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 6 Coming Soon!

#### Unit 7 Coming Soon!

# 3.3 Model Training with Stochastic Gradient Descent (Part 1-4)

#### Slides

**What we covered in this video lecture**

This lecture introduced the training algorithm behind logistic regression: stochastic gradient descent. This is the same training algorithm we use for training deep neural networks.

Stochastic gradient descent is based on calculus: we compute the loss function’s derivatives (or gradients) with respect to the model weights. Why? The loss measures “how wrong” the predictions are. And the gradient tells us how we have to change the weights to minimize (improve) the loss.

The loss is correlated to the accuracy, but sadly, we cannot optimize the accuracy directly using stochastic gradient descent. That’s because accuracy is not a smooth function.

Computing the loss gradients is based on the chain rule from calculus, and if you are not familiar with it, it may look daunting at first. But do not worry. We will introduce PyTorch functions that can handle the differentiation (that is, the calculation of the gradients) automatically for us. This is known as *automatic differentiation* or *autograd*.

**Additional resources if you want to learn more**

The following lecture introduces PyTorch functionality that calculates the gradients automatically for us. However, if you are new to calculus or need a refresher and you want to learn more (not required for this course), I have written a concise calculus primer that you might find helpful: Calculus and Differentiation Primer.

Moreover, if you are interested in an alternative introduction to stochastic gradient descent, you may find my article Single-Layer Neural Networks and Gradient Descent helpful.

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