Photo by Michael Schiffer on Unsplash

Convolutional Neural Networks are a class of deep learning networks that can be used for high-complexity problems which many industries face, and they even push against limiting bounds of human possibility—from detection of terminal medical conditions from magnetic resonance imaging, to allowing for the possibility of fully autonomous vehicles, and even allowing for the advance of brain-computer interfaces.

With such mind-bending possibilities, imagining where to begin with an approach to a solution is crushingly perplexing. Understanding the capabilities of these sorts of networks, however, can allow one to follow the logical processes in approaching deeply complex problems. …


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Researchers at NVIDIA just developed a new technology that will take video streaming compression, manipulation, and deepfakes to a new standard. Dubbed One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing, the GAN-based model will be presented at the 2021 Conference on Computer Vision and Pattern Recognition that takes place in June.


Photo by Sam Moqadam on Unsplash

On December 3rd, Luminar Technologies, a company that develops sensors and software for passenger vehicles and trucks, went public.

The Autonomy Solutions segment of the company designs, manufactures, and sells LiDAR(light detection and ranging) sensors, and the related perception and autonomy software solutions necessary in utilizing such sensors. This technology is likely to be critical in the progress towards automated vehicles (despite the dismissal of this fact by some other large players in this space), and for many reasons, may be one of the most important quality of life improvements to come in the next several decades.

For those of…


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Just days in to hands-on learning data manipulation with Pandas, my instructor paused to make a point. “Do yourself a favor,” he said to the class, with more intention than ever before, “before going too much further in learning Pandas, watch this talk on vectorization.”

The value of vectorization seemed apparent, both from our instructor’s affect when he was directing us to the clip, and from the claim that the presenter in the clip was suggesting—vectorize your code to manipulate data 1000 times faster. The video breaks down several examples of using a variety of manipulation operations—Python for-loops, NumPy array…

Mike Flanagan

Data Science Student, Flatiron School.

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