Case Introduction This case will demonstrate how to use Recurrent Neural Networks (RNNs) for time series prediction. Specifically, we will use RNNs to predict the future values of a variable that depends on its own historical values. In this case, we will use a temperature dataset. We will provide the temperature values from the past … Read more
Author: Victor Zhou Translator: Wang Yutong Proofreader: Wu Jindi This article is about 3800 words, recommended reading time is 15 minutes. This article will introduce the basics of Recurrent Neural Networks (Vanilla RNNs), how they work, and how to implement them in Python. Recurrent Neural Networks (RNNs) are a type of neural network specifically designed … Read more
Author: Debarko De Source: Hackernoon, Machine Heart What is a Recurrent Neural Network (RNN)? How do they work? Where can they be used? This article attempts to answer these questions and also showcases an RNN implementation demo that you can expand upon as needed. RNN Architecture Basic knowledge. Familiarity with Python and CNN is essential. … Read more
With the increasing popularity of Artificial Intelligence (AI) technology, various algorithms play a key role in driving the development of this field. From linear regression predicting housing prices to neural networks powering self-driving cars, these algorithms silently support the operation of countless applications. Today, we will give you a glimpse of these popular AI algorithms … Read more
Click on the above“Visual Learning for Beginners” to selectStar or Pin. Important content delivered at the first moment 1. Brief Overview of Various Optimization Algorithms Stochastic Gradient Update: For standard SGD, I won’t elaborate. The main point to note is that mini-batch gradient descent is commonly used in deep learning. Momentum Update: This method can … Read more
Since the concept of deep learning was proposed in 2006, almost 20 years have passed. As a revolution in the field of artificial intelligence, deep learning has given rise to many influential algorithms. So, what do you think are the top 10 deep learning algorithms? Here are my top 10 deep learning algorithms, which hold … Read more
Click the above“Beginner Learning Vision” to select “Star” or “Pin” Heavyweight content delivered first-hand Introduction In recent years, there has been increasing interest in extending deep learning methods to graphs. Driven by multiple factors, researchers have drawn on ideas from convolutional networks, recurrent networks, and deep autoencoders to define and design neural network structures for … 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
Madio.net Mathematics China ///Editor: Only tulips’ garden Neural networks are a clever combination of linear and nonlinear modules. When we wisely choose and connect them, we have a powerful tool to approximate any mathematical function. For example, using nonlinear decision boundaries for classification. The backpropagation technique is responsible for updating the trainable parameters. Although it … Read more
Click the "Beginner's Visual Learning" above, select "Star" or "Top" Heavy content delivered to you first Researchers proposed the concept of CNN (Convolutional Neural Networks) while studying image processing algorithms. Traditional fully connected networks are a black box – they take all inputs and pass each value through a dense network, then to a hot … Read more