Deep Learning

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• Choose between PyTorch and TensorFlow based on ecosystem, philosophy, and project needs
• Judge when deep learning beats classical machine learning by weighing data, compute, timeline, and interpretability
• Decide between training a network from scratch and applying transfer learning from pretrained models
• Pick the right computer vision task: image classification, object detection, or segmentation
• Reason about training choices such as batch size, learning rates, loss functions, optimizers, regularization, and early stopping
• Understand how convolutional neural networks (CNNs) are structured for vision problems

• Url: https://aiacademy.anthropos.work/chapters/dl-frameworks-pytorch-tensorflow/ · DL Frameworks: PyTorch and TensorFlow · Position: 1 · A decision guide to the two dominant deep learning frameworks — their philosophies, ecosystems, and the practical factors that should drive your choice.
• Url: https://aiacademy.anthropos.work/chapters/building-from-scratch-vs-transfer-learning/ · Building from Scratch vs Transfer Learning · Position: 2 · The first strategic decision after choosing deep learning: train a new network from zero or stand on the shoulders of pretrained giants.
• Url: https://aiacademy.anthropos.work/chapters/dl-vs-ml/ · DL vs ML: When Depth Wins · Position: 3 · A decision framework for choosing between Deep Learning and classical Machine Learning — based on your data, compute, timeline, and interpretability needs.
• Url: https://aiacademy.anthropos.work/chapters/deep-learning-intro/ · Deep Learning: Start Here · Position: 4 · 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.
• Url: https://aiacademy.anthropos.work/chapters/vision-task-types/ · Vision Task Types · Position: 5 · Image classification, object detection, and segmentation — pick the right computer vision task before you pick an architecture.
• Url: https://aiacademy.anthropos.work/chapters/training-techniques-dl/ · Training Techniques for Deep Learning · Position: 6 · The full toolkit for training neural networks — batch size, learning rates, loss functions, activations, optimizers, regularization, and early stopping.
• Url: https://aiacademy.anthropos.work/chapters/cnns-convolutional-neural-networks/ · CNNs (Convolutional Neural Networks) · Position: 7 · Design, train, and interpret the architecture that powers modern computer vision — from first convolution to production deployment.

• Choose between PyTorch and TensorFlow based on ecosystem, philosophy, and project needs
• Judge when deep learning beats classical machine learning by weighing data, compute, timeline, and interpretability
• Decide between training a network from scratch and applying transfer learning from pretrained models
• Pick the right computer vision task: image classification, object detection, or segmentation
• Reason about training choices such as batch size, learning rates, loss functions, optimizers, regularization, and early stopping
• Understand how convolutional neural networks (CNNs) are structured for vision problems

Course Mode: online · Course Workload: PT140M · Mode: online