Example of video data labeling using k-means clustering with a color histogram – Exploring Video Data
Let us see example code for performing k-means clustering on video data using the open source scikit-learn Python package and the Kinetics human action dataset....
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Frame visualization – Exploring Video Data
We create a line plot to visualize the frame intensities over the frame indices. This helps us understand the variations in intensity across frames:# Frame...
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Example of video data labeling using k-means clustering with a color histogram – Exploring Video Data
Let us see example code for performing k-means clustering on video data using the open source scikit-learn Python package and the Kinetics human action dataset....
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Frame visualization – Exploring Video Data
We create a line plot to visualize the frame intensities over the frame indices. This helps us understand the variations in intensity across frames:# Frame...
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Appearance and shape descriptors – Exploring Video Data
Extract features based on object appearance and shape characteristics. Examples include Hu Moments, Zernike Moments, and Haralick texture features. Appearance and shape descriptors are methods...
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Optical flow features – Exploring Video Data
We will extract features based on the optical flow between consecutive frames. Optical flow captures the movement of objects in video. Libraries such as OpenCV...
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Extracting features from video frames – Exploring Video Data
Another useful technique for the EDA of video data is to extract features from each frame and analyze them. Features are measurements or descriptors that...
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Loading video data using cv2 – Exploring Video Data
Exploratory Data Analysis (EDA) is an important step in any data analysis process. It helps you understand your data, identify patterns and relationships, and prepare...
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Technical requirements – Exploring Video Data
In today’s data-driven world, videos have become a significant source of information and insights. Analyzing video data can provide valuable knowledge about human actions, scene...
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CNN using image data augmentation with the CIFAR-10 dataset – Labeling Image Data Using Data Augmentation-2
The following line adds a max-pooling layer with a pool size of (2, 2), which reduces the spatial dimensions of the input by taking the...
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Generating labels for customer reviews (sentiment analysis) – Labeling Text Data-2
The maximum sequence length is determined by finding the length of the longest sequence. The pad_sequences function is used to pad the sequences to the...
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Generating labels for customer reviews (sentiment analysis) – Labeling Text Data-1
Customer reviews are a goldmine of information for businesses. Analyzing sentiment in customer reviews helps in understanding customer satisfaction, identifying areas for improvement, and making...
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Hands-on label prediction using K-means clustering – Labeling Text Data
K-means clustering is a powerful unsupervised machine learning technique used for grouping similar data points into clusters. In the context of text...
Read out allHands-on text labeling using Logistic Regression – Labeling Text Data
Text labeling is a crucial task in NLP, enabling the categorization of textual data into predefined classes or sentiments. Logistic Regression, a...
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Hands-on labeling of text data using the Snorkel API – Labeling Text Data-3
Here’s the output: Figure 7.5 – Training a LabelModel It then predicts the labels for the training set and prints them:# Predict...
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Hands-on labeling of text data using the Snorkel API – Labeling Text Data-2
Step 1: Data preparation and labeling function definition. This step prepares the data and defines the labeling functions. It first imports the...
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Hands-on labeling of text data using the Snorkel API – Labeling Text Data-1
In this section, we are going to learn how to label text data using the Snorkel API. Snorkel provides an API for...
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Use case 5 – aspect-based sentiment analysis – Labeling Text Data
Sentiment aspect analysis is a sophisticated NLP task that involves evaluating the sentiment expressed towards specific aspects or features within a given...
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Use case 4 – information retrieval using entity extraction – Labeling Text Data
Let us see how to extract the entity names from the text data using Azure OpenAI. Entity extraction is a vital aspect...
Read out allUse case 3 – classification of customer queries using the user-defined categories and sub-categories – Labeling Text Data
Let’s see how to classify the customer queries into user-defined categories and sub-categories using Azure OpenAI. Text classification is a fundamental NLP...
Read out allUse case 2 – topic generation for news articles – Labeling Text Data
Let’s explore generating topic names for news articles using a generative model, specifically, Azure OpenAI. Topic generation is a powerful application of...
Read out allUse case 1 – summarizing the text – Labeling Text Data
Summarization is a crucial NLP task that involves condensing a piece of text while retaining its essential information and main ideas. In...
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