Deep Learning Fundamentals

- Deep Learning Fundamentals
- Unit 1Intro to ML and DL
- Unit 2Using Tensors w/ PyTorch
- Unit 3Model Training in PyTorch
- Unit 3.1Using Logistic Regression for Classification
- Unit 3.2The Logistic Regression Computation Graph
- Unit 3.3Model Training with Stochastic Gradient Descent
- Unit 3.4Automatic Differentiation in PyTorch
- Unit 3.5The PyTorch API
- Unit 3.6Training a Logistic Regression Model in PyTorch
- Unit 3.7 Feature Normalization
- Unit 3 ExercisesUnit 3 Exercies

- Unit 4Training Multilayer Neural Networks Overview
- Unit 4.1Logistic Regression for Multiple Classes
- Unit 4.2Multilayer Neural Networks
- Unit 4.3Training a Multilayer Neural Network in PyTorch
- Unit 4.4Defining Efficient Data Loaders
- Unit 4.5Multilayer Neural Networks for Regression
- Unit 4.6Speeding Up Model Training Using GPUs
- Unit 4 ExercisesUnit 4 Exercises

- Unit 5Organizing Your Code with Lightning
- Unit 5.1 Organizing Your Code with Lightning
- Unit 5.2Training a Multilayer Perceptron using the Lightning Trainer
- Unit 5.3Computing Metrics Efficiently with TorchMetrics
- Unit 5.4Making Code Reproducible
- Unit 5.5Organizing Your Data Loaders with Data Modules
- Unit 5.6The Benefits of Logging Your Model Training
- Unit 5.7Evaluating and Using Models on New Data
- Unit 5.8Add Functionality with Callbacks
- Unit 5 ExercisesUnit 5 Exercises

- Unit 6Essential Deep Learning Tips & Tricks
- Unit 6.1 Model Checkpointing and Early Stopping
- Unit 6.2Learning Rates and Learning Rate Schedulers
- Unit 6.3Using More Advanced Optimization Algorithms
- Unit 6.4Choosing Activation Functions
- Unit 6.5Automating The Hyperparameter Tuning Process
- Unit 6.6Improving Convergence with Batch Normalization
- Unit 6.7Reducing Overfitting With Dropout
- Unit 6.8Debugging Deep Neural Networks
- Unit 6 ExercisesUnit 6 Exercises

- Unit 7Getting Started with Computer Vision
- Unit 7.1Working With Images
- Unit 7.2How Convolutional Neural Networks Work
- Unit 7.3Convolutional Neural Network Architectures
- Unit 7.4Training Convolutional Neural Networks
- Unit 7.5Improving Predictions with Data Augmentation
- Unit 7.6Leveraging Pretrained Models with Transfer Learning
- Unit 7.7Using Unlabeled Data with Self-Supervised
- Unit 7 ExercisesUnit 7 Exercises

- Unit 8Natural Language Processing and Large Language Models
- Unit 8.1Working with Text Data
- Unit 8.2Training A Text Classifier Baseline
- Unit 8.3Introduction to Recurrent Neural Networks
- Unit 8.4From RNNs to the Transformer Architecture
- Unit 8.5Understanding Self-Attention
- Unit 8.6Large Language Models
- Unit 8.7A Large Language Model for Classification
- Unit 8 ExercisesUnit 8 Exercises

- Unit 9Techniques for Speeding Up Model Training
- Unit 10 The Finale: Our Next Steps After AI Model Training

# Unit 10 Exercises

## Exercise 1: Using Fabric

It’s been a while since we used pure PyTorch code in Unit 4. If you are interested in using Fabric, a good exercise is to convert the existing PyTorch code into Fabric — it only requires changing a handful of lines.

For this, you can use this template based on the MNIST classifier from Unit 4.

Remember, the conversion to Fabric involves

- Setting up the model and optimizer via
`fabric.setup(model, optimizer)`

- Converting the data loaders via
`fabric.setup_dataloaders`

Hint: If you prefer to run this code on a CPU instead of GPU, you don’t need to specify the accelerator. Or just set it to `accelerator="cpu"`

.

## Exercise 2: Confidence Intervals

If you want to practice using the confidence interval method introduced in this lecture, tru to compute a confidence interval for the MNIST classifier from Unit 4 using this template.

Hint: In this case, since it’s a relatively large test set, the confidence interval is less than a percentage point wide.

And for a more advanced challenge, you can also try to implement the other 3 confidence interval methods for this MNIST classifier.

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