As the 2010’s draw to a close, it’s worth taking a look back at the monumental progress that has been made in Deep Learning in this decade.

This post is an overview of some the most influential Deep Learning papers of the last decade. My hope is to provide a jumping-off point into many disparate areas of Deep Learning by providing succinct and dense summaries that go slightly deeper than a surface level exposition, with many references to the relevant resources.

https://leogao.dev/2019/12/31/The-Decade-of-Deep-Learning/

In this piece, we’ll look at some of the top open source machine learning projects in 2019

​

https://heartbeat.fritz.ai/2019s-top-open-source-machine-learning-projects-3cd082a02f78

https://www.humblebundle.com/books/python-machine-learning-packt-books

i just saw this bundle on HumbeBundle is it worth buying or it’s just waste of money ?

Hello world!

I’m running a project requiring a significant amount of compute where it’ll be cheaper to buy RTX cards outright (+electricity) instead of running them on the cloud.

I’m looking for a rack-mountable option that can house 8 GPU (can settle for 6 GPUs+ 1 network card).

So far I’ve found:

• ASUS 4RU ESC8000 – excellent fit for a hefty price tag (AUD 10K) that I’d rather put toward the GPU.
• Mining cases/motherboard – very cheap but rubbish layout.
• A few online tutorials for building smaller systems but they are limited to < 4 GPU.

Other requirements:

• Dual PSU support, for obvious reason.
• Will need to allow a minimum 128GB of RAM on the motherboard, ideally 256GB+
• Ideally PCIe x16 Gen 4 but can settle with Gen 3.

I’ve also considered:

• Electricity: yes, I have 3-phase feed, won’t burn the house down 🙂
• Cooling – high powered split AC (for the small room where this live)
• Networking – second hand InfiniBand.

Now the big question:

Are you aware of there that fits this bill?

Budget: $1000-2000 for the case + motherboard. Probably another grand for the CPU (not too important), and however much the RAM are gonna cost, lolz.

Thanks in advance!

TL;DR: custom pip wheels for TF 2.0 / 2.1 for Py 3.7 / 3.8 and CUDA 10.1 / 10.2: https://github.com/inoryy/tensorflow-optimized-wheels

I’m sharing my pip wheels for TF built from source for some non-standard versions, notably Python 3.8 + CUDA 10.2 and Python 3.7 + CUDA 10.1, the latter is “compatible” with PyTorch 1.3 so you can have them share a single env.

The builds also enable various performance flags like XLA JIT support and modern CPU opt flags, including SIMD support (AVX2, SSE4, FMA). If you have a CPU released after ~2013 then you’ll likely benefit from these on e.g. data pre-processing. Though I should note that if you have Intel CPU then you might not see a large difference since now TF comes pre-built with MKL which can dispatch required intrinsics at runtime.

Finally, I’ve enabled additional compute capabilities support (5.0, 6.1, 7.0), which means these wheels should also work on older GPUs (7xx – 9xx families).

A week ago, I posted here that I have built a face detector to blur faces for videos with GitHub repository.

The entire project was based on OpenCV examples. So it’s obvious that the result can’t be good enough to use and slow.

So, I’ve been looking for better projects for face detection and one of the comments from my earlier post said using DeepPrivacy might be good. Sounds great, so I tried to run DeepPrivacy and I couldn’t run because of lack of GPU memory.

Okay…🤔 It looks like DeepPrivacy uses DSFD to detect faces. Shouldn’t I just use DSFD to detect faces and blur them?

There, I wrote the simple implementation of anonymizing videos by DSFD. https://github.com/JeiKeiLim/Anonymizing_video_by_DSFD

And I found lightDSFD which is a lightweight version of DSFD. So I also applied lightDSFD to anonymize videos. https://github.com/JeiKeiLim/Anonymizing_video_by_lightDSFD

​

However, lightDSFD looks less accurate(obviously). It works most of the time but it shows failure when a person is laying down or the light is too dark, etc.

So far, DSFD looks great that I feel like I can actually publish videos that I can’t upload until the faces are anonymized. The problem is that it takes 11 hours to complete for 15 minutes of 60fps video.

…. I am still waiting for the first video to be finished. And if my calculation is correct, all videos will be processed in 51 days.

What are some machine learning topics that are applicable to cancer, or more broadly, biology, besides diagnostics?

I’m planning to switch to JAX for DL in CV. But however still having doubts about its future. Anyone with insights?