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Computer Vision Essential Training: Deep Learning for Image Classification

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Computer vision has come a long way since its humble beginnings. And today, it’s one of the most talked-about fields in tech. Join instructor Harpreet Sahota in this comprehensive overview of the history and evolution of this increasingly important industry, developing your understanding of convolutional neural networks, network training, deep learning models for image classification tasks, transfer learning with pretrained models, and more. Explore the wide variety of functionalities offered by the SuperGradients flexible training library, which gives you the power to shorten and streamline the model development lifecycle. Along the way, Harpreet shares practical insights on how to train models and networks more effectively, applying state-of-the-art techniques like exponential moving average, weighted average, batch accumulation, and BatchNorm.

Note: This course requires a basic working knowledge of machine learning as well as experience with Python and PyTorch.


01 - Introduction
  • 01 - Computer vision introduction
  • 02 - What you should know

  • 02 - 1. Computer Vision
  • 01 - What is computer vision
  • 02 - A history of computer vision
  • 03 - Limitations of traditional CV techniques
  • 04 - ImageNet
  • 05 - The deep learning revolution

  • 03 - 2. Introduction to Convolutional Neural Networks
  • 01 - Overview of CNNs
  • 02 - Why CNNs
  • 03 - Convolutional layers
  • 04 - Types of convolutions
  • 05 - Pooling layers
  • 06 - Activation functions
  • 07 - Fully connected layers

  • 04 - 3. How Networks Are Trained
  • 01 - Supervised learning and loss functions
  • 02 - Backpropagation in CNNs
  • 03 - Optimization techniques
  • 04 - Regularization and data augmentation

  • 05 - 4. The Evolution of CNN Architectures
  • 01 - LeNet
  • 02 - AlexNet
  • 03 - VGG
  • 04 - ResNet
  • 05 - MobileNetV1
  • 06 - MobileNetV2
  • 07 - MobileNetV3
  • 08 - EfficientNet

  • 06 - 5. Transfer Learning
  • 01 - Introduction to transfer learning
  • 02 - Types of transfer learning
  • 03 - Steps in feature extracting and fine-tuning
  • 04 - Best practices for transfer learning

  • 07 - 6. PyTorch Crash Course
  • 01 - Setting up the environment
  • 02 - Dataset and DataLoader
  • 03 - Training setup
  • 04 - The training loop
  • 05 - Testing and evaluation
  • 06 - Inference

  • 08 - 7. Hands-on Transfer Learning with SuperGradients
  • 01 - Introduction to SuperGradients
  • 02 - The trainer
  • 03 - Required training params
  • 04 - Optional training params
  • 05 - Training the model
  • 06 - Predicting with the model
  • 07 - How to solve almost any image classification problem with SG

  • 09 - 8. Training Tricks
  • 01 - Exponential moving average
  • 02 - Weight averaging
  • 03 - Batch accumulation
  • 04 - Precise BatchNorm
  • 05 - Zero weight decay on BatchNorm and bias
  • 06 - Training tricks in SuperGradients

  • 10 - Conclusion
  • 01 - Next steps
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