interviews.ai
ivy
| interviews.ai | ivy | |
|---|---|---|
| 12 | 17 | |
| 4,437 | 14,015 | |
| - | 0.1% | |
| 0.0 | 10.0 | |
| over 2 years ago | 4 days ago | |
| Python | ||
| - | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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interviews.ai
- Deep Learning Interviews
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Ask HN: Leet code/CTCI equivalent for Data science/ML roles
scientists" - those interviews focus a lot of SQL, product metrics, A/B testing etc. You can also do SQL problems on leetcode for those types of positions.
2. Deep learning interviews book for ML positions - https://github.com/BoltzmannEntropy/interviews.ai - it's a bit too deep and advanced for most interviews though so don't be intimidated if you can't cover everything. Don't read this book if you're applying for a product DS position (and vice versa). You can also replace this with an ML theory book of your choice if you like.
3. Still leetcode and CTCI because they often come up for ML positions anyway.
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what to study for MLE interviews? Is it leetcode all the way?
Regarding how to study, my suggestion is to solve problems with sample datasets. A couple of books that might come in handy. 1. https://github.com/BoltzmannEntropy/interviews.ai - I like this because there are problems and solutions in there. 2. https://huyenchip.com/ml-interviews-book/
- Deep Learning Interviews book: Hundreds of fully solved job interview questions from a wide range of key topics in AI.
- GitHub - BoltzmannEntropy/interviews.ai: Deep Learning Interviews book: Hundreds of fully solved job interview questions from a wide range of key topics in AI
- Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI
- Deep Learning Interviews book: Hundreds of fully solved job interview questions
ivy
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Keras 3.0
See also https://github.com/unifyai/ivy which I have not tried but seems along the lines of what you are describing, working with all the major frameworks
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Show HN: Carton – Run any ML model from any programming language
is this ancillary to what [these guys](https://github.com/unifyai/ivy) are trying to do?
- Ivy: All in one machine learning framework
- Ivy ML Transpiler and Framework
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[D] Keras 3.0 Announcement: Keras for TensorFlow, JAX, and PyTorch
https://unify.ai/ They are trying to do what Ivy is doing already.
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Ask for help: what is the best way to have code both support torch and numpy?
Check Ivy.
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CoreML Stable Diffusion
ROCm's great for data centers, but good luck finding anything about desktop GPUs on their site apart from this lone blog post: https://community.amd.com/t5/instinct-accelerators/exploring...
There's a good explanation of AMD's ROCm targets here: https://news.ycombinator.com/item?id=28200477
It's currently a PITA to get common Python libs like Numba to even talk to AMD cards (admittedly Numba won't talk to older Nvidia cards either and they deprecate ruthlessly; I had to downgrade 8 versions to get it working with a 5yo mobile workstation). YC-backed Ivy claims to be working on unifying ML frameworks in a hardware-agnostic way but I don't have enough experience to assess how well they're succeeding yet: https://lets-unify.ai
I was happy to see DiffusionBee does talk the GPU in my late-model intel Mac, though for some reason it only uses 50% of its power right now. I'm sure the situation will improve as Metal 3.0 and Vulkan get more established.
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DL Frameworks in a nutshell
Won't it all come together with https://lets-unify.ai/ ?
- Unified Machine Learning
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[Discussion] Opinions on unify AI
What do you think about unify AI https://lets-unify.ai.
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