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README.md

These are the slides for this course.

Chapter 1: Supervised Learning

In this chapter we over the basics of Supervised Learning, where we train models using labeled data.

1.1. Linear Regression Part 1 - Part 2

1.2. Linear Classification

1.3. Logistic Regression

1.4. K-Nearest Neighbors

1.5. Ensemble Learning

Chapter 2: Unsupervised Learning

In this chapter we over the basics of Unsupervised Learning, where we train models using unlabeled data.

2.1. Clustering

2.2. Dimensionality Reduction

Chapter 3: Neural Networks

In this chapter we over the basics of Neural Networks, which are the building blocks of modern Deep Learning.

3.1. Introduction to Neural Networks

3.2. Backpropagation

3.3. Optimization

3.4. Regularization & Training Improvements

3.5. Batch Normalization

Chapter 4: Computer Vision

In this chapter we over the basics of Computer Vision, exploring how neural networks can be modified to solve different problems in Computer Vision.

4.1. Convolutional Neural Networks

4.2. Channels and Pooling

4.3. AlexNet and ResNet

4.4. AutoEncoders

4.5. Semantic Segmentation

Chapter 5: Natural Language Processing

In this chapter we over the basics of Natural Language Processing, exploring how neural networks can be modified to solve different problems in NLP.

5.1. Word Embeddings

5.2. Attention Mechanism

5.3. Bidirectional Encoder Representations from Transformers (BERT)

5.4. Large Language Models (LLM) and Adaptations

Chapter 6: Contrastive Learning

In this chapter we over the basics of Contrastive Learning, exploring how models trained on images and languages can be used together to enhance the performance of our models.

6.1. Vision Transformers (ViT)

6.2. Contrastive Language–Image Pre-training (CLIP)