What’re some good datasets for image classification projects other than the MNISTs out there. I’m still a beginner, but I’m looking for a challenging project, that I’ll spend a lot of time tuning, to push me closer to that intermediate stage.

Appreciate any suggestions and thank you

https://twitter.com/SchoolOfAIOffic/status/1185499979521150976

Wow, just when you thought it wouldn’t get any worse for Siraj lol

Chip Huyen gives a comprehensive overview of some of the most important evaluation metrics in language modeling.

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https://thegradient.pub/understanding-evaluation-metrics-for-language-models/

Deep Learning approach to identify herpesvirus and adenovirus infected cells in the absence of virus specific markers. This was possible based on DNA labelling alone. https://youtu.be/xdAzdkzPF-A

https://www.biorxiv.org/content/10.1101/798074v1

Since our recent release of Transformers (previously known as pytorch-pretrained-BERT and pytorch-transformers), we’ve been working on a comparison between the implementation of our models in PyTorch and in TensorFlow.

We’ve released a detailed report where we benchmark each of the architectures hosted on our repository (BERT, GPT-2, DistilBERT, …) in PyTorch with and without TorchScript, and in TensorFlow with and without XLA. We benchmark them for inference and the results are visible in the following spreadsheet.

We would love to hear your thoughts on the process.

Tried to evolve sequence using an algorithm which is the combination of both Genetic Algorithm and Reinforcement Learning. The aim of the project was to evolve SMILES chemical molecules from scratch.

Github: https://github.com/Gananath/NERD

Blog: https://gananath.github.io/nerd.html

https://raw.githubusercontent.com/Gananath/gananath.github.io/master/images/nerd.jpg

Problem 1: Lots of places suck… they’re simultaneously losing ecology, soil, habitability, jobs, profitability and carbon. We’ve got effective, profitable methodologies which could greatly improve many of these ecosystems, yet our scarce allocation of restoration resources, time, and willpower compels project designs with higher IRL effectiveness per effort.

Problem 2: Disaster-response-ecological-restoration benefits immensely from rapid analysis and planing yet designers are booked, expensive and slow.

Solution: An AI tool which triages options and creates effective ecology restoration designs and plans, quickly.

My Questions: About how much time and money may it take ML professionals to build and train a working version of this AI that’s worth having regenerative design professionals use as a tool?

What should it be called?

Is this a realistic candidate project for a paid programming challenge?

What are your thoughts?

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Here’s more details.

Example of Hypothetical AI Results: Perhaps after training, the AI identifies some part of the Mojave Desert in Southern California as being the best place in the world to restore. It suggests we buy this barren 300 acre parcel of land called “Rancho Desertification AF” that has problems with flashfloods, less-and-less ecology and supports zero jobs. It suggests that we then make 400 yards of compost from local rice straw and food waste that’s inoculated with indigenous microorganisms and biochar, that we use equipment to dig large amounts of big swales on contour and that we spread the compost, compost tea, native grass seeds and trees in the swales. That we drip irrigate the trees temporarily while we wait for rain which it forecasts to be in the late summer monsoon season. That the swales will recharge the groundwater in the expected flashflood allowing the grasses and trees to grow, creating hundreds of acres of relative oasis. That we’d do planned holistic grazing the following year with cattle (using short duration, high intensity stocking that’s dynamically modified by herders) who would eat the grass creating more compost, better microorganisms and more mulch. That this would create lots of jobs, profit, biodiversity, bio-productivity and we’d have the option of selling the ranch at 300-1000% profit in 1.5-3 years to a holistic planned grazing rancher or to continue ranching it ourselves. It estimates this project would reduce GHG emissions by say 3,000 tons of carbon annually, which may also be worth $20,000 on the Nori carbon removal marketplace. That this project would provide jobs to 20 people for initial construction and 10 people for herding, climate-solution tourism and eco-tourism.

Ecosystem Training/Evaluation Data Types: Topographical, LiDAR, Forestry, Ecology, Climate, Rainfall, Wind, Geology, Soil Chemistry, Soil Microbiology, Local Industries, Local Population, Local Economics, Local Politics, Local and Global Funding Available, NORI carbon removal marketplace, Grants Available, Tax Breaks Available, Labor Available, Comet-Farm, Current Real Estate Prices, Real Estate Projections, Stuff I haven’t figured out yet

Methodology Training/Evaluation Data Types: Data from software like Granular which farmers who get paid per ton of carbon sequestered use. Organic farming ERP data. Data used by the authors of high impact journal articles on agroecology, conservation, ecological restoration, and organic agriculture. Data used by environmental agencies, and large conservation NGOs. Experimental data generated in-house as needed. Stuff I haven’t figured out yet

Draft Program Training Parameters (these are how the AI would compare location and restoration design options): Increase Biodiversity, Increase Bioproductivity, Increase Human Wellness, Increase Ecosystem and Human Resilience, Improve Groundwater Levels, Reduce Disaster Risks, Stabilize Rainfall, Improve Downwind Clouds, Lower Cost, Lower Negative Externalities, Lower Greenhouse Gas Emissions, Reduce Endangered Species Losses, Lower Risk of Implementation Failure or Unintended Consequences, Increase Scalability, Increase Profitability, Decrease Implementation Time etc.

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Thank you!

Is anyone aware of any efforts underway to extend Google’s Cloud Vision to include support for older (say 1500-1900 AD) manuscripts? I have seen how some libraries are digitizing their collections and thought it would be awesome to try to use the Handwriting OCR on them but my attempts have had less then stellar results so far. I figure that the current model hasn’t been trained on older handwriting and would be very much interested in seeing if I could get involved in a community or effort that is focused on this. Anyone have any recommendations?