Deep Learning, NLP, NMT, AI, ML
This research was presented as a demonstration at the 40th IEEE International Conference on Data Engineering (opens in new tab) (ICDE 2024), one of the premier conferences on data and information engineering. Since the inception of cloud computing, autoscaling has been an essential technique for optimizing resources and performance. By
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From the prompt to the picture, Stable Diffusion is a pipeline with many components and parameters. All these components working together creates the output. If a component behave differently, the output will change. Therefore, a bad setting can easily ruin your picture. In this post, you will see: How the different components of the Stable Diffusion pipeline affects your output How to find the best configuration to help you generate a high quality picture Let’s get started. How to Use […]
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PyTorch vs TensorFlow: What’s the difference? Both are open-source Python libraries that use graphs to perform numerical computations on data in deep learning applications. Both are used extensively in academic research and commercial code. Both are extended by a variety of APIs, cloud computing platforms, and model repositories. If they’re so similar, then how do you decide which one is best for your project? You’ll start by taking a close look at both platforms, beginning with the slightly older TensorFlow. […]
Read moreThis research paper was presented at the 12th International Conference on Learning Representations (opens in new tab) (ICLR 2024), the premier conference dedicated to the advancement of deep learning. Large language models (LLMs) rely on complex internal mechanisms that require more memory than what is typically available to operate on standard devices. One such
Read moreSometimes, when you’re working with data, you may have the data as a list of nested lists. A common operation is to flatten this data into a one-dimensional list in Python. Flattening a list involves converting a multidimensional list, such as a matrix, into a one-dimensional list. In this video course, you’ll learn how to do that in Python. What’s Included: Downloadable Resources:
Read moreThis research paper was presented at the 12th International Conference on Learning Representations (opens in new tab) (ICLR 2024), the premier conference dedicated to the advancement of deep learning. Large language models (LLMs) use extensive datasets and advanced algorithms to generate nuanced, context-sensitive content. However, their development requires substantial computational resources. To address this, we developed LoftQ, an innovative technique that streamlines the fine-tuning process—which is used to
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In April 2024, Python’s core development team released versions 3.13.0a6 and 3.12.3 of the language! The former received several exciting features, improvements, and optimizations, while the latter got more than 300 commits for security improvements and bug fixes. The 3.13.0a6 release is the last alpha release. In the first half of May, the code will be frozen and won’t accept new features. Note that 3.13.0a6 is a pre-release, so you shouldn’t use it for production environments. However, it provides a […]
Read moreMICHEL GALLEY: Thank you for having me. HUIZINGA: So I like to start with a distillation or sort of an elevator pitch of your research. Tell us in just a couple sentences what problem or issue your paper addresses and why we should care about it. GALLEY: So this paper is about evaluating large foundation models. So it’s a very important part of researching large language models because it’s a good way to evaluate, kind of, the capabilities—what these models […]
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Function optimization requires the selection of an algorithm to efficiently sample the search space and locate a good or best solution. There are many algorithms to choose from, although it is important to establish a baseline for what types of solutions are feasible or possible for a problem. This can be achieved using a naive optimization algorithm, such as a random search or a grid search. The results achieved by a naive optimization algorithm are computationally efficient to generate and […]
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Basin hopping is a global optimization algorithm. It was developed to solve problems in chemical physics, although it is an effective algorithm suited for nonlinear objective functions with multiple optima. In this tutorial, you will discover the basin hopping global optimization algorithm. After completing this tutorial, you will know: Basin hopping optimization is a global optimization that uses random perturbations to jump basins, and a local search algorithm to optimize each basin. How to use the basin hopping optimization algorithm […]
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