AnitaKirkovska/machine_learning_course

Machine learning course at UCF

repo for CAP 5610 Machine Learning

Common ML problems, overview of (a) supervised, (b) unsupervised, and (c) reinforcement learning

• 1 Slides

ML terminology, linear regression, training & loss, gradient descent, stochastic gradient descent

• 2 Slides

Linear regression using the normal equation - numpy implementation

To understand the mathematics underlying the normal equation, read the following materials:

• Chapter 2 Linear Algebra

• Chapter 4 Numerical Computation, Section 4.3 Gradient-Based Optimization

• Chapter 5 Machine Learning Basics, Subsection 5.1.4 Example: Linear Regression

• Additional materials: proof of convexity of MSE and computation of gradient of MSE

• Colab notebook for solving linear regression using normal equation

Effect of learning rate on gradient descent

• Colab notebook for experimenting with different learning rates

Linear regression using gradient descent - numpy implementation

• Colab notebook for solving linear regression using gradient descent

Overview of TensorFlow and Keras

• 3 Slides

• Anatomy of a neural network

• 4 Slides

Keras examples

• Colab notebook for solving linear regression for artificial data set

• Colab notebook for loading and exploring the MNIST digits data set

• Colab notebook for classifying MNIST digits with dense layers and analyzing model performance

• Colab notebook for classifying MNIST fashion items with dense layers and analyzing model performance

• Colab notebook for displaying CIFAR10 data set

Generalization, overfitting, splitting data in train & test sets

• 5 Slides

Validation

• 6 Slides

Logistic regression, gradients for squared error and binary cross-entropy loss functions

• Logistic regression notes

Softmax, categorical cross entropy loss, gradients

• Softmax, categorical cross entropy

• Colab notebook for verifying formulas for partial derivatives with symbolic differentiation

Sequential neural networks with dense layers

• Notes on forward propagation, backpropagation algorithm for computing partial derivatives wrt weights and biases

• Code for creating sequential neural networks with dense layers and training them with backprop and mini-batch SGD; currently, code is limited to (1) mean squared error loss and (2) sigmoid activations.

Deep learning for computer vision (convolutional neural networks)

• CNN slides

TO DO: add note of preventing overfitting with data augmentation (also, add L2/L1 regularization and dropout earlier!)

Classification of MNIST digits and fashion items

• Colab notebook for classifying MNIST digits with convolutional layers and analyzing model performance

• Colab notebook for classifying MNIST fashion items with convolutional layers and anlyzing model performance

Classification of cats and dogs

based on Chapter 5 Deep learning for computer vision of the book Deep learning with Python by F. Chollet

• training convnet from scratch

• training convnet from scratch, using data augmentation and dropout

• using VGG16 conv base for fast feature extraction (data augmentation not possible), using dropout

• using VGG16 conv base for feature extraction, using data augmentation, not using dropout

• using VGG16 conv base for feature extraction, using data augmentation, not using dropout, fine-tuning

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based on Google ML Practicum: Image Classification

• Colab notebook for training a convolutional neural network from scratch

• Colab notebook for training a CNN from scratch with data augmentation and dropout

• Colab notebook for fine-tuning Google's Inception v3 model

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Visualizing what convnets learn

based on chapter 5 Deep learning for computer vision of the book Deep learning with Python by F. Chollet

• Visualizing intermediate activations

• Visualizing convnet filters, the convnet filter visualizations at the bottom of the notebook look pretty cool!

• Visualizing heatmaps of class activations

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Some cool looking stuff

Based on Section 8.2 DeepDream and Section 8.3 Neural style transfer of the book Deep learning with Python by F. Chollet. I am not going to explain in detail how deep dream and neural style transfer work. I just wanted to include these notebooks to show you two cool examples of what can be done with deep neural networks.

• Deep dream

• Neural style transfer

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Deep learning for computer vision (residual networks)

The goal is to introduce more advanced architectures and concepts. This is based onthe Keras documentation: CIFAR-10 ResNet.

The relevant research papers are:

• Deep residual learning for image recognition

• Identity mappings in deep residual networks

Notebooks

• Resnet for CIFAR10 - train/val/test

I have made several changes to the code from the Keras documentation. In the above notebook, I had to change the number of epochs and the learning rate schedule because the model is only trained on 40k and validated on 10k, whereas the model in the Keras documentation is trained on 50k and not validated at all. I wanted to have a situation that is similar to the situation in HW 2 so we can better compare the performance of the ResNet and the (normal) CNN.

• Resnet for CIFAR10- train/test

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Visualizing high-dimensional data using t-SNE

• How to use t-SNE effectively?

• PCA and t-SNE visualizations of MNIST fashion data set

• t-SNE visualization of feature vectors for MNIST fashion data set

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Deep learning for text and sequences (RNN, LSTM)

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Generative deep learning (VAE, GAN)

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Tools, additional materials