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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OpenAI API overview – Labeling Text Data
The OpenAI API is a service provided by OpenAI that allows users to access their advanced language models through an API. It...
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Exploring Generative AI and OpenAI for labeling text data – Labeling Text Data
Generative AI refers to a category of artificial intelligence that involves training models to generate new content or data based on patterns...
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Exploratory data analysis of sample text data set – Labeling Text Data
Here’s an example Python code for performing EDA on a text dataset. We will be using the Gutenberg corpus (https://pypi.org/project/Gutenberg/), which is...
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Understanding the data – Labeling Text Data
Exploratory data analysis of text Exploratory Data Analysis (EDA) is a crucial step in any data science project. When it comes to...
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Tools and frameworks for text data labeling – Labeling Text Data
There are several open source tools and frameworks available for text data analysis and labeling. Here are some popular ones, along with...
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Real-world applications of text data labeling – Labeling Text Data
Text data labeling or classification is widely used across various industries and applications to extract valuable information, automate processes, and improve decision-making....
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Technical requirements – Labeling Text Data
In this chapter, we will explore techniques for labeling text data for classification in cases where an insufficient amount of labeled data...
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CNN using image data augmentation with the CIFAR-10 dataset – Labeling Image Data Using Data Augmentation-1
Let us see some example Python code for a supervised CNN using image data augmentation with the CIFAR-10 dataset:import tensorflow as tffrom...
Read out allPractical example of a CNN using data augmentation – Labeling Image Data Using Data Augmentation-3
We use the fit() function to train the model on the augmented dataset. We specify the training and validation generators, the number...
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Practical example of a CNN using data augmentation – Labeling Image Data Using Data Augmentation-2
Here, we are creating a simple CNN model with four convolutional layers and one fully connected layer. We are using ReLU activation...
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