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CZII - CryoET Object Identification #3 Baseline YOLO11 Solution

This post is an annotation of baseline YOLO11 solution kernel from @SERGIO ALVAREZ.https://www.kaggle.com/code/sersasj/czii-yolo11-submission-baseline-with-kdtree-update CZII YOLO11 Submission Baseline with KDTree UpdateExplore and run machine learning code with Kaggle Notebooks | Using data from multiple data sourceswww.kaggle.comCZII YOLO11 Submission Baseline with KDTree Update - LB 0.682Insp..

CZII - CryoET Object Identification #2 Baseline UNet Solution

This post is an annotation of baseline unet solution kernel from "fnands".https://www.kaggle.com/code/ldywinner/baseline-unet-train-submit/notebook#Baseline-UNet-training-+-prediction/submission'Baseline UNet train + submit' - LB score 0.529Literally a baseline solution with no high lb scoreBased on 3 notebooks:https://www.kaggle.com/code/ahsuna123/3d-u-net-training-onlyhttps://www.kaggle.com/co..

This post is an annotation of training data code kernel from "fnands".https://www.kaggle.com/code/fnands/create-numpy-dataset-exp-name Create Numpy dataset exp nameExplore and run machine learning code with Kaggle Notebooks | Using data from CZII - CryoET Object Identificationwww.kaggle.comKernel 'Create Numpy dataset exp name'Overall this kernel is about PREPARING TRAINING DATA!pip install git+..

코인 단타를 치기 위해 배워야 하는 것들바닥을 잘 잡는 법차트, 거래량, 호가창을 계속 스캔하락이 약해졌다 느껴지는 순간 바로 매수바닥을 잘못 잡았을 때 탈출(물타기)하는 법좋은 코인 (상승할만한 코인)을 찾는 법가상화폐 트레이딩의 기본트레이딩은 확률 싸움이다.다시 말해, 100%란 존재하지 않는다.조금 더 높은 확률의 선택지를 찾으려고 노력하는 것일 뿐이다.실제로 유명하다는 기법들 (피보나치 되돌림, 엘리엇 파동 등등)을 열심히 공부한 뒤 차트에 디입시켜보면 맞는 구간도 있지만 틀리는 구간도 상당히 많다."성공적인 투자는 내가 산 금액보다 비싸게 남이 사주는 것"이라는 투자의 기본 원칙에 따라 당연한 말이기도 하다.주의점많은 초보자들은 이런 투자의 기본 원칙을 잊고 특정 기법에 빠져서 그 기법을 모든 ..

This post heavily relies on this lecture: 개발자를 위한 ChatGPT 프롬프트 엔지니어링2시간 이내에 이 안내 프로젝트를 완료하세요. 채팅 상자를 넘어서세요. API 액세스를 사용하여 자체 애플리케이션에 LLM을 활용하고 맞춤형 챗봇을 구축하는 방법을 배워보세요. 개발자를 위한 Cwww.coursera.orgTwo types of LLMsBase LLMPredicts next word based on text training dataInstruction Tuned LLMTries to follow instructionsFine-tune on instructions and good attempts at following those instructionsOften furth..

BackgroundTransformer, introduced by Google in 2017 for natural language processing, is a language model that's leading innovation in the AI field. ChatGPT, which first enabled us to use AI through web and API interfaces, is also based on Transformer, as are the language models that companies like Google and Facebook are developing as competitors.Transformer is expected to achieve state-of-the-a..

What is Zero-shot prompting?In artificial intelligence, a "shot" refers to an example.Therefore, zero-shot means the AI processing a new task without examples, in other words, handling tasks it hasn't specifically learned.Zero-shot prompting refers to how AI like ChatGPT processes responses to prompts without specifically trained data or example answers.Simply put, AI performs the requested task..

What is Few-shot Prompting?In artifical intelligence, a "shot" refers to an exampleTherefore, Few-shot means a few examples.Few-shot prompting is a method that helps AI models better understand and perform new tasks by providing a small number of examples when the model needs to perform a new task.Few-shot prompting is broadly divided into:Instructions: Description of the task the model needs to..

Child Mind Institute — Problematic Internet UseRelating Physical Activity to Problematic Internet Usewww.kaggle.comAbout the CompetitionThe aim of this competition is to develop a model that predicts problematic internet usage levels based on physical activity and health data from children and adolescents.Since the current method of measuring problematic internet use requires complex expert eval..

Twelveth competition following Youhan Lee's curriculum. Natural Language Processing competition.First Kernel: Mercari Interactive EDA + Topic ModellingEDA kernel with matplotlib.Insight / Summary:1. Log transformation on target varOur response or target variables is the price we are suggesting to the Mercari's marketplace sellers. The median price of all the items in the training is about $267 b..

Fourteenth(Last) course following Youhan Lee's curriculum. Not competition.First Kernel: Novice to GrandmasterThe biggest problem that we might face is fake and bogus responses. As it is a survey, not everyone will answer with proper credentials, and thus I assume that there will be a lot many outlier. Second Kernel: What do Kagglers say about Data Science ?EDA Kernel with trying some prediction..

Thirteenth competition following Youhan Lee's curriculum. Natural Language Processing competition.First Kernel: [For Beginners] Tackling Toxic Using KerasKernel using keras LSTM.Insight / Summary:1. Checking null valuestrain.isnull().any(),test.isnull().any() 2. Tokenizationlist_tokenized_train = tokenizer.texts_to_sequences(list_sentences_train)list_tokenized_test = tokenizer.texts_to_sequences..

Eleventh competition following Youhan Lee's curriculum. Natural language processing competition. Spooky Author IdentificationShare code and discuss insights to identify horror authors from their writingswww.kaggle.comFirst Kernel: Spooky NLP and Topic Modelling tutorialTopic modeling: the process in which we try uncover abstract themes or "topics" based on the underlying documents and words in a..

Tenth competition following Youhan Lee's curriculum. Anomaly detection competition using tabular data. Credit Card Fraud DetectionAnonymized credit card transactions labeled as fraudulent or genuinewww.kaggle.comFirst Kernel: In depth skewed data classif. (93% recall acc now)Testing different methods on skewed data.The idea is to compare if preprocessing techniques work better when there is an o..

Nineth competition following Youhan Lee's curriculum. Regression competition using tabular data. Zillow Prize: Zillow’s Home Value Prediction (Zestimate)Can you improve the algorithm that changed the world of real estate?www.kaggle.comFirst Kernel: Simple Exploration Notebook - Zillow PrizeEDA kernel focused on univariate correlation analysis.Insight / Summary:1. Removing outliersulimit = np.per..