Machine Learning Jokes

 xkcd machine learning joke https://xkcd.com/1838/

Generative Adversarial Networks (GANs) 

Startup office tech company interior design inspiration

 Headspace office has an open amphitheater for group meditation and meeting, improving happiness, wellness.

What does it take to be a Software Engineer or Machine Learning Engineer at Quora?

 Quora is a place where people go ask questions, and get relevant answers / tips back from experts, the crowd, people with experiences... What does it take to be an engineer at Quora? This recruiting flyer tells us what it really takes to be a part of the Quora engineering team. Pro (paid) members can access this info card . Follow us for more job post analysis, career growth suggestions like this http://ml.learn-to-code.co/.

Cool GPT-3 Demos

 GPT-3 Discussion on clubhouse

OpenAI DALL-E

https://openai.com/blog/dall-e/

Next Rembrandt - Generated AI - Uniqtech curated coolest AI demo series

This is a state-of-art collaboration among researchers and technologists at ING, Microsoft, Tu Delft ... Rembrandt Museum.  This is a perfect example of AI generated fine art. Unlike hobbyist generated pictures, and unlike prototype experimental art generation, this is high fidelity, fine grained, HD generated fine art,  meticulously 3D printed, which is hard to achieve. In this case, it is executed to perfection. Title: Next Rembrandt - Generated AI - Uniqtech curated coolest AI demo series. Original source citation : see URL link.

Developer stickers perks for winter interns

 A warm welcome to our Winter 2020 Machine Learning Interns. It's a fantastic Stanford group.

Machine learning versus traditional programming vs Deep Learning

In traditional programming, developers give computer explicit instructions in procedural top down scripts and or via control flow statements that can “jump around” as opposed to top-down. Machine learning and deep learning is about supplying well-known, proven algorithms with cleaned, feature selected and or feature engineered data, as well as corresponding labels for the data (in unsupervised learning, only data is supplied), the algorithm leverage loss calculation, metrics, and optimizer to update parameters such as weights and coefficients in the algorithm. Finally these learned weights and coefficients are used along with the algorithm for prediction.

The more high quality data the better.

The biggest difference is: developers give specific instructions in traditional programming, and in machine learning and deep learning algorithms learn parameters based on data and loss function rather than rules.

Not having to write all the rules has benefits especially when the rules are hard to encode or program. The final product is also more robust, less likely to fail because it is not a strict rule based program.

Deep learning uses many layers of neural networks, hence the word deep. It is usually consisted of weight-learning layers or neural networks. Neural networks stacked together, have the unique capability of being universal function approximation - representing complex functions without explicitly coding them.