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Sneha Nanavati

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Behavioral Representation Learning from Animal Poses.

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ASCII-rendered single-player dungeon crawl game

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5 Puzzles 21 Days. Can you solve it all?

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Measure sample efficiency and generalization in reinforcement learning using procedurally generated environments

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5 Puzzles 21 Days. Can you solve it all?

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Self-driving RL on DeepRacer cars - From simulation to real world

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3D Seismic Image Interpretation by Machine Learning

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5 Puzzles 21 Days. Can you solve it all?

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5 Puzzles 21 Days. Can you solve it all?

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5 Puzzles 21 Days. Can you solve it all?

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Multi-Agent Reinforcement Learning on Trains

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A benchmark for image-based food recognition

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Sample-efficient reinforcement learning in Minecraft

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5 Puzzles, 3 Weeks. Can you solve them all? 😉

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Multi-agent RL in game environment. Train your Derklings, creatures with a neural network brain, to fight for you!

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Predicting smell of molecular compounds

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5 Problems 21 Days. Can you solve it all?

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5 Puzzles 21 Days. Can you solve it all?

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5 Puzzles, 3 Weeks | Can you solve them all?

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Grouping/Sorting players into their respective teams

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5 Problems 15 Days. Can you solve it all?

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5 PROBLEMS 3 WEEKS. CAN YOU SOLVE THEM ALL?

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Remove Smoke from Image

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Classify Rotation of F1 Cars

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Can you classify Research Papers into different categories ?

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Can you dock a spacecraft to ISS ?

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Multi-Agent Reinforcement Learning on Trains

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Multi-Class Object Detection on Road Scene Images

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Detect Mask From Faces

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Identify Words from silent video inputs.

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Multi Agent Behavior Challenge 2022

Travel Grant for CVPR Workshop

18 days ago

We are excited to offer a limited number of travel grants to students and postdocs to attend CVPR and Multi-Agent Behavior workshop. Each award will cover at least $500 towards travel and meeting attendance costs (larger grants may be available to those travelling longer distances.)

You will get a chance to meet a panel of experts from various disciplines and discuss the key goals of multi-agent behavior research. CVPR is the premier annual computer vision & pattern recognition event regarded as one of the most important conferences in its field.

:airplane: How To Apply For Travel Grant

Apply for travel grants by submitting an extended abstract or paper to Round 2, and we will review travel grant applications submitted between now and May 27th on a rolling basis.

:alarm_clock: Timeline

Round 2 Submission deadline: May 27th, midnight AoE
Submit at: Conference Management Toolkit - Login

Travel grants with submissions reviewed on a rolling basis.
Final Decisions: June 3rd
Camera-ready Deadline: June 10th

:books: Read More Details on the CVPR Workshop & Travel Grant


Just a quick reminder, we’ve extended Round-2 deadline by one and a half months after receiving your feedback that the current timeline was too short to work with video data.

[Name], now with the new deadline of 3rd July, 2022, we look forward to seeing your unique submission.

:writing_hand: Don’t miss the chance to present your work at CVPR workshop by participating in Round-2.

Make Your Submission For Round-2 :green_book:

[Round-1 Winners] Here Are The Winners For Round-1

About 1 month ago

:wave:t3: Hello AIcrowd,

As Round-1 of Multi-Agent Behavior Challenge 2022 comes to an end, let’s shine a spotlight on the winners of the Mouse Triplet Task and Fruit Fly Task.

Round-1 saw 224 participants making 700+ submissions. We thank you all for your participation. Here are the winners of Round-1 of Multi-Agent Behavior Challenge 2022. :clap:

:honeybee: Fruit Fly Winners

:trophy: Leaderboard Ranks

:1st_place_medal: Zac Partridge
:2nd_place_medal: Team DIG111
:3rd_place_medal: Param Uttarwar

:mouse: Mouse Triplet Winners

:trophy: Leaderboard Ranks

:1st_place_medal: Param Uttarwar
:2nd_place_medal: Team Jerry Mouse
:3rd_place_medal: Zac Partridge


:notebook: Winning Solutions

@edhayes1 from :2nd_place_medal: Team Jerry Mouse shared his solution with the participants. Click here to learn from his solution that won second place for :mouse: Mouse Triplet Task.

@Zac the :1st_place_medal: first-place winner for :honeybee: Fruit Fly Task shares the casual overview of his approach over here:

"I used a Bert style encoder, treating handcrafted features as the tokens and performing masked language modelling. Initially, every frame of key points is converted into a large number of handcrafted features representing angles, distances and velocities between body parts within an animal as well as features from each animal to all others. For the flies, I had 2222 features and 456 for the mice (but would use more in the future, especially for the mice) and these features are all normalised.

I’ll refer to the neural network as having a head, body and tail where the head and tail act on the single frame level and the body acts on the sequence level. The head is a two-layer fully connected network that reduces the input features down to the target dimension size (256 or 128). The body does the “language modelling” part - input is 512 partially masked tokens (masked before the head) and output is a sequence of the same shape (batch_size, seq_length, 128 or 256) but now hopefully includes higher-level sequence features. I used huggingface’s perceiver model for this - Perceiver.

The tail is a single linear layer and can be thought of in two parts, original unmasked features reconstruction and predicting any known labels so for example that would be of size 458 for the mice. The loss function I was using was mean square error for reconstructing the features and cross-entropy loss for the (non-nan) labels where the mean square error loss was weighted approximately 10 times more." Stayed tuned for a more in-depth breakdown of his solution.


:hourglass_flowing_sand: What Next? Participate In Round-2

In the new round, you’ll be given two sets of raw overhead videos and tracking data. As in Round 1, we ask you to submit a frame-by-frame representation of the dataset. We hope this video dataset will inspire you to try new ideas and see how much incorporation of information from video improves your ability to represent animal behaviors! Explore the sub-tasks to know more :point_down:

:mouse: Mouse Triplet Video Data
:ant: Ant & Beetle Video Data

The end goal remains the same, create a representation that captures behavior and generalizes well in any downstream task.

:trophy: $6000 USD cash prize pool for the two subtasks
:spiral_calendar: Round-2 runs till May 20th, 2022, 23:59 UTC
:medal_sports: Claim up to $400 AWS credits in Round-2
:writing_hand: Submit Your Solution to CVPR Workshop 2022

[Round-2 Update] $400 AWS Credits Per Team - How To Win & Claim Them

About 1 month ago

:wave: Hello there,

As mentioned in the Round-2 announcement for Multi-Agent Behavior Challenge participants who make a submission in round-2 can win up to $400 AWS credits.

:raised_hand_with_fingers_splayed: Keep reading to see how you can claim AWS credits for yourself or for your team :point_down:

:white_check_mark: Eligibility

  • You (or your team) must have received scores higher than the below-mentioned scores in any of the 2 tasks.
  • On the Ant & Beetle Video Data task, you must beat a mean F1 score of 0.606. Here’s the baseline for the Ant & Beetle Task, which achieves a mean F1 score of 0.591.
  • On the Mouse Triplet Video Data task, you must beat a mean F1 score of 0.306. Here’s the baseline for the Mouse Triplet Task, which achieves a mean F1 score of 0.292.
  • Each participant (or team) can claim a $200 credit code for each target beaten (up to $400 total.)

Please note:

  • We will be sharing 50 X $200 = $10,000 worth of AWS Credits. The distribution will happen on a first come first serve basis and will cease once all the 50 codes (each for $200) are distributed.
  • For teams claiming the code, please nominate one person to represent. The code will be mailed to that team member.

:computer: How to redeem?

  • Please share the following as a reply to this thread to receive your code.

Team name (if relevant):

Submission id (the one that beats baseline as defined above):

How much did you improve over the relevant baseline score?:

A brief intro about you (Us and the participants would love to know what brought you to this challenge):

:shield: We will be sending out the AWS credits every week after verifying the details. After receiving your code, you can go to this website and claim your credits.

If you haven’t gone through the baselines, check them out here :point_down:

  1. :mouse2: Mouse Triplet Video Task Baseline

  2. :ant: Ant Beetle Video Task Baseline

All the best! :+1:

[Challenge Announcement] New Round, Dataset & Prizes

About 2 months ago

:wave:t3: Hello there

The new round of Multi-Agent Behavior Challenge 2022 is live!

As the Round 1 of this very unique & exciting challenge comes to an end, we are happy to share the new prizes, dataset and deadline for Round 2! :sparkles:

So, What’s New In Round-2?

Everything! In this round you’ll be given a dataset of raw overhead videos and tracking data. Rather than being asked to detect a specific behavior of interest, we ask you to submit a frame-by-frame representation of the dataset. This video dataset is unique will inspire you to try new ideas that they could not on just the tracking data. Explore the sub-tasks to know more :arrow_down:

:mouse: Mouse Triplet Video Data

:ant: Ant & Beetle Video Data

The end goal remains the same, create a representation that captures behavior and generalizes well in any downstream task.

:trophy: $6000 USD cash prize pool for the two subtasks.
:spiral_calendar: Round-2 runs till May 20 th, 2022, 23:59 UTC.
:medal_sports:Claim upto $400 AWS credits in Round-2


:notebook: Resources For Round-2

:owl:To help you get started with the two new tasks, we have created an easy to follow starter kit for Mouse Triplet Video Task & Ant Tripler Video Task.

These notebooks will help you perform initial data exploration and a basic embedding using a vision model.

  1. Mouse Triplet Video Task Starter Kit

  2. Ant Tripler Video Task Starter Kit

:video_camera: Bonus: If you want additional information on Round-2 dataset and embedding evaluations**,** watch this video.


:trophy: Prizes For Round-2

:money_with_wings: There is a $6000 cash prize pool for the new round. For top positions on the leaderboard, each task carries a $3000 cash prize pool.

:1st_place_medal: 1st on the leaderboard: $1500 USD
:2nd_place_medal: 2nd on the leaderboard: $1000 USD
:3rd_place_medal: 3rd on the leaderboard: $500 USD

:medal_sports:Bonus: Teams that beat the advanced baseline can stand to win $400 AWS credits. Details on the score to beat and the advanced baseline will be announced soon. !:stopwatch:


:fly: Chance To Present At CVPR 2022 Workshop

:writing_hand: Top participants and teams will be invited to speak at CVPR 2022 Workshop. You will get a chance to meet a panel of experts from a variety of disciplines and discuss the key goals of multi-agent behavior research. CVPR is the premier annual computer vision & pattern recognition event regarded as one of the most important conferences in its field.

Submit your model and solution as a paper to the MABe Workshop Poster session. Get a chance to present your work at the CVPR workshop by participating in Round-2.

📹 Town Hall Recording & Resources from top participants

About 2 months ago

940ed7d915178b858bc0e68e41c677a31a3b1238_2_517x161

:rotating_light: Check out the latest updates, datasets and baselines for Multi-Agent Behaviour Challenge.
:play_or_pause_button: Watch the Town Hall Here :arrow_right:

:raising_hand_woman: What does this town hall include?

Challenge organiser, Dr Ann Kennedy from Northwestern University, presented the overview of round 1 of the Multi-Agent Behavior Challenge. She also discussed the embedding evaluations.

Andrew Ulmer from Kennedy Lab @ Northwestern University presented the tVAE baselines.

Jennifer Sun from Caltech presented the details of Round-2 of the challenge. She also previewed the new dataset.

:envelope_with_arrow: Participate Now: AIcrowd | Multi Agent Behavior Challenge 2022 | Challenges

Watch the Town Hall 🎥 Multi-Agent Behaviour Challenge Town Hall | How to use AI to study animal movements. - YouTube

[Live Event] 🏛 Multi-Agent Behavior Challenge 2022 Town Hall

2 months ago

:classical_building:Join the Multi-Agent Behavior Challenge Town Hall

:love_letter: You are invited to join Multi-Agent Behavior Challenge 2022 Town Hall event on 03 April Sunday, 3 PM UTC.

:clapper: The town hall will feature challenge organisers, Dr Ann Kennedy & Jennifer Sun, from Northwestern University & Caltech. They will discuss details about the new round, preview the dataset and present new baselines.

:classical_building: Multi-Agent Behavior Challenge 2022 Town Hall
:spiral_calendar: 03 April Sunday, 3 PM UTC

:owl: Since Round-1 of Multi-Agent Behavior Challenge 2022 is extended to 11th April, this event will help you improve your solutions. Round 2 of the challenge will also be announced soon. Get details about the upcoming datasets and evaluation metrics.

:people_holding_hands: Share your feedback and experience from Round 1 with challenge organisers.

:timer_clock: Make the most of the new deadline by getting the latest challenge updates, baseline overview and preview of the new dataset.

Register For The Multi-Agent Behavior Challenge Town Hall


:face_with_monocle: Why Should You Join The Town Hall?

:woman_scientist: Challenge organiser, Dr Ann Kennedy from Northwestern University, will present the overview of round 1 of the Multi-Agent Behavior Challenge. She will also discuss the embedding evaluations.

:woman_technologist: Jennifer Sun from Caltech will present the details of Round-2 of the challenge. She will also preview the new dataset.

:scientist: Andrew Ulmer from Kennedy Lab @ Northwestern University will present the tVAE baselines.

If you want to get the latest updates, improve your solution or discuss new ideas with other participants, don’t miss this Town Hall event this Sunday.

:raising_hand_woman: Got a burning question? Get it answered by challenge organisers and the AIcrowd research team during the event. :speech_balloon: Drop your questions and query before the event on this post.

:wave:t3: Meet your fellow challenge participants IRL. This is your chance to discuss new ideas and approaches with other AIcrowd community members.

:classical_building:Join the Multi-Agent Behavior Challenge Town Hall

📚[New Baselines] Trajectory Variational Autoencoder baseline

2 months ago

:notebook: Checkout The New Baselines

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:owl: This repository contains jupyter notebooks that implement a trajectory variational autoencoder (tVAE) baseline model for embedding the mouse and fly trajectory datasets for the 2022 Multi-Agent Behavior Challenge.

Performance of these notebooks is as follows:

Dataset Mean F1 Mean MSE task F1 1 task F1 2 task F1 3 task F1 4 task F1 5
Mouse trajectories 0.121 0.095 0.339 0.479 0.021 0.491 x
Fly trajectories 0.291 x 0.0 0.0 0.0 0.388 0.539

Where the “task F1” values are F1 scores on specific sample evaluation tasks. Note, while this baseline is outperforming PCA for the mice, it actually does significantly worse than PCA for the flies! (Why? Who knows!)


How to use these notebooks

Clone this repository and open train_mouse_tvae.ipynb or train_fly_tvae.ipynb in a jupyter notebook session. Follow notebook instructions to make your own submission, then play with the model architecture and parameters inside the tvae directory to see if you are able to improve performance!

Note, you’ll need to download the mouse and/or fly datasets into the provided mouse_data and fly_data directories to use this code.


Got questions about the new baseline? Drop them in the comments below :point_down:

🗓 [Challenge Update] Round 1 Deadline Extension

2 months ago

Hello all! :wave:

Hope you are enjoying the Multi-Agent Behavior Challenge 2022. This is a very exciting problem and we are glad to have you on board! We understand that this unique challenge might require some extra time so we are extending the deadline for Round 1.

New Round-1 Deadline

The deadline for Round-1 submissions for two subtasks Fruit Fly Groups and Mouse Triplets is extended to April 11th, 2022, 12:00 UTC.

:trophy: There is a $6000 USD cash prize pool for the two subtasks.
:calendar: The new deadline for the first round is April 11th, 2022, 12:00 UTC.

You can find the notebook for Fruit Fly Groups over here. Notebook for Mouse Triplets can be found here.

Update with submission page

Detailed individual task scores are now available on the submissions page. This will help you identify & understand which ideas improved different scores.

Have any more questions? Comment below and we’ll answer.
You can always post your questions and issues on the challenge forum :smiley:


:link: Handy Links

:muscle: Challenge Page | :loudspeaker: Share your feedback with us
:radio: Join Community Slack Channel | :notebook: Discussions on Starter Kit

Lip Reading

[Suggestion Box 📥 ] What AI puzzle do you want to solve?

21 days ago

Hello you :wave:

At AIcrowd, we have a very creative team working on new AI puzzles for you to solve. They set the problem, brew the dataset and create solution notebooks.

What kind of puzzles do you want to solve next?

Suggest puzzle ideas or new topics we should explore. Comment on the types of puzzles you’ll like to see. Want to collaborate with us to create a unique AI puzzle? Drop an email at blitz@aicrowd.com.

AIcrowd Blitz is for you. So help us make it better by creating problems you want to solve!

Happy Blitzing
Team AIcrowd

[Feedback Corner 🗣] We Want To Hear Your Thoughts

21 days ago

Hello you,

We have built Blitz through extensive user interviews and in-depth surveys of existing participants. We want AIcrowd Blitz to be a comprehensive AI companion. Blitz is a place where you can learn, solve and show your AI skillset.

We want to make Blitz better for you. We want to hear your unfiltered, honest thoughts on Blitz. You can also answer these questions (either through comments below or by email at blitz@aicrowd.com).

  1. What is one thing you love about Blitz?
  2. What is one thing you immediately wish to change about Blitz?
  3. How would you rate your Blitz experience so far? (Out of 5, where 5 is excellent and 1 is poor)
  • This can be the user experience, quality of puzzles and solutions and overall ease of subscription and access to Blitz.
  1. How would you rate Blitz AI puzzles? (Out of 5, where 5 is excellent and 1 is poor)
  • This can be about the puzzle’s difficulty, the quality of the dataset or the puzzle content.
  1. How would you rate Blitz AI solutions? (Out of 5, where 5 is excellent and 1 is poor)
  • This can be based on ease of understanding, explanation quality, and implementation ease.
  1. How easy was it to deploy your first ML App? (Out of 5 where 5 is very easy and 1 is very hard)
  • This can be based on the ease of implementation and sharing of the app.
  1. Would you recommend Blitz to a friend? If not, why?
  2. What is one item we should improve?

We have tried to incorporate features that will accelerate your AI learning. We have also polished & perfected the things you love about Blitz.

Like you, we love learning AI & solving new problems to build our skills. So we want to hear your thoughts on AIcrowd Blitz. Please share your honest feedback to help us provide the best AI platform to you. We are always available on @AIcrowdHQ on Twitter and blitz@aicrowd.com.

Happy Blitzing
Team AIcrowd

[New Launch] AIcrowd Blitz Is Now Live

21 days ago

:wave: Hello you,

For the last 1.5 years, we have been developing unique AI puzzles and sharing them with you every month. The next phase of the evolution of AI Blitz is here!

AIcrowd Blitz is a comprehensive AI Learning companion with unique AI puzzles, expert solutions, your own AI community & most excitingly, an impressive ML portfolio.

AI Puzzles You Love + Easy to understand Expert Solutions

Blitz boasts a library of hand-crafted AI puzzles designed around home-brewed datasets on real-world problems. Signup and get these unique AI puzzles delivered straight to your inbox.

Learn As You Go

Unlock solutions and resources created by experts for beginners. Learn different approaches to solve AI puzzles, understand the theory and gain hands-on experience.

Build & Share Your ML Portfolio
Create AI Solutions For The World To See

Don’t just sharpen your skills; show them to the world! Through Blitz ML Apps, make your solutions accessible to all. Host live ML Apps for anyone to use in real-time. With Blitz, you can easily build a stellar ML Portfolio.

Meet Your AI Tribe

We know learning is hard, so we never want you to feel alone in your AI journey. Through Blitz, meet like-minded AI enthusiasts that will inspire & help you. Get exclusive access to live problem-solving events and 24*7 community support.

Like you, we love learning AI & solving new problems to build our skills. So we want to hear your thoughts on AIcrowd Blitz. Please share your honest feedback to help us provide the best AI platform to you.

Happy Blitzing
Team AIcrowd

Data Purchasing Challenge 2022

[Announcement] Leaderboard Winners

About 1 month ago

As the Round-2 of Data Purchasing Challenge 2022 comes to an end, let’s shine a spotlight on the winners.

This challenge saw 300+ participants making 2200+ submissions. We thank you all for your participation. Here are the winners of Data Purchasing Challenge 2022. :clap:

:trophy: Leaderboard Winners

:1st_place_medal: xiaozhou_wang USD 6,000
:2nd_place_medal: sergey_zlobin USD 4,500
:3rd_place_medal:ArtemVoronov USD 3,000

Community Contribution prize winners are being finalised and will be announced soon, stay tuned.

[Resources] 🗂 Notebooks Created By Community for Round 2

2 months ago

:wave: Hello,

Data Purchasing Challenge Round 2 is going strong :muscle:t3: To help you make the most of the challenge, we have released a new baseline containing fast heuristic implementations of some simple ideas.

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Need some new ideas? Check out these excellent resources created by the community :arrow_down:

:bulb: Under the spotlight: Helpful resources created by challenge participants

If these notebooks and explainers help you, don’t forget to :heart: the notebook and leave a comment.

  1. Representation Learning: In his notebook, aorhan explains how Representation Learning can be utilised for data label purchase aka Active Learning (AL). You can find the complete explainer over here.

  2. Explainability: How does your model actually learn? Another notebook by aorhan, explain how given a sample you can identify what features contribute to the decision. And how you can improve your model. Read the notebook over here.

  3. Purchase with data anamoly: In this notebook, moto shows how to use anomaly scores to select images to buy. His notebook explains the approach and the result of his experiments.

  4. Labels co-occurence & Image Similarity: In this explainer santiactics performs label co-occurence analysis & Image Similarity using image embeddings. Read the notebook over here.

  5. Sneak peek into the image sample of Round 2: Taking a data visualisation approach sagar_rathod notebook visualises images from different classes and combinations of them. Complete notebook over here.

  6. :movie_camera: Additional Resources from Challenge Organisers and Top Participants: We recently hosted a live Town Hall event where the organisers and participants shared their ideas. You can find the recording & resource compilation over here.

:hourglass_flowing_sand:Don’t forget to submit your own notebook or resource for the Community Contribution Prize.

:raising_hand_woman: Do you have questions about the notebooks & baseline? Drop a comment on this thread to them answered quickly. What approach will you try? Let us know :arrow_down_small:

AIcrowd Blitz ⚡

🙋‍♀️ Got questions about AIcrowd Blitz? Get them answered ⬇️

About 2 months ago

:wave: Hi there,

AIcrowd Blitz is your perfect AI learning companion.

:computer: Solve AI Puzzles, Build ML Apps.
:woman_technologist: Grow Your Skills With The Community. Show Your Work To The World.

:sparkles: Be the first to participate in AIcrowd Blitz. Join the waitlist now! :point_left:


:raising_hand_woman: Have questions about AIcrowd Blitz?

Comment your questions in the thread below or email blitz@aicrowd.com :email:
Get your queries on waitlist, launch, features & more answered quickly. :speech_balloon:
Drop your questions in the comments below :point_down:

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