Data Visualization

A Comprehensive Guide to Machine Learning Model Visualization

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A Comprehensive Guide to Machine Learning Model Visualization

Originally from Data Analysis and Applications Machine learning models have powerful and complex mathematical structures. Understanding their intricate workings is an important aspect of model development. Model visualization is crucial for gaining insights, making informed decisions, and effectively communicating results. In this article, we will delve into the art of machine learning visualization, exploring various … Read more

Traditional Chinese Medicine Knowledge Graph System

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Traditional Chinese Medicine Knowledge Graph System

Traditional Chinese Medicine Knowledge Graph System Author: Liu Di What Is a Knowledge Graph? The introduction of “Knowledge Graph” from Baidu Encyclopedia states: A knowledge graph is a series of various different graphics that display the development process and structural relationships of knowledge, visualizing knowledge resources and their carriers, and mining, analyzing, constructing, drawing, and … Read more

Step-By-Step Tutorial on GIS Knowledge Graph

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Step-By-Step Tutorial on GIS Knowledge Graph

Content Guide Read Before, I had training on knowledge graph products, and I found it relatively easy to get started. So today, I asked a colleague to write a step-by-step tutorial. Software Used: GeoScene Pro 4.0 (requires Knowledge authorization) This article will popularize the basic concepts and principles of knowledge graph technology while briefly introducing … Read more

Simple Time Series Prediction Using XGBoost (Python)

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Simple Time Series Prediction Using XGBoost (Python)

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import xgboost as xgb from sklearn.metrics import mean_squared_error color_pal = sns.color_palette() plt.style.use('fivethirtyeight') df = pd.read_csv('AAPL Hourly.csv') df = df[['timestamp', 'close']] df = df.set_index('timestamp') df.plot(style='.', figsize=(15, 5), color=color_pal[0], title='stock growth') plt.show() df.head() mean = df['close'].mean() mean 162.3033402402023 df.index = pd.to_datetime(df.index) … Read more

Visualizing CNNs: A Comprehensive 3D Representation

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Visualizing CNNs: A Comprehensive 3D Representation

Click on the top “Beginner’s Guide to Computer Vision”, and choose to add a star or “pin” Essential insights delivered in real time. In computer vision, CNNs are indispensable. However, what do convolution, pooling, and Softmax actually look like, and how are they interconnected? Imagining it from the code can be a bit daunting. Therefore, … Read more

Some Visualization Methods for CNNs

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Some Visualization Methods for CNNs

Click on 'Xiaobai Learns Vision' above, select 'Star' or 'Top' to receive important content first. Author | yishun@Zhihu Source丨https://zhuanlan.zhihu.com/p/53683453 Editor | Jishi Platform Introduction Visual analysis of neural networks is significant for both learning and practical applications. Based on this, this article introduces three visualization methods for CNNs: visualizing intermediate feature maps, visualizing convolutional kernels, … Read more

Visualizing CNN: An Interactive Tool for Understanding Convolution

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Visualizing CNN: An Interactive Tool for Understanding Convolution

Click the above“Visual Learning for Beginners” to add it to your Favorites or “Pin” Important content delivered promptly. What is CNN? Is it the Cable News Network? Every beginner with aspirations in AI will encounter the term CNN (Convolutional Neural Network) at the start. However, every time they try to understand what CNN is and … Read more

Understanding Neural Networks, Manifolds, and Topology Through 18 Visuals

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Understanding Neural Networks, Manifolds, and Topology Through 18 Visuals

So far, a major concern about neural networks is that they are difficult to interpret black boxes. This article primarily explains theoretically why neural networks perform so well in pattern recognition and classification. Essentially, they distort and transform the original input through layers of affine transformations and nonlinear transformations until different categories can be easily … Read more

Illustrating the Architecture of Deep Neural Networks

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Illustrating the Architecture of Deep Neural Networks

Source: Xiao Bai Learns Vision This article is about 4500 words long and suggests reading for more than 10 minutes. It illustrates the entire architecture of neural networks and tools and techniques for understanding specific modules. Baseline Model AlexNet is a groundbreaking architecture that has made convolutional networks (CNN) the primary machine learning algorithm for … Read more

Understanding Neural Networks, Manifolds, and Topology Through 18 Images

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Understanding Neural Networks, Manifolds, and Topology Through 18 Images

Source | OSCHINA Community Author | OneFlow Deep Learning Framework Original link: https://my.oschina.net/oneflow/blog/5559651 So far, one major concern about neural networks is that they are difficult to interpret black boxes. This article primarily aims to theoretically understand why neural networks perform so well in pattern recognition and classification, fundamentally distorting and transforming the original input … Read more