https://github.com/beegass/cs-541-deep-learning
CS-541 Deep Learning is a graduate class that teaches both a theoretical and practical approach to deep learning. You will be able to see this in the different homework files in the form of workable code that can be tested as well as proofs and explanations as to where the code is coming from.
Science Score: 23.0%
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Low similarity (5.8%) to scientific vocabulary
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CS-541 Deep Learning is a graduate class that teaches both a theoretical and practical approach to deep learning. You will be able to see this in the different homework files in the form of workable code that can be tested as well as proofs and explanations as to where the code is coming from.
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README.md
CS-541-Deep_Learning
Course Textbook: Deep Learning Book
Recommended Additional Textbook: - Dive Into Deep Learning - Deep Learning Book - Pattern Recognition and Machine Learning
Reading 1: - Chapters 1 In Deep Learning Book - Chapters 2 In Deep Learning Book - Chapters 5 In Deep Learning Book
Reading 2: - Chapters 3 In Deep Learning Book - Chapters 6 In Deep Learning Book - Additional Helpful Reading - From Autoencoder to Beta-VAE
Reading 3: - Auto-Encoding Variational Bayes Paper - Tutorial on Variational Autoencoders
Generic Readings That Should Be Done As You Need - Fashion-MNIST with tf.Keras - Understanding LSTM Networks - Derivation Of Softmax Function
Assignment 1
If you dont want to download the file you can run it HERE in your browser
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- Name: Bryan
- Login: BeeGass
- Kind: user
- Location: Cambridge, MA
- Company: @USArmyResearchLab
- Website: onlygass.dev
- Twitter: BeeAGass
- Repositories: 14
- Profile: https://github.com/BeeGass
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