1 Algorithm Introduction From the name, we can see that the BP neural network can be divided into two parts: bp and neural network. Here, bp is the abbreviation for Back Propagation, which means reverse propagation. The BP network can learn and store a large number of input-output mapping relationships without needing to reveal the … Read more
Author: Chen Zhiyan This article is about 2000 words long and is recommended to be read in 5 minutes. This article will help you understand <strong>convolutional neural networks</strong>.
Source: 36 Big Data This article is 8000 words long and is recommended to be read in 12 minutes. This article organizes and introduces neural network architectures for you. As new neural network architectures emerge constantly, it is quite challenging to document all neural networks. Understanding all the networks represented by these abbreviations (DCIGN, IiLSTM, … Read more
Abstract The brain controls everything we do; it is more powerful than any computer. This complex organ continuously transmits and analyzes information through neurons, even while we sleep. Scientists are trying to create a “digital brain” by understanding the brain’s functions. Can computers achieve the functionality of the human brain? To make this happen, scientists … Read more
Click on the above “Beginner Learning Vision”, choose to add Star or “Top” Essential insights delivered at the first moment Contents: 1. Concept of Convolutional Neural Networks 2. Characteristics of Convolutional Neural Networks 2.1 Local Region Connections 2.2 Weight Sharing 2.3 Downsampling 3. Structure of Convolutional Neural Networks 3.1 Convolutional Layer 3.2 Pooling Layer 4. … Read more
Datawhale Insights Compiled by: Wang Xiaoxin, Source: Quantum Bit When adjusting the way models update weight and bias parameters, have you considered which optimization algorithm can yield better and faster results for the model? Should you use Gradient Descent, Stochastic Gradient Descent, or the Adam method? This article introduces the main differences between various optimization … Read more
1. Basic Principles of Neural Networks 1. Simple Principles of Biological Neural Networks In biological neural networks, each neuron’s dendrite receives electrical signals from multiple previous neurons, combining them into a stronger signal. If the combined signal is strong enough and exceeds the threshold, the neuron will be activated and will also send out a … Read more
First, let’s visually perceive the status of “Deep Learning”. The image below is a relationship diagram of AI, Machine Learning, and Deep Learning. The field of AI is relatively broad, Machine Learning is a subfield of AI, and Deep Learning is a subset within the Machine Learning domain. Deep learning algorithms have recently become increasingly … Read more
MLNLP community is a well-known machine learning and natural language processing community, covering domestic and international NLP graduate students, university teachers, and corporate researchers. The Vision of the Community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning at home and abroad, especially for beginners. … Read more
Introduction In 1997, computer scientist Wolfgang Maass proposed that networks composed of spiking neurons—spiking neural networks (SNN)—would become the “third generation of neural network models” following artificial neural networks. As one of the forefront intersections of neuroscience and artificial intelligence, the research on spiking neural networks starts from the biological rationale of neuron nodes and … Read more