the PDF file. https://www.anaconda.com/state-of-data-science-2021

#ML Julia Computing got a lot of investment recently. I need to dive deeper into the Julia Language. https://juliacomputing.com/blog/2021/07/series-a/

#DS PyData goes virtual this year. https://pydata.org/global2021/present/

#Coding I found a nice place to practice programming thinking. It is not as comprehensive as hackerrank/leetcode but these problems are quite fun. https://codingcompetitions.withgoogle.com/

#ML Implicit Regularization in Tensor Factorization: Can Tensor Rank Shed Light on Generalization in Deep Learning? – Off the convex path http://www.offconvex.org/2021/07/08/imp-reg-tf/

#TIL In PyTorch, conversion from Torch tensors to numpy arrays is very fast on CPUs, though torch tensors and numpy arrays are very different things. This is because of the Python buffer protocol. The protocol makes it possible to use binary data directly from C without copying the object. https://docs.python.org/3/c-api/buffer.htm Reference: Eli Stevens Luca Antiga. Deep Learning with PyTorch: Build, Train, and Tune Neural Networks Using Python Tools. Simon and Schuster, 2020;

#Academia The distill team's thought on interactive publishing and self-publishing in academia. https://distill.pub/2021/distill-hiatus/

#ML Great. Tensorflow implemented built-in decision forest models. https://blog.tensorflow.org/2021/05/introducing-tensorflow-decision-forests.html?m=1

#fun GitHub Copilot · Your AI pair programmer https://copilot.github.com/ This is crazy. What is GitHub Copilot? GitHub Copilot is an AI pair programmer that helps you write code faster and with less work. GitHub Copilot draws context from comments and code, and suggests individual lines and whole functions instantly. GitHub Copilot is powered by OpenAI Codex, a new AI system created by OpenAI. The GitHub Copilot technical preview is available as a Visual Studio Code extension. How good is GitHub Copilot? We recently benchmarked against a set of Python functions that have good test coverage in open source repos. We blanked out the function bodies and asked GitHub Copilot to fill them in. The model got this right 43% of the time on the first try, and 57% of the time when allowed 10 attempts. And it’s getting smarter all the time.

#ML A Turing lecture article by the three famous DL guys. It's an overview of the history, development, and future of AI. There are two very interesting points in the outlook section: - "From homogeneous layers to groups of neurons that represent entities." In biological brains, there are memory engrams and motifs that almost do this. - "Multiple time scales of adaption." This is another key idea that has been discussed numerous times. One of the craziest things about our brain is the diversity of time scales of plasticity, i.e., different mechanisms change the brain on different time scales. Reference: Bengio Y, Lecun Y, Hinton G. Deep learning for AI. Commun ACM. 2021;64: 58–65. doi:10.1145/3448250 https://dl.acm.org/doi/10.1145/3448250