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
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
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
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
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
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
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
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
To date, a major concern regarding neural networks is that they are difficult to interpret black boxes. This article primarily aims to understand theoretically why neural networks perform so well in pattern recognition and classification. The essence lies in the fact that they distort and transform the original input through layers of affine transformations and … Read more
Click the "Xiaobai Learns Vision" above, select "Star" or "Pin" Heavy content delivered to you first Author丨Piotr Migdał Source丨Youer’s Cabin Editor丨Jishi Platform Jishi Guide Illustrated overview of the entire neural network architecture, and tools and techniques for understanding specific modules. Baseline Model AlexNet is a groundbreaking architecture that made Convolutional Neural Networks (CNNs) the main … Read more