AI Digest - The modern mathematics of deep learning
#156 — May 17, 2021 | View in browser |
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The modern mathematics of deep learning
We describe the new field of mathematical analysis of deep learning. This field emerged around a list of research questions that were not answered within the classical framework of learning theory. These questions concern: the outstanding generalization power of overparametrized neural networks, the role of depth in deep architectures, the apparent absence of the curse of dimensionality, the surprisingly successful optimization performance despite the non-convexity of the problem, understanding what features are learned, why deep architectures perform exceptionally well in physical problems, and which fine aspects of an architecture affect the behavior of a learning task in which way.
Evaluate NER parsers with spaCy and Label Studio
This tutorial helps you evaluate accuracy of Named Entity Recognition (NER) taggers using Label Studio. Gather predictions from standard spaCY language models for a dataset based on transcripts from the podcast This American Life, then use Label Studio to correct the transcripts and determine which model performed better to focus future retraining efforts.
Good data Scientist, bad data scientist
At their core, data scientists exist to create business value with data. The ways in which value is generated will vary largely company to company, and even team to team. For teams that are early in their lifecycle, this will often take the form of building up basic analytical foundations. The team is likely blind to aspects of the product performance, user behaviour or total addressable market.
Diffusion models beat GANs on image synthesis
We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations.
Enhancing photorealism enhancement
We present an approach to enhancing the realism of synthetic images. The images are enhanced by a convolutional network that leverages intermediate representations produced by conventional rendering pipelines. The network is trained via a novel adversarial objective, which provides strong supervision at multiple perceptual levels. We analyze scene layout distributions in commonly used datasets and find that they differ in important ways. We hypothesize that this is one of the causes of strong artifacts that can be observed in the results of many prior methods.
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Practical SQL for data analysis
Sunday, May 9, 2021
And more news, tutorials and articles about AI, machine learning, and data science in this week's issue. #155 — May 10, 2021 View in browser AI Digest Spread the word, build the community, share
Why AI is harder than we think
Sunday, May 2, 2021
And more news, tutorials and articles about AI, machine learning, and data science in this week's issue. #154 — May 03, 2021 View in browser AI Digest Spread the word, build the community, share
Unsupervised 3D neural rendering of Minecraft worlds
Sunday, April 25, 2021
And more news, tutorials and articles about AI, machine learning, and data science in this week's issue. #153 — April 26, 2021 View in browser AI Digest Spread the word, build the community, share
CPU algorithm trains deep neural nets up to 15 times faster than top GPU trainers
Monday, April 19, 2021
And more news, tutorials and articles about AI, machine learning, and data science in this week's issue. #152 — April 19, 2021 View in browser AI Digest Spread the word, build the community, share
How I built a €25K machine learning rig
Sunday, April 11, 2021
And more news, tutorials and articles about AI, machine learning, and data science in this week's issue. #151 — April 12, 2021 View in browser AI Digest Spread the word, build the community, share
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