TIMESTAMPS:
00:00 - Video Intro
02:06 - STL10 Dataset Overview
03:11 - Understanding Transfer Learning
10:40 - Implementing Adaptive Average Pooling
12:15 - Adapting the Model for Training
14:40 - Saving Our Trained Model
19:30 - Training Process Explained
20:26 - Utilizing a Pre-trained Model
21:50 - Weight Freezing Techniques
24:40 - Experimenting with Another Model
28:40 - Conclusion and Final Remarks
Donations
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Discord Server:
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GitHub Repository (Section 5)
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In this tutorial, we delve into PyTorch's pre-defined models and transfer learning to enhance CNN Image Classifier performance. Explore the STL10 dataset, understand transfer learning, implement adaptive average pooling, and experiment with model architectures. Dive into the code and resources available on our GitHub repository.
Prerequisites:
- Basic knowledge of convolutional neural networks (CNNs) and PyTorch concepts.
- Proficiency in Python programming.
Whether you're a beginner or an expert, this tutorial offers valuable insights into advanced image classification techniques. Don't forget to like, share, and subscribe for more informative content on AI, machine learning, and programming.
