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機械学習

DL [Course 5/5] Sequence Models [Week 1/3] Recurrent Neural Networks

Why sequence models Examples of sequence data Speech recognition x: wave🌊 y: “The quick brown fox jumped […]

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機械学習

DL [Course 4/5] Convolutional Neural Networks [Week 4/4] Special applications: Face recognition & Neural style transfer

[mathjax] Face Recognition What is face recognition? Face verification vs. face recognition Verification Input […]

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機械学習

DL [Course 4/5] Convolutional Neural Networks [Week 3/4] Object detection

Key Concepts Remember the vocabulary of object detection (landmark, anchor, bounding box, grid, …) Under […]

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機械学習

DL [Course 4/5] Convolutional Neural Networks [Week 2/4] Deep convolutional models: case studies

Key Concepts Understand and Implement a Residual network Clone a repository from github and use transfer learn […]

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機械学習

DL [Course 4/5] Convolutional Neural Networks [Week 1/4] Foundations of Convolutional Neural Networks

Key Concepts Understand the convolution operation Understand the pooling operation Remember the vocabulary use […]

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機械学習

DL [Course 3/5] Structuring Machine Learning Projects [Week 2/2]

Key Concepts Understand what multi-task learning and transfer learning are Recognize bias, variance and data-m […]

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機械学習

DL [Course 3/5] Structuring Machine Learning Projects [Week 1/2]

Key Concepts Apply satisficing and optimizing metrics to set up your goal for ML projects Take the correct ML […]

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機械学習

DL [Course 2/5] Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization [Week 3/3] Hyperparameter tuning, Batch Normalization and Programming Frameworks

Key Concepts Master the process of hyperparameter tuning [mathjax]\(\require{cancel}\) Improving Deep Neural N […]

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機械学習

DL [Course 2/5] Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization [Week 2/3] Optimization algorithms

Key Concepts Know the benefits of learning rate decay and apply it to your optimization Remember different opt […]

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機械学習

DL [Course 2/5] Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization [Week 1/3] Practical aspects of Deep Learning

Key Concepts Use gradient checking to verify the correctness of your backpropagation implementation Understand […]