Introduction In recent years, the development of the field of computer science has attracted much attention, and pursuing a master’s degree in computer science has become a goal for many students. A Google Scholar search for keywords like computer vision and natural language processing yields hundreds of thousands of results. Among the many popular directions, … Read more
https://iamarookie.blog.csdn.net/article/details/117755839 Basic Operations in TensorFlow2 – Part 3 Merge and Split tf.concat tf.concat helps us perform concatenation operations. Format: tf.concat( values, axis, name='concat' ) Parameters: values: a tensor or tensor list – axis: the dimension to operate on – name: the name of the operation, defaults to “concat” Example: part_1 = tf.zeros([5, 3]) print(part_1) part_2 … Read more
Original Title: An Introduction to PyTorch – A Simple yet Powerful Deep Learning Library Author: FAIZAN SHAIKH Translator: He Zhonghua This article is about 3600 words, recommended reading time is 15 minutes.This article will guide you step by step through PyTorch with practical examples. Introduction Every once in a while, a Python library emerges that … Read more
Click on the above“Beginner’s Vision Learning” to selectStar or “Top” Important content delivered at the first time Recently, in a research project, I started to understand the importance of image augmentation techniques. The goal of the project is to train a robust generative model capable of reconstructing the original image. The problem being addressed is … Read more
Click on the above“Beginner’s Guide to Vision” to select and add a Bookmark or “Pin” Important content delivered promptly This article is reprinted from: AI Park Introduction While there are other ways to achieve the same results, these operations make usage more convenient. PyTorch is a scientific package based on Python that performs advanced operations … Read more
Click the above “Beginner’s Guide to Vision” to select and add a Bookmark or “Pin” Heavyweight content delivered first-hand This article introduces the last powerful engineering implementation model in the GBDT series—CatBoost. CatBoost is one of the three mainstream models under the GBDT framework, alongside XGBoost and LightGBM. CatBoost, open-sourced by the Russian search giant … Read more
Every algorithm has its applicable range, and understanding its pros and cons can help avoid errors caused by inappropriate use. This article summarizes the pros and cons of common machine learning algorithms for reference. The sources are from “Machine Learning: Using R, Tidyverse, and mlr” (Algorithms 1 to 17) and “Neural Networks: Implementation in R” … Read more
The benefit of using ensemble decision tree methods like gradient boosting is that they can automatically provide estimates of feature importance from trained predictive models. In this article, you will discover how to estimate the importance of features for predictive modeling problems using the XGBoost library in Python. After reading this article, you will know: … Read more
When I first started exploring machine learning algorithms, I was overwhelmed by all the mathematical content. I found that without fully understanding the intuition behind the algorithm, it was difficult to grasp the underlying mathematical principles. Therefore, I tend to favor explanations that break down the algorithm into simpler, more digestible steps. This is what … Read more
Introduction Reference code homepage, still the great Julia Silge’s code, who is also a main author of tidymodels. Overall process The original official tutorial URL: https://juliasilge.com/blog/xgboost-tune-volleyball/ Notes 1. Due to poor external data currently, the data used is the test data from the tidytuesdayR package. 2. Tidymodels is an integrated R language machine learning environment … Read more