Data Augmentation Techniques in Deep Learning for Image Classification

Learn how to implement data augmentation in deep learning to improve CNN performance for classifying cat and dog images using TensorFlow.

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#deeplearning #machinelearning #cnn #python #codersarts Learn how to build a Convolutional Neural Network (CNN) to classify cat and dog images using TensorFlow. This video will cover the basics of CNNs, as well as how to train and evaluate a CNN model for image classification. We'll also showcase some interesting projects that have been built using CNNs for cat and dog classification, such as a cat and dog breed classifier and a cat and dog detection system. Watch the full video: https://youtu.be/E0B5wLlbH2g Get your Programming Tasks done & 1-on-1 Mentorship: Codersarts is a trusted and top-rated website for programming help. Get help from vetted software developers, engineers, ML researchers, and programming experts. Need More Help in Machine Learning? =================================== ✅Machine Learning Assignment Help : https://www.codersarts.com/machine-learning-assignment-help ✅Deep Learning Assignment Help: https://www.codersarts.com/deep-learning-assignment-help ✅NLP Assignment Help: https://www.codersarts.com/nlp-assignment-help ✅Data Visualization Assignment Help: https://www.codersarts.com/data-visualization-assignment-help ✅Computer Vision Assignment Help: https://www.codersarts.com/computer-vision-assignment-help ✅Face Recognition Assignment Help: https://www.codersarts.com/face-recognition-project-help ✅R Programming Assignment Help : https://www.codersarts.com/r-programming-assignment-help 📲Follow us on our Social Media Handles : ================================= 🔗 Main Website: https://www.codersarts.com/ 📚 Codersarts Training: https://www.training.codersarts.com/ 🤖 Codersarts AI: https://www.ai.codersarts.com/ 📷 Instagram: https://www.instagram.com/codersarts/ 📘 Facebook: https://www.facebook.com/codersarts2017 💼 LinkedIn: https://in.linkedin.com/company/codersarts

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1.2K

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0:42

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Sep 28, 2023

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