Kurs · 7 Kapitel
Deep Learning
Deep learning in production — DL frameworks (PyTorch + TensorFlow), training techniques, vision tasks and CNNs, building from scratch vs transfer learning, and modern architectures.
Das wirst du können
- A decision guide to the two dominant deep learning frameworks — their philosophies, ecosystems, and the practical factors that should drive your choice.
- The first strategic decision after choosing deep learning: train a new network from zero or stand on the shoulders of pretrained giants.
- A decision framework for choosing between Deep Learning and classical Machine Learning — based on your data, compute, timeline, and interpretability needs.
- A 12-minute orientation to the Deep Learning skill path — why it exists, what you will build, how the six chapters connect, and where to begin.
- Image classification, object detection, and segmentation — pick the right computer vision task before you pick an architecture.
- The full toolkit for training neural networks — batch size, learning rates, loss functions, activations, optimizers, regularization, and early stopping.
Was drin ist
- 1DL Frameworks: PyTorch and TensorFlow
A decision guide to the two dominant deep learning frameworks — their philosophies, ecosystems, and the practical factors that should drive your choice.
- 2Building from Scratch vs Transfer Learning
The first strategic decision after choosing deep learning: train a new network from zero or stand on the shoulders of pretrained giants.
- 3DL vs ML: When Depth Wins
A decision framework for choosing between Deep Learning and classical Machine Learning — based on your data, compute, timeline, and interpretability needs.
- 4Deep Learning: Start Here
A 12-minute orientation to the Deep Learning skill path — why it exists, what you will build, how the six chapters connect, and where to begin.
- 5Vision Task Types
Image classification, object detection, and segmentation — pick the right computer vision task before you pick an architecture.
- 6Training Techniques for Deep Learning
The full toolkit for training neural networks — batch size, learning rates, loss functions, activations, optimizers, regularization, and early stopping.
- 7CNNs (Convolutional Neural Networks)
Design, train, and interpret the architecture that powers modern computer vision — from first convolution to production deployment.
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