PyTorch for Deep Learning and Computer Vision

Module 1: Introduction to PyTorch & Deep Learning

⏳ 30 minutes

• Overview of PyTorch ecosystem and installation.

• Basics of tensors, gradients, and autograd.

Module 2: Building Neural Networks

⏳ 60 minutes

• Creating models using nn.Module and Sequential.

• Defining loss functions and optimizers.

Module 3: Training Deep Neural Networks

⏳ 60 minutes

• Building training loops with dataloaders and evaluation cycles.

• Saving, loading, and reusing trained models.

Module 4: Computer Vision with CNNs

⏳ 75 minutes

• Building CNNs from scratch for image classification.

• Applying convolution, pooling, and flattening techniques.

Module 5: Famous Architectures in PyTorch

⏳ 90 minutes

• Recreating LeNet, AlexNet, VGG, and ResNet models.

• Adapting pretrained models to new tasks.

Module 6: Working with Image Datasets

⏳ 60 minutes

• Loading datasets like MNIST and CIFAR-10 with torchvision.

• Custom dataset handling and preprocessing.

Module 7: Transfer Learning & Fine-Tuning

⏳ 60 minutes

• Using pretrained models to accelerate training.

• Modifying output layers and retraining for custom classes.

Module 8: Data Augmentation & Regularization

⏳ 45 minutes

• Applying torchvision.transforms for image enhancement.

• Techniques to improve generalization and reduce overfitting.

Module 9: Final Project – Image Classifier Deployment

⏳ 75 minutes

• End-to-end pipeline from model creation to inference.

• Exporting and deploying models in real-world environments.

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