Deep Learning Foundations with PyTorch and TensorFlow

Completed a 10-notebook PyTorch program (tensors, workflow, classification, CNNs with FashionMNIST, custom datasets via TorchVision/ImageFolder, modular data_setup/engine/model_builder/train architecture, transfer learning, experiment tracking, Vision Transformer paper replication, deployment) and an 11-notebook TensorFlow program (regression, classification, CNNs, transfer learning with feature extraction, fine-tuning, and scaling using EfficientNet and ResNet50V2, TensorBoard tracking, Food Vision Big with 101 classes, NLP with word embeddings and Universal Sentence Encoder, SkimLit sequential sentence classification, BitPredict time series forecasting). Applied modular code architecture and experiment tracking throughout.

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