Why are tree-based machine learning methods, such as XGBoost and random forests, superior to deep learning on tabular data? This article provides reasons behind this phenomenon, selecting 45 open datasets and defining a new benchmark to compare tree-based models with deep models, summarizing three reasons to explain this phenomenon. Deep learning has made significant progress … Read more
Source: DeepHub IMBA This article is about 3800 words, and it is recommended to read in 5 minutes. Since the release of diffusion models, the attention and papers on GANs have decreased significantly, but some ideas within them are still worth understanding and learning. Therefore, in this article, we will implement SN-GAN using PyTorch. Spectral … Read more
You can “listen” to this article anytime on your mobile phone or computer Edge browser. Key Points: Generative AI, based on predictive models, can accurately perceive numbers, possess absolute mathematical knowledge and undeniable logic, and tirelessly reason to derive the best or optimal output based on current prompts. The tight connection between logic and the … Read more
Click on the above “Beginner’s Guide to Vision”, select to add “Bookmark” or “Pin” Important content delivered immediately From | Blog Garden Author | CZiFan Background Geoffrey Hinton is one of the pioneers of deep learning and the inventor of classic algorithms for neural networks like backpropagation. He and his team proposed a novel neural … Read more
Author: zzq https://zhuanlan.zhihu.com/p/68411179 This article is authorized, and unauthorized reproduction is not allowed. Introduction to Basic Components of CNN 1. Local Receptive Field In images, the connections between local pixels are relatively tight, while the connections between distant pixels are weaker. Therefore, each neuron does not need to perceive the entire image globally; it only … Read more
Click on the above “Beginners Learn Vision”, select to add “Star” or “Top” Important content delivered at the first time Convolutional Neural Networks (CNN) are fundamental knowledge in deep learning. This article provides a detailed interpretation of the basic principles of CNN and common CNN architectures, and introduces the process of building deep networks with … Read more
Recommended Reading Time: 8min~13min Reason for Recommendation: This is a summary and reflection after watching Professor Li Hongyi’s deep learning videos from National Taiwan University. After finishing the introduction of the first part, particularly the introduction to RNN and especially LSTM, I felt enlightened. 1 0. Starting with RNN Recurrent Neural Network (RNN) is a … Read more
Author: Shivam Bansal Translated by: Shen Libin Proofread by: Ding Nanya This article is approximately 2300 words, and is recommended to be read in 8 minutes. This article will detail the text classification problem and implement this process in Python. Introduction Text classification is a common natural language processing task in business problems, with the … Read more
Compiled by | Jiang Changzhi Today, I would like to introduce an article published in Nature Methods by a team from ETH Zurich and Friedrich Schiller University Jena. The article proposes a new method for generating small molecule structures from mass spectrometry based on an encoder-decoder neural network: MSNovelist. It first uses SIRIUS and CSI: … Read more
Figure1. Deep Learning Mind Map Introduction The concept of deep learning can be traced back to the field of cybernetics between 1940 and 1960. It later developed into connectionism during the 1980s and 1990s, with the third wave of development beginning in 2006 with the expansion of artificial neural networks, evolving into the highly popular … Read more