This is a beginner-friendly coding-first online course on PyTorch – one of the most widely used and fastest growing frameworks for machine learning. This video covers the process of applying advanced techniques like residual networks, data augmentation, batch normalization and transfer learning to achieve state of the art results for image classification in a very short time. Resources:
🔗 Classifying CIFAR10 images using a ResNet : https://jovian.ml/aakashns/05b-cifar10-resnet
🔗 Transfer learning starter: https://jovian.ml/aakashns/transfer-learning-starter 🔗 Discussion forum: https://jovian.ml/forum/t/lecture-5-data-augmentation-regularization-and-resnets/1546 🔗 Data science competition: https://www.kaggle.com/c/jovian-pytorch-z2g 🔗 Course project: https://jovian.ml/forum/t/assignment-5-course-project/1563 Topics covered:
⌨️ Improving the dataset using data normalization and data augmentation
⌨️ Improving the model using residual connections and batch normalization
⌨️ Improving the training loop using learning rate annealing, weight decay and gradient clip
⌨️ Training a state of the art image classifier from scratch in 5 minutes 🎥 Watch the entire series here: https://www.youtube.com/watch?v=vo_fUOk-IKk&list=PLWKjhJtqVAbm3T2Eq1_KgloC7ogdXxdRa This course is taught by Aakash N S, Founder & CEO of Jovian.ml – a platform for sharing, showcasing and collaborating on data science projects online. 🔗 YouTube: https://youtube.com/jovianml
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