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

# 1.4 The First Machine Learning Classifier

#### Slides

#### References

- The Perceptron — A Perceiving and Recognizing Automaton (1957) by Frank Rosenblatt

#### What we covered in this video lecture

In this lecture, we introduced the perceptron algorithm, a binary classification algorithm inspired by how neurons in the human brain work.

In the forward pass, the perceptron takes the input features, computes the net input, and finally applies a threshold to determine the predicted class labels. The perceptron then updates its weight and bias parameters based on whether the prediction is correct. Here, the weights and bias parameters are the model parameters learned from the training set.

We use the letter m (as a subscript) to denote the number of features (dimensions) in a given dataset. And we use the letter n (as a superscript) to indicate the number of training examples. Another piece of technical jargon we introduced in this lecture is the concept of a training epoch. A training epoch refers to one full iteration over the training set.

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

If you are interested in the early perceptron algorithms and how it all began, I recommend checking one of the early papers The Perceptron — A Perceiving and Recognizing Automaton (1957) by Frank Rosenblatt.

While it is absolutely not necessary for this course or for understanding neural networks in general, it can be interesting to look at the perceptron from a geometrical perspective. If you are interested, I have a lecture video on the geometric intuition behind the perceptron here.

If you can’t get enough of the perceptron, there is even a whole book devoted to the topic: Perceptrons (1969). (Note that this book was written and published to highlight the limitations of the perceptron.)

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