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수업 설명, Colab 설명 | 수업 설명, Colab 설명 | ![]() |
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Chapter 1 Machine Learning Landscape | 1. Types of Machine Learning system 2. Main challenges of Machine Learning, 3. Testing and validation | ![]() |
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Chapter 2 End-to-End Machine Learning Project | 1. Look at the big picture 2. Get the data 3. Discover and visualize the data to gain insights 4. Prepare the data for Machine Learning algorithms | ![]() |
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Chapter 2 End-to-End Machine Learning Project | 5. Select a model and train it 6. Fine-tune your model | ![]() |
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Chapter 3 Classification | 1. MNIST data 2. Training a Binary Classifier 3. Performance Measures 4. Multiclass Classification 5. Multilable Classification | ![]() |
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Chapter 4 Training Models | 1. Linear Regression 2. Gradient Descent 3. Polynomial Regression 4. Learning Curves 5. Regularized Linear Models | ![]() |
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Chapter 5 Support Vector Machines | 1. Linear SVM Classification 2. Nonlinear SVM Classification | ![]() |
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Chapter 5 Support Vector Machines | 3. SVM Regression | ![]() |
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Chapter 6 Decision Tree | 1. Training and Visualizing a Decision Tree 2. Making Predictions 3. Estimating Class Probabilities 4. The CART Training Algorithm 5. Regularization Hyperparameters | ![]() |
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Chapter 7 Ensemble Learning and Random Forests | 1. Voting Classifiers 2. Bagging and Pasting 3. Random Forests 4. Boosting 5. Stacking | ![]() |
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Chapter 8 Dimensionality Reduction | 1. The Curse of Dimensionality 2. Main Approaches for Dimensionality Reduction 3. PCA | ![]() |
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Chapter 8 Dimensionality Reduction | 4. Kernel PCA 5. Locally Linear Embedding (LLE) 6. Other Dimensionality Reduction Techniques | ![]() |
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Chapter 9 Unsupervised Learning Techniques | 1. Clustering 2. Gaussian Mixtures | ![]() |
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Chapter 10 Introduction to Artificial Neural Networks with Keras | 1. From Biological to Artificial Neurons 2. Implementing MLPs with Keras | ![]() |
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Chapter 10 Introduction to Artificial Neural Networks with Keras | 3. Fine-Tuning Neural Network Hyperparameters | ![]() |
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Chapter 11 Training Deep Neural Nets | 1. The Vanishing/Exploding Gradients Problems 2. Reusing Pretrained Layers | ![]() |
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Chapter 11 Training Deep Neural Nets | 3. Faster Optimizers 4. Avoiding Overfitting through Regularization | ![]() |
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Chapter 12 Custom Models and Trainingwith TensorFlow | 1. A Quick Tour of Tensorflow 2. Using Tensorflow like Numpy | ![]() |
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Chapter 12 Custom Models and Trainingwith TensorFlow | 3. Customizing Models and training algorithms 4. Tensorflow functions and graphs | ![]() |
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Chapter 13 Loading and Preprocessing Data with TensorFlow | 1. Data API 2. TFRecord Format 3. Preprocessing the input features 4. TF transform, TF Datasets | ![]() |
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Chapter 14 Deep Computer Vision Using Convolutional Neural Networks | 1. Convolutional Layers 2. Pooling Layers 3. CNN Architectures 4. Implementing a ResNet-34 CNN using Keras | ![]() |
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Final Presentation 1 | Final Team presentation | ![]() |
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Final Presentation 2 | Final Team presentation 2 | ![]() |