- KDnuggets™ News 21:n29, Aug 4: GitHub Copilot Open Source Alternatives; 3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks - Aug 4, 2021.
GitHub Copilot Open Source Alternatives; 3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks; A Brief Introduction to the Concept of Data; MLOps Best Practices; GPU-Powered Data Science (NOT Deep Learning) with RAPIDS
- GitHub Copilot: Your AI pair programmer – what is all the fuss about? - Jul 5, 2021.
GitHub just released Copilot, a code completion tool on steroids dubbed your "AI pair programmer." Read more about it, and see what all the fuss is about.
- Adversarial generation of extreme samples - Feb 2, 2021.
In order to mitigate risks when modelling extreme events, it is vital to be able to generate a wide range of extreme, and realistic, scenarios. Researchers from the National University of Singapore and IIT Bombay have developed an approach to do just that.
- Deep Learning for Virtual Try On Clothes – Challenges and Opportunities - Oct 16, 2020.
Learn about the experiments by MobiDev for transferring 2D clothing items onto the image of a person. As part of their efforts to bring AR and AI technologies into virtual fitting room development, they review the deep learning algorithms and architecture under development and the current state of results.
- Markov Chains: How to Train Text Generation to Write Like George R. R. Martin - Nov 29, 2019.
Read this article on training Markov chains to generate George R. R. Martin style text.
- When Too Likely Human Means Not Human: Detecting Automatically Generated Text - May 23, 2019.
Passably-human automated text generation is a reality. How do we best go about detecting it? As it turns out, being too predictably human may actually be a reasonably good indicator of not being human at all.
- Graduating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own - Apr 25, 2019.
Read how generative adversarial networks (GANs) research and evaluation has developed then implement your own GAN to generate handwritten digits.
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- More Deep Learning “Magic”: Paintings to photos, horses to zebras, and more amazing image-to-image translation - Apr 17, 2017.
This is an introduction to recent research which presents an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples.
- Top /r/MachineLearning Posts, September: Open Images Dataset; Whopping Deep Learning Grant; Advanced ML Courseware - Oct 7, 2016.
Google Research announces the Open Images dataset; Canadian Government Deep Learning Research grant; DeepMind: WaveNet - A Generative Model for Raw Audio; Machine Learning in a Year - From total noob to using it at work; Phd-level machine learning courses; xkcd: Linear Regression
- MNIST Generative Adversarial Model in Keras - Jul 19, 2016.
This post discusses and demonstrates the implementation of a generative adversarial network in Keras, using the MNIST dataset.
- Are Deep Neural Networks Creative? - May 12, 2016.
Deep neural networks routinely generate images and synthesize text. But does this amount to creativity? Can we reasonably claim that deep learning produces art?
- Deep Learning Transcends the Bag of Words - Dec 7, 2015.
Generative RNNs are now widely popular, many modeling text at the character level and typically using unsupervised approach. Here we show how to generate contextually relevant sentences and explain recent work that does it successfully.