deeplearning.stanford.eduDeep Learning
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Deeplearning.stanford.edu is a subdomain of stanford.edu,
which was created on 1985-10-04,making it 39 years ago.
It has several subdomains, such as cs229.stanford.edu
republicofletters.stanford.edu , among others.
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deeplearning.stanford.edu Information
| HomePage size: 3.318 KB |
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| Website IP Address: 171.64.68.10 |
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deeplearning.stanford.edu PopUrls
| stanfordmlgroup/nlm-noising http://deeplearning.stanford.edu/noising |
| Deep Learning http://deeplearning.stanford.edu/ |
| Unsupervised Feature Learning and Deep Learning Tutorial http://deeplearning.stanford.edu/tutorial/ |
| Lexicon-Free Conversational Speech Recognition with ... http://deeplearning.stanford.edu/lexfree/ |
| UFLDL Tutorial, Linear Regression http://deeplearning.stanford.edu/tutorial/supervised/LinearRegression/ |
| Multi-Layer Neural Network http://deeplearning.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/ |
| Feature Extraction Using Convolution http://deeplearning.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/ |
| Exercise: Convolution and Pooling http://deeplearning.stanford.edu/tutorial/supervised/ExerciseConvolutionAndPooling/ |
| Optimization: Stochastic Gradient Descent http://deeplearning.stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent/ |
| Logistic Regression http://deeplearning.stanford.edu/tutorial/supervised/LogisticRegression/ |
deeplearning.stanford.edu Httpheader
| Date: Tue, 14 May 2024 05:04:31 GMT |
| Server: Apache/2.2.15 (CentOS) |
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deeplearning.stanford.edu Ip Information
| Ip Country: United States |
| City Name: Hayward |
| Latitude: 37.6736 |
| Longitude: -122.0944 |
deeplearning.stanford.edu Html To Plain Text
Computer Science Department , Stanford University Home People Papers Sponsor Contact is a rapidly growing area of machine learning. To learn more, check out our deep learning tutorial . (There is also an older version , which has also been translated into Chinese ; we recommend however that you use the new version .) Machine learning has seen numerous successes, but applying learning algorithms today often means spending a long time hand-engineering the input feature representation. This is true for many problems in vision, audio, NLP, robotics, and other areas. To address this, researchers have developed deep learning algorithms that automatically learn a good representation for the input. These algorithms are today enabling many groups to achieve ground-breaking results in vision, speech, language, robotics, and other areas. Our deep learning tutorial will teach you how to apply these algorithms to your own...
deeplearning.stanford.edu Whois
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