Introduction to Deep Learning and Convolutional Neural Networks (CNNs)

Name: Introduction to Deep Learning and Convolutional Neural Networks (CNNs)
Start: 2024-03-02T14:00:00+00:00
End: 2024-03-02T15:00:00+00:00

GDG Oxford

Dives into the world of deep learning. Shows why deep learning excels in image tasks, demystifies the architecture of CNNs (convolutions, pooling, etc.), and explains the training process.

Mar 2, 2024, 2:00 – 3:00 PM (UTC)

5 RSVP'd

Key Themes

AIBuild with AIMachine Learning

About this event

Feature Extraction:
Hand-crafted features: SIFT, HOG, LBP
Feature engineering and selection
Classification Algorithms:
k-Nearest Neighbors (kNN)
Support Vector Machines (SVM)
Decision Trees and Random Forests
Case Study: Building an image classifier using the techniques above (e.g., handwritten digit recognition)
The limitations of traditional approaches
Biological inspiration of neural networks
Building blocks of CNNs:
Convolutional layers
Pooling layers
Activation functions (ReLU, sigmoid)
Fully connected layers
Training CNNs:
Loss functions (cross-entropy)
Optimization algorithms (stochastic gradient descent, Adam)
Backpropagation

Organizer

Gregory McGann

United Nations