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

# 2.4 Improving Code Efficiency with Linear Algebra (Parts 1-4)

#### Slides

#### Code

#### What we covered in this video lecture

In this video, we saw how we could replace a Python for-loop computing of a weighted sum with a linear algebra concept called *dot product*. Given two vectors with the same number of elements, the dot product multiplies pairs of elements and sums the results. A common use case is computing the dot product between a feature vector and a weight vector.

Now, if we have *n* training examples, we can compute *n* dot products — the dot product between each training example and the weight vector. We can accomplish this via a for-loop over the *n* dot products. To make this more efficient, we can use matrix-vector multiplication.

What if we have *n* training examples and *m* feature vectors — we will encounter this later when we work with multilayer perceptrons. In this case, we can use matrix multiplication to multiply two matrices, a training example matrix, and a weight matrix.

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

Linear algebra is a big topic, and there is a vast number of courses and textbooks out there. An exceptionally good course is Gilbert Strang’s Linear Algebra. However, good news is that we do not have to become linear algebra experts before we can get started leveraging it for deep learning. As we have seen in this lecture, we can regard linear algebra as a means of implementing computations more efficiently.

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