What will you in PyTorch for Deep Learning and Computer Vision Course
Master deep learning concepts and neural network design with PyTorch.
Build, train, and optimize CNNs for computer vision tasks.
Implement key architectures like LeNet, AlexNet, and ResNet.
Work with image datasets such as MNIST, CIFAR-10, and custom data.
Apply transfer learning, data augmentation, and deployment strategies.
Program Overview
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.ModuleandSequential.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.transformsfor 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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Job Outlook
High Demand: PyTorch skills are sought after in AI, computer vision, and deep learning roles.
Career Advancement: Qualifies learners for roles like AI Researcher, Deep Learning Engineer, or Vision Specialist.
Salary Potential: Professionals can expect $100K–$170K based on experience and specialization.
Freelance Opportunities: Opportunities in building CV solutions for startups, healthcare, and autonomous tech firms.
Explore More Learning Paths
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Specification: PyTorch for Deep Learning and Computer Vision Course
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