📌 PyTorch Full Course Part 2 — Transfer Learning, Attention, Transformers, and Debugging скачать в хорошем качестве

📌 PyTorch Full Course Part 2 — Transfer Learning, Attention, Transformers, and Debugging

🎓 Deepen your PyTorch mastery with this follow-up to our Full Course series! Welcome to the next level of PyTorch training! In this continuation of our PyTorch Full Course, we move beyond the fundamentals and dive deep into real-world techniques used in professional deep learning projects. Whether you're preparing for a job in machine learning, building production-ready models, or sharpening your model-debugging skills — this video gives you the tools and intuition to do it right. ✅ In this video, you’ll learn: 🔁 Transfer Learning How to use pretrained models from torchvision.models The difference between feature extraction and fine-tuning How to freeze layers and adapt classifiers for new tasks 🧪 Custom Loss Functions Write your own loss functions for specialized objectives Create multi-part loss functions for advanced training strategies 🤖 Advanced Model Architectures Understand Attention Mechanisms with intuitive examples Implement Transformers using PyTorch's built-in modules Learn how Residual Networks help train deep models Build simple RNNs for sequential tasks 🛠️ Debugging and Visualization Visualize your computation graph using torchviz Track training progress and scalars with TensorBoard Print gradients, activations, and model internals to troubleshoot effectively 🧠 By the end of this video, you'll understand how advanced models work under the hood — and how to inspect and improve them during training. 📺 Missed Part 1? Watch the full PyTorch Foundation Course here:    • PyTorch Full Course Part 1 — Build Deep Le...   👉 Subscribe for more deep learning content, and turn on notifications so you don’t miss the next part! #PyTorch #TransferLearning #Attention #Transformers #DeepLearning #MachineLearning #AI #NeuralNetworks #Debugging #TensorBoard #Bit-by-Bit