Heartbeat

NOVEMBER 14, 2023

Integrating XGboost with Convolutional Neural Networks Photo by Alexander Grey on Unsplash XGBoost is a powerful library that performs gradient boosting. It has an excellent reputation as a tool for predicting many kinds of problems in data science and machine learning. It was envisioned by Thongsuwan et al.,

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Heartbeat

JUNE 27, 2023

Neural networks leverage the structure and properties of graph and work in a similar fashion. Graph Neural Networks are a class of artificial neural networks that can be represented as graphs. These tasks require the model to categorize edge types or predict the existence of an edge between two given nodes.

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Heartbeat

MARCH 8, 2023

Things You Can do Using Kangas Library in Data Science — by Pranjal Saxena Kangas, developed by the team at Comet , is an open source tool that allows data developers to load, sort, group, and visualize millions of images at once without the risk of crashing their notebooks.

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Heartbeat

JANUARY 12, 2023

Convolutional neural networks ( CNNs ) are a subtype of artificial neural networks that have been popular in several applications linked to computer vision and are attracting interest in other domains. It also offers comprehensive developer instructions. To get a baseline score, we shall employ a CNN model.

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Heartbeat

APRIL 18, 2023

Human Action Recognition (HAR) is a process of identifying and categorizing human actions from videos or image sequences. The VGG model The VGG ( Visual Geometry Group ) model is a deep convolutional neural network architecture for image recognition tasks. What is Human action recognition (HAR)? Zisserman and K.

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Pickl AI

DECEMBER 10, 2024

Categorical Features (Nominal vs. Ordinal) Categorical features group data into distinct categories or classes, often representing qualitative attributes. Handling categorical data appropriately is essential for ensuring accurate interpretations by Machine Learning models.

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Heartbeat

FEBRUARY 22, 2023

Deep learning models can extract different, and often more useful, features compared to traditional machine learning models for several reasons: Depth Deep learning models, especially Convolutional Neural Networks (CNNs), have multiple layers that can learn hierarchical representations of the input data.

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Pickl AI

AUGUST 1, 2023

The success of a Machine Learning model depends on various factors, including the quality and quantity of the training data, the model architecture, and hyperparameters’ tuning. Types of Machine Learning Model: Machine Learning models can be broadly categorized as: 1. Handle missing data and outliers appropriately.

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Heartbeat

AUGUST 29, 2023

Types of Deep Learning Approaches A variety of methods and designs are used to train neural networks under the umbrella of deep learning. Some of the symbolic approaches of deep learning are listed below: CNNs (Convolutional Neural Networks) : CNNs are frequently employed in image and video recognition jobs.

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Heartbeat

FEBRUARY 8, 2024

Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often employed to extract meaningful representations from images and text, respectively. Then, compile the model, harnessing the power of the Adam optimizer and categorical cross-entropy loss.

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Pickl AI

NOVEMBER 28, 2024

These networks can learn from large volumes of data and are particularly effective in handling tasks such as image recognition and natural language processing. Key Deep Learning models include: Convolutional Neural Networks (CNNs) CNNs are designed to process structured grid data, such as images.

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PyImageSearch

SEPTEMBER 16, 2024

Types of Anomaly Detection Problems Anomaly detection problems can be broadly categorized into three main types: point anomalies contextual anomalies collective anomalies Each type ( Figure 4 ) has distinct characteristics and applications, making it essential to understand their differences and how they can be effectively identified.

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