Large text-to-video models trained on internet-scale data have shown extraordinary capabilities to generate high-fidelity films from arbitrarily written descriptions. However, fine-tuning a pretrained huge model might be prohibitively expensive, making it difficult to adapt these models...
Researchers have proposed a novel approach to enforcing distributional constraints in machine learning models using multi-marginal optimal transport. This approach is designed to be computationally efficient and allows for efficient computation of gradients during backpropagation.
Existing methods...
Artificial intelligence (AI) is primarily a math problem. We finally have enough data and processing capacity to take full advantage of deep neural networks,...
Scientific endeavors such as weather forecasting, climate modeling, analyzing new energy sources, drug discovery, training AIs, or running any form of large-scale simulation require...
In this tutorial, you will learn Python Logistic Regression. Here you’ll know what exactly is Logistic Regression and you'll also see an Example with...
Link to Part 1.
Dealing with Data
Motivation
Training deep learning models requires data... A lot of data! Unfortunately, in most cases data comes messy,...
Who thought in 1950’s that AI and deep learning will make self-driving cars and impossible missions like Mission Mars almost possible. While these innovations...
At present, the technology is evolving with focused on convenience; machines are being designed in a way that they can communicate with human beings...
Here is the list of some of the recommended deep learning books for reading:
1. Deep Learning by Goodfellow, Bengio, and Courville
The text offers mathematical...
Generally, when we think of blockchain technology, our first thoughts turn to the idea of finance, security, and cryptocurrency. Would many individuals don't realize...
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