Data Science Project | Part 2 | Intel Image Classification with PyTorch
Welcome to Part 2 of our Data Science Project series!
In this session, we continue our journey into image classification using the Intel Image Classification dataset with PyTorch. This session builds upon the fundamentals covered in Part 1, delving deeper into model building, training, and optimization.
What You'll Learn:
Review of the Intel Image Classification Dataset: A quick recap of the dataset structure and categories to refresh your understanding.
Advanced Data Augmentation: Explore advanced techniques for augmenting image data, such as rotation, flipping, and color jittering, to increase model robustness.
Transfer Learning: Introduction to transfer learning using pre-trained models like ResNet, VGG, or EfficientNet for image classification tasks.
Fine-Tuning Pre-trained Models: Step-by-step guide on how to fine-tune pre-trained models to adapt them to the specific requirements of the Intel Image Classification dataset.
-Model Optimization: Techniques for optimizing model performance, including learning rate scheduling, batch normalization, and regularization.
Why Watch This session?
This session is essential for data science practitioners looking to advance their skills in image classification with PyTorch. By the end of this session, you'll have a deeper understanding of advanced data augmentation, transfer learning, and fine-tuning techniques, preparing you to build high-performing image classification models.
Phone: +91 8071176111
Website: https://ineuron.ai/
Instagram: / official_ineuron.ai
Discord : / discord
YouTube: / @ineuronintelligence
Hindi: / @ineurontechhindi
Tech News: / @ineurontechnews
DevHub: / @ineurondevhub
DevOps : / @ineurondevops
Non Tech : / @ineuronnontech
Linkedin: / ineuron-ai
Twitter: / ineuron_ai
Quora: https://www.quora.com/profile/INeuron...
