Learn computer vision fundamentals with the famous MNIST data
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地址：https://www.kaggle.com/c/digit-recognizer
| 翻译项目原地址 | 作者 | 本库地址 | 翻译后版本 |
|---|---|---|---|
| introduction-to-cnn-keras-0-997-top-6 | Yassine Ghouzam | GO | GO |
| welcome-to-deep-learning-cnn-99 | Peter Grenholm | GO | GO |
| --- | --- | --- | --- |
Start here if... You have some experience with R or Python and machine learning basics, but you’re new to computer vision. This competition is the perfect introduction to techniques like neural networks using a classic dataset including pre-extracted features.
Competition Description MNIST ("Modified National Institute of Standards and Technology") is the de facto “hello world” dataset of computer vision. Since its release in 1999, this classic dataset of handwritten images has served as the basis for benchmarking classification algorithms. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike.
In this competition, your goal is to correctly identify digits from a dataset of tens of thousands of handwritten images. We’ve curated a set of tutorial-style kernels which cover everything from regression to neural networks. We encourage you to experiment with different algorithms to learn first-hand what works well and how techniques compare.
Practice Skills Computer vision fundamentals including simple neural networks
Classification methods such as SVM and K-nearest neighbors
Acknowledgements
More details about the dataset, including algorithms that have been tried on it and their levels of success, can be found at http://yann.lecun.com/exdb/mnist/index.html. The dataset is made available under a Creative Commons Attribution-Share Alike 3.0 license.