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Learn PyTorch through an extensive tutorial series that progresses from fundamental concepts to advanced deep learning implementations. Start with environment setup using Anaconda and PyCharm, then master tensor operations including initialization, mathematical operations, indexing, and reshaping. Build your first neural networks with examples covering basic neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and bidirectional LSTMs. Develop practical skills in model persistence, transfer learning, and fine-tuning techniques. Master data handling by creating custom datasets for both images and text, implementing data augmentation with Torchvision and Albumentations, and addressing imbalanced datasets using weighted random sampling. Explore computer vision architectures by implementing classic networks from scratch including LeNet, VGG, GoogLeNet/InceptionNet, ResNet, and EfficientNet. Dive into advanced computer vision applications with image captioning, neural style transfer, and comprehensive coverage of Generative Adversarial Networks (GANs) including DCGAN, WGAN, Conditional GAN, Pix2Pix, CycleGAN, ProGAN, SRGAN, and ESRGAN implementations. Master natural language processing through text generation with character-level LSTMs, comprehensive Torchtext tutorials covering custom datasets and built-in datasets, and advanced sequence-to-sequence models for machine translation. Implement transformer architectures from scratch, including attention mechanisms and complete transformer models for translation tasks. Tackle computer vision challenges with U-NET for image segmentation and object detection fundamentals including Intersection over Union (IoU), Non-Max Suppression, Mean Average Precision (mAP), and complete YOLO implementations (v1 and v3). Optimize your workflow with practical tips covering mixed precision training, progress bars, reproducible results, data statistics calculation, weight initialization, and learning rate scheduling using TensorBoard for visualization and monitoring.
