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Digests » 139
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There are 70% more open roles at companies in data engineering as compared to data science. As we train the next generation of data and machine learning practitioners, let’s place more emphasis on engineering skills.
We ran a sweep of 8 different configurations of our training script and show that the Apple M1 offers impressive performance within reach of much more expensive and less energy efficient accelerators such as the Nvidia V100 for smaller architectures and datasets.
Image morphing without reference points by applying warp maps and optimizing over them.
Deep neural networks can perform wonderful feats thanks to their extremely large and complicated web of parameters. But their complexity is also their curse: The innerworkings of neural networks are often a mystery—even to their creators. This is a challenge that has been troubling the artificial intelligence community since deep learning started to become popular in the early 2010s.
Humans understand the world by perceiving and fusing information from multiple channels, such as images viewed by the eyes, voices heard by the ears, and other forms of sensory input. One of the core aspirations in AI is to develop algorithms that endow computers with a similar ability: to effectively learn from multimodal data like vision-language to make sense of the world around us. For example, vision-language (VL) systems allow searching the relevant images for a text query (or vice versa) and describing the content of an image using natural language.