Using TensorFlow in R
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Lesson 1.1 - Getting started with TensorFlow
- A learning objective: Create tensors in R and use the building blocks of TensorFlow APIs
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Lesson 1.2 - Implementing graphs and loops in TensorFlow
- A learning objective: Implement a multilayer neural network and evaluate it for predictions against a sample dataset
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Lesson 1.3 - Training a neural network with gradient descent
- A learning objective: Use stochastic gradient descent and other training algorithms to improve neural network weights
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Lesson 2.1 - Implementing regression
- A learning objective: Fit a deep neural network model to predict continuous values with mean squared error loss
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Lesson 2.2 - Implementing classification
- A learning objective: Predict categorical values with a deep neural network using a cross-entropy loss function
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Lesson 2.3 - Improving model performance with feature engineering
- A learning objective: Implement one-hot encoding, feature crosses, bucketization, and other custom features
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Lesson 2.4 - Training on out of memory data
- A learning objective: Using tf.dataset and supporting libraries to train, validate, and test from sharded datasets.
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Lesson 3.1 - Implementing customized layers in deep neural networks
- A learning objective: Implement sequential and functional layers in Keras-based models.
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Lesson 3.2 - Using dropout for regularization
- A learning objective: Extend Keras models with customized regularization layers and options.
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Lesson 3.3 - Getting started with hyperparameter tuning
- A learning objective: Implement hyperparameter tuning for linear, logarithmic, and categorical hyperparameters.
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Lesson 3.4 - Setting up a machine learning pipeline
- A learning objective: Create a pipeline to train, evaluate, and serve using multiple machine learning models
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Lesson 4.1 - Adding convolutional and maxpooling layers
- A learning objective: Implement a convolutional neural network for image classification
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Lesson 4.2 - Adding batch normalization
- A learning objective: Include batch normalization during deep neural network training
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Lesson 4.3 - Using image augmentation
- A learning objective: Use tf.image library to augment image datasets with synthetic data
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Lesson 4.4 - Conclusion
- A learning objective: Implement a deep neural network model for taxi fare prediction