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Tag: Imbalanced
DEEP LEARNING: AN AUTOMATED IMBALANCED CLOUD BASED FRAUD DETECTION MODEL
Price:$87.00– $79.86
(as of Dec 28,2024 13:06:58 UTC – Details)
In today’s digital age, fraud detection has become a critical concern for businesses across all industries. With the rise of online transactions and digital payments, the need for effective fraud detection mechanisms has never been more pressing. Traditional fraud detection methods are often manual and time-consuming, leaving businesses vulnerable to sophisticated cyber threats.Enter deep learning, a cutting-edge technology that leverages artificial intelligence and machine learning algorithms to automate and enhance fraud detection processes. By analyzing vast amounts of data and identifying patterns and anomalies, deep learning models can detect fraudulent activities with a high level of accuracy and efficiency.
One of the key advantages of deep learning-based fraud detection models is their ability to adapt and learn from new data in real-time. This means that as fraudsters develop new tactics and techniques, the model can quickly adjust and improve its detection capabilities to stay ahead of evolving threats.
Furthermore, by leveraging cloud-based infrastructure, businesses can scale their fraud detection capabilities easily and cost-effectively. Cloud-based deep learning models can process massive amounts of data quickly, enabling businesses to detect and prevent fraud in real-time.
Overall, a cloud-based deep learning fraud detection model offers businesses a powerful and automated solution to combat fraud effectively. By harnessing the power of artificial intelligence and machine learning, businesses can protect their assets, customers, and reputation from the ever-growing threat of fraud in today’s digital landscape.
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Machine Learning for Imbalanced Data: Tackle imbalanced datasets using machine learning and deep learning techniques
Price: $49.99
(as of Dec 16,2024 03:11:29 UTC – Details)
Publisher : Packt Publishing (November 30, 2023)
Language : English
Paperback : 344 pages
ISBN-10 : 1801070830
ISBN-13 : 978-1801070836
Item Weight : 1.32 pounds
Dimensions : 9.25 x 7.52 x 0.72 inches
Machine Learning for Imbalanced Data: Tackle imbalanced datasets using machine learning and deep learning techniquesImbalanced datasets are a common challenge in machine learning, where one class of data is significantly more prevalent than another. This can lead to biased models that perform poorly on the minority class. However, there are several techniques that can be used to address this issue and improve the performance of models on imbalanced datasets.
One approach is to use resampling techniques, such as oversampling the minority class or undersampling the majority class, to balance the dataset. Another option is to use different evaluation metrics, such as precision, recall, and F1 score, that are more suitable for imbalanced datasets.
Additionally, machine learning algorithms such as decision trees, random forests, and support vector machines can be adjusted to give more weight to the minority class, helping to improve performance on imbalanced datasets.
Deep learning techniques, such as neural networks and convolutional neural networks, can also be effective for handling imbalanced data. Techniques such as class weights, focal loss, and data augmentation can help to improve the performance of deep learning models on imbalanced datasets.
By using these techniques and approaches, machine learning practitioners can effectively tackle imbalanced datasets and build more accurate and reliable models.
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