Python

PyTorch vs TensorFlow for Your Python Deep Learning Project

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. […]

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Python

Flattening a List of Lists in Python

Sometimes, 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:    

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Artificial intelligence Leave a comment

LoftQ: Reimagining LLM fine-tuning with smarter initialization

This 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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Python

Python News: What’s New From April 2024

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 […]

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Artificial intelligence Leave a comment

Abstracts: May 6, 2024

MICHEL 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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Machine Learning

Random Search and Grid Search for Function Optimization

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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Machine Learning

Basin Hopping Optimization in Python

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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Machine Learning

XGBoost for Regression

Extreme Gradient Boosting (XGBoost) is an open-source library that provides an efficient and effective implementation of the gradient boosting algorithm. Shortly after its development and initial release, XGBoost became the go-to method and often the key component in winning solutions for a range of problems in machine learning competitions. Regression predictive modeling problems involve predicting a numerical value such as a dollar amount or a height. XGBoost can be used directly for regression predictive modeling. In this tutorial, you will […]

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Machine Learning

Develop a Neural Network for Banknote Authentication

It can be challenging to develop a neural network predictive model for a new dataset. One approach is to first inspect the dataset and develop ideas for what models might work, then explore the learning dynamics of simple models on the dataset, then finally develop and tune a model for the dataset with a robust test harness. This process can be used to develop effective neural network models for classification and regression predictive modeling problems. In this tutorial, you will […]

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