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Aug 24, 2020 · Why Kaggle? I started using Kaggle seriously a couple of months ago when I joined the SIIM-ISIC Melanoma Classification Competition.. The initial reason, I think, was that I wanted a serious way to test my Machine Learning (ML) and Deep Learning (DL) skills.

Feb 12, 2017 · Quora recently announced the first public dataset that they ever released. It includes 404351 question pairs with a label column indicating if they are duplicate or not. In this post, I like to investigate this dataset and at least propose a baseline method with deep learning. Beside the proposed method, it includes some examples showing how to use […]
Started 5 big data companies, Kaggle competition champion: Internet deep learning misunderstanding-spend great efforts on those things with little influence. Author: Gregory Piatetsky, KDnuggets. I conducted an exclusive interview with Jeremy Howard, a data scientist known as a "rock star".
In past years, deep convolutional neural networks (DCNN) have achieved big successes in image classification and object detection, as demonstrated on ImageNet in academic field. However, There are some unique practical challenges remain for real-world image recognition applications, e.g., small size of the objects, imbalanced data distributions ...
Kaggle is a platform where you can learn a lot about machine learning with Python and R, do data science projects, and (this is the most fun part) join machine learning competitions. Competitions are changed and updated over time. Currently, “ Titanic: Machine Learning from Disaster ” is “ the beginner’s competition ” on the platform.
Evaluation, Machine Learning Competitions ACM RecSys Challenge Workshop 2020, Online 1 INTRODUCTION Deep Learning (DL) has become the method of choice in many ar-eas of applied machine learning, and recommender systems are no exception. The main machine learning problem in the area of recom-
Evaluation, Machine Learning Competitions ACM RecSys Challenge Workshop 2020, Online 1 INTRODUCTION Deep Learning (DL) has become the method of choice in many ar-eas of applied machine learning, and recommender systems are no exception. The main machine learning problem in the area of recom-
Sep 17, 2014 · This blog is for describing the winning solution of the Kaggle Higgs competition. It has the public score of 3.75+ and the private score of 3.73+ which has ranked at 26th. This solution uses a single classifier with some feature work from basic high-school physics plus a few advanced but calculable physical features. Github link to…
Jul 18, 2017 · Kaggle, a popular platform for data science competitions, can be intimidating for beginners to get into. After all, some of the listed competitions have over $1,000,000 prize pools and hundreds of competitors. Top teams boast decades of combined experience, tackling ambitious problems such as improving airport security or analyzing satellite data.
Jun 18, 2015 · The Titanic Competition on Kaggle. MATLAB is no stranger to competition - the MATLAB Programming Contest continued for over a decade. When it comes to data science competitions, Kaggle is currently one of the most popular destinations and it offers a number of "Getting Started 101" projects you can try before you take on a real one.
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.
I am passionate about solving unstructured and non-standard mathematical problems. I develop and fit Deep Learning, Statistical, and Optimization models. I would love to work on your projects. If you have any questions beforehand, please feel free to contact me. Skills: PyTorch, Keras, Tensorflow, Theano, Numpy, Pandas, SymPy, Matplotlib, SQL
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  • Deep Learning / Participant of Kaggle Competitions https://kaggle.com. Sep 2018 – Present 2 years 1 month. House Prices: Advanced Regression Techniques:
  • Deep Learning / Participant of Kaggle Competitions https://kaggle.com. Sep 2018 – Present 2 years 1 month. House Prices: Advanced Regression Techniques:
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  • In this talk, we will review modern rendering techniques and discuss how deep learning can extend the gamut of this long-lasting research topic. We will investigate deep neural networks as 1) plug-and-play sub-modules that reduce the cost of physically-based rendering; 2) end-to-end pipelines that inspire novel graphics applications.
  • The process of making Kaggle kernel and Using Kaggle Dataset; Building Classification model using Keras; Some Image Preprocessing methods; This Crash course Assumes that you have basic knowledge about. Python programming languages; Deep Learning Basics. Keras & Tensorflow. In this class, we will use the FER2013 Dataset that you can get from here

Deep Learning. Twitter Facebook はてブ Pocket LINE コピー. 2020.12.30. KaggleのNotebookでTPUを使っていたときに表題のエラー。 ...

Nov 23, 2020 · Kaggle is a competition site which provides problems to solve or questions to ask while providing the datasets for training your data science model and testing the model results against a test dataset. The Titanic competition is probably the first competition you will come across on Kaggle.
Aug 07, 2017 · “a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem” where in this case the ‘relatedness’ of the problem is that both the Kaggle competition and the pre-trained model(s) are addressing computer vision problems. When Kaggle made their ill-advised decision to focus 100% on the oil and gas analytics business, there was no reason for me to stay - and I had been dying to spend more time researching into how deep learning can make a difference to society.

Kaggle's Grasp and Lift EEG Detection Competition 28 Nov 2015. I recently participated in Kaggle’s Grasp-and-Lift EEG Detection, as part of team Tokoloshe (Hendrik Weideman and Julienne LaChance). None of the team members had ever used deep learning for EEG data, and so we were eager to see how well techniques that are generally applied to problems in computer vision and natural language processing would generalize to this new domain.

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I found some kernels in Kaggle using huge networks like VGG for this competition. It has a whopping 130 million parameters! Well, for 32×32 images, a small convolution network with as little as 360,000 parameters will do the job. We are gonna build a model that's 360 times smaller than the VGG and achieve the feat of 99.9% test accuracy.