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Dec 09, 2020 · As time passed by, I was learning more and more about Data Science and Machine Learning and that allowed me to improve my performance in competitions. I really like learning new things and I think in Data Science that is a very necessary condition to succeed. AV: You’ve participated in over 100 competitions, this is a huge number.

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3.17. Kaggle의 주택 가격 예측하기¶. 앞 절들에서 딥 네트워크를 만들고 차원과 가중치 감쇠(weight decay) 그리고 드롭아웃(dropout)을 사용해서 용량을 제어하는 다양한 기본적인 도구들을 소개했습니다.

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Nov 10, 2019 · Based on my own observations, this used to be true up to the end of 2016/start of 2017 but isn’t the case anymore. In fact, in addition to XGBoost [1], competitors also use other gradient boosting [2] libraries: lightgbm [3] is the most popular on...

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Nov 22, 2019 · Kaggle is one of the most well-known platforms for Data Science competitions, which offers a chance to compete on some of the most intriguing machine learning problems. People of myriad experiences take part in these competitions. Some people do extremely well and go on to achieve the title of Kaggle Grandmasters.

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In the "Diabetic Retinopathy: Blindness Detection" Kaggle competition, the task was to train a computer vision model to detect diabetic retinopathy using a large set of retina images (in order to...

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Kaggle is the most famous platform for Data Science competitions. Taking part in such competitions allows you to work with real-world datasets, explore various machine learning problems, compete with other participants and, finally, get invaluable hands-on experience.

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DeepLearningMovies. Kaggle's competition for using Google's word2vec package for sentiment analysis. Installation. There're some requirements for making the stuff work.

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This is my first weblog and I'm very pleased to share my machine learning & deep learning experience while deciphering the solution for a Kaggle competition. Though it was a late submission to Kaggle, my learning journey through the analysis was also very intuitive, entertaining, and challenging.

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Aug 12, 2020 · Imagine if you could get all the tips and tricks you need to tackle a binary classification problem on Kaggle or anywhere else. I have gone over 10 Kaggle competitions including: Toxic Comment Classification Challenge $35,000; TalkingData AdTracking Fraud Detection Challenge $25,000; IEEE-CI S Fraud Detection $20,000

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- Kaggle is a good way to practice machine learning, data analysis skills - Kaggle’s goal: Turn data into prediction results - Data dictionary is important for understanding the data set

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May 29, 2019 · In today’s post, Neha Goel is going to share an overview about how you can use MATLAB and Simulink for Developing Artificial Intelligent components in your competitions. Neha joined the team recently and focuses on deep learning and data science competitions. Also find a section in this post where Neha reveals what projects she is currently working on. -- Competitions and students
description This is the in-class competition of the deep learning course at HEC Montreal. In this competition, we consider scientific papers from different domains. The goal is to predict the domain of test papers based on their titles and their references.
Dec 17, 2018 · Try as many Kaggle competitions as you can, take as many online courses as you have the time for, or try to implement as many small projects as possible. You will most likely “fail” in one way or another at most of them, but make sure that you learn from all of those mistakes.
A Robotics, Computer Vision and Machine Learning lab by Nikolay Falaleev. The main focus of the blog is Self-Driving Car Technology and Deep Learning.
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Learning from Kaggle Masters. David is a PhD student at The Technical University of Denmark in the Cognitive Systems department. Before that he was a masters student and the title of his thesis was “Concepts in Predictive Machine Learning“. You would know it from the title, but it is a great thesis.
Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.