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
This article first summarizes the development history of large model technology, the iterative path of autonomous driving models, and the role of large models in the autonomous driving industry. Next, it details the basic definition, fundamental functions, and key technologies of large models, especially the Transformer attention mechanism and the pre-training-fine-tuning paradigm. The article also … Read more
This article is a bit long, and I will divide it into several parts. Through this article, I hope to help you thoroughly understand the principles of RNN (Recurrent Neural Networks) and be able to implement it at the code level. Table of Contents What This Article Does Inputs and Outputs of RNN RNN Network … Read more
CNN and RNN are the two most commonly used deep learning network structures in deep learning, and some students may still be unclear about the differences between these two networks. Now let’s illustrate the specific applications of CNN and RNN with a diagram: One to One: This represents the scenario of the CNN network, from … Read more
In the field of deep learning, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Deep Neural Networks (DNN) are the three most widely researched and applied neural network models. They each have their own focus in terms of structure, function, and applicable scenarios, complementing each other. This article will provide a detailed analysis of … Read more
Table of Contents 1. Neuron Model 2. Perceptron and Neural Networks 3. Error Backpropagation Algorithm 4. Common Neural Network Models 5. Deep Learning 6. References Currently, deep learning (Deep Learning, referred to as DL) is exceptionally popular in the field of algorithms, not only in the internet and artificial intelligence but also in various fields … Read more
In the field of computer science, artificial intelligence refers to the behavior of machines that can respond to their environment in a way similar to human intelligence and improve their probability of achieving goals as much as possible. Artificial intelligence can generally be divided into three categories: Weak Artificial Intelligence Strong Artificial Intelligence Super Artificial … Read more