Linear Classifier Network
Lecture 02: Image Classification
This lecture introduces the computer vision task - image classification. Then we explain a naive approach K nearest neighbor
Lecture 03: Linear Classifier & Regularization
This lecture introduces linear classifier and the concept of regularization.
- Linear classifier: An image classification method that enables the model learn from the training data
- Regularization: A method which enable us to tell the model our preference toward the final model and prevent overfitting
Lecture 04: Optimization
In the previous lecture, we learned the loss function which tells us how good our model is performing currently. In this lecture, we introduces “optimization”, which is the process utilizing the loss we’ve computed to improve the model
Lecture 05: Neural Networks
In this lecture, we introduces
- Feature Transform
- Neural Network
- Space Warping
- Universal Approximation
We can’t control our data distribution, but we can do feature transform to make them distribute in a way which we can easily classify Neural network gave us a way to tackle the problem we find in linear classifier. Space warping and universal approximation give us intuition about why and how neural network works
Lecture 06: Backpropagation
Backpropagation gives us an efficient and modular way to calculate gradient of the loss
Convolutional Network
Lecture 07: Convolutional Network
Convolutional Network (CNN) resolves the problem of neural network we’ve learnt in previous lecture, which flattens the image pixels and doesn’t respect the spacial structure of the image
Lecture 08: CNN Architectures
By introducing models in ImageNet classification challenges, this lecture taught us common rules and methods in creating CNN architectures
Lecture 10: Training Neural Network I
In this lecture, we talk in detail about the initializations before training the network: activation function choice, activation functions, data preprocessing, weight initialization, and regularization
Lecture 11: Training Neural Network II
- Learning Rate Schedule
- Tips and Tricks choosing hyperparameters
- Model Ensembles
- Transfer Learning
- Distributed Training
RNN and Transformers
Lecture 12: Recurrent Neural Network
This lecture introduce RNN, which is a kind of neural network that can deal with tasks where context and order of data matters. For example, speech understanding and stock price prediction over time
Lecture 13: Transformers
This lecture we start from the concept “attention”, which resolve the bottleneck problem in sequence to sequence. After that, we extend the to a new kind of layer, “attention layer”, which is a crucial part of the “transformer”
Other Computer Vision Tasks
Lecture 14: Visualizing and Understanding
This lecture we try to figure out what is actually happening in neural networks, and how do we use these findings to create interesting applications
Lecture 15: Object Detection
This lecture introduces object detection, a computer vision task that detects multiple objects in images and encloses them with bounding boxes.
Lecture 16: Image Segmentation
This lecture introduces a new computer vision task - “image segmentation”, where we want to classify each pixel in the image to a category
Lecture 17: 3D Vision
This lecture we focus on two tasks
- Predicting 3D shapes from single shape
- Processing 3D input data
Lecture 18: Videos
This lecture introduces computer vision tasks about videos
Lecture 19: Generative Models I
This lecture we first explain what is generative models? Then we introduce “Autoregressive Model” and “Variational Autoencoder”
Lecture 20: Generative Models II (GANs)
This lecture introduces Generative Adversarial Networks (GANs)
Lecture 21: Reinforcement Learning
This lecture introduces reinforcement learning