Join us on February 18th at 18:00 for a deep learning class where you'll receive hands-on experience with Pytorch to develop convolutional neural networks and expand your programming expertise. This workshop is a collaboration between GDSC TU/e, TU Delft, and AGU to bring students together under the same machine learning umbrella. Note: Some knowledge in Python is expected.

1. Neural Style Transfer
Nidhish Shah
<GDSC Tech Lead>
Teaching neural networks to paint.

2. What are ConvNets?
- Type of neural networks that specialise in images and
their classification.
The LeNet5 architecture. (source)

3. - Shallow layers learn low level features like edges.
- Deeper layers learn high level features like patterns and
objects.
What do ConvNets learn?
Visualising and Understanding ConvNets (source)

4. Convolution Operation
ConvNets have a receptive field defined by their kernels
which are used to learn features.
The Convolution Operation (source)

5. Example
Convolution Operations
An intuitive visualisation of convolutional operator (source)

6. Loss functions: Measure how far off the estimated
value is from the true value.
Optimizers: Tweaks the Neural Network in the hopes of
minimizing the loss function.
How do ConvNets learn?

7. We take the outputs of the conv1_1, conv2_1, conv3_1,
conv4_1 and conv5_1
VGG Architecture
The VGG-19 Architecture (source)

8. Loss Function for NST

9. See you on
Google Colab!
https://drive.google.com/drive/folders/1ibVbqlz
Xl9yrHPPzC2OJKHRe5cPykek7?usp=sharing