I got a couple answers from other Kagglers also without a team either through the forum or reaching me with direct messages to get into the team. So far, I participated solo to all the challenges and I felt ready to join a team, and I published this post on the Kaggle forum, I was about 84th on the leaderboard by then. So to live my new passion I started another new challenge on Kaggle (the 6th to be precise) and it was that Quick, Draw! Doodle Recognition Challenge.

I am still a novice in this field, but I find it truly fascinating. Second, let’s explain the context: I love learning new things about software engineering, science, programming… and the artificial intelligence/machine learning is my latest learning passion for about a year now. This will have an immediate impact on handwriting recognition and its robust applications in areas including OCR (Optical Character Recognition), ASR (Automatic Speech Recognition) & NLP (Natural Language Processing).įirst, I really want to thank all my teammates, it hasn’t been possible without them! By advancing models on this dataset, Kagglers can improve pattern recognition solutions more broadly.

Your task is to build a better classifier for the existing Quick, Draw! dataset. You’ll need to build a recognizer that can effectively learn from this noisy data and perform well on a manually-labeled test set from a different distribution. Sounds fun, right? Here’s the challenge: since the training data comes from the game itself, drawings can be incomplete or may not match the label. That subset contains 50M drawings encompassing 340 label categories. The game generated more than 1B drawings, of which a subset was publicly released as the basis for this competition’s training set. The game prompts users to draw an image depicting a certain category, such as ”banana,” “table,” etc. “Quick, Draw!” was released as an experimental game to educate the public in a playful way about how AI works.

Quick doodle ideas