Sure!
1. Assembly: ~1977
2. Basic: ~1978
3. Pascal: ~1981
4. Modula-2: ~1986
5. C: ~1986
6. Lisp: ~1987
7. Ada: ~1991
8. Perl: ~1992
9. Miranda:~1992
10. C++:~1992
11. Self:~1992
12. Cecil:~1993
13. Modula-3:~1995
14. Java:~1996
15. Javascript:~1998
16. Python:~2003
17. Go:~2015
CODE
-
Programming Languages Evolution Timeline 1977-2015
By
–
-
Convert UI Workflows to Terraform Infrastructure as Code
By
–
DYK that it's possible to inherit a UI-created workflow and maintain it as code?! Learn how to convert an existing workflow into Terraform IaC and the various ways that it can be deployed https://
dbricks.co/3Ceh2nd -
Running Diffusion Models on Mac Devices with ANE Support
By
–
You can select whichever device you want to run it on. ANE is supported as well. More deets here: https://
huggingface.co/blog/fast-mac-
diffusers
… -
Deep Learning Models vs Systems: Bridging the Complexity Gap
By
–
Building a Deep Learning model is only a fraction of the work. Building an entire Deep Learning System is much more complex and takes time. Closing this gap is precisely what @abacusai does.
-
Criticism of Greek letters in algorithm naming conventions
By
–
Being cute and putting Greek letters in your algorithm name is not appreciated. Looking at you, Maximal Update Parameteization / (µP)!
-
Non-linear image distortion for Gaussian pixel statistics in ML models
By
–
I wonder if there would be any benefit for ML models to non linearly distorting image pixel statistics into a true Gaussian distribution instead of just the traditional scale and bias.
-
Open Science and Efficient AI Models on Hugging Face
By
–
Open science and open source can't stop won't stop! + smaller more efficient models for the win! Soon on @huggingface
-
LLaMA-62B Surpasses PaLM on Code Generation Benchmarks
By
–
On code generation benchmarks, LLaMA-62B outperforms cont-PaLM (62B) as well as PaLM-540B.
-
Inference: Applying Trained Models to Make Predictions
By
–
Inference "Inference" is applying a trained model to unlabeled samples to obtain the corresponding targets. In other words, "inference" is the process of making predictions using a model.
-
Validation: Assessing Model Performance on Unseen Data
By
–
Validation Validation is the process that lets us know whether a model is any good. Usually, we run a set of (unseen) labeled samples through a model to ensure that it can predict the targets.