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Machine Learning Workshop

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GDSCTUe

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.

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Neural Style Transfer Nidhish Shah <GDSC Tech Lead> Teaching neural networks to paint.
What are ConvNets? - Type of neural networks that specialise in images and their classification. The LeNet5 architecture. (source)
- 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)
Convolution Operation ConvNets have a receptive field defined by their kernels which are used to learn features. The Convolution Operation (source)
Example Convolution Operations An intuitive visualisation of convolutional operator (source)
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?
We take the outputs of the conv1_1, conv2_1, conv3_1, conv4_1 and conv5_1 VGG Architecture The VGG-19 Architecture (source)
Loss Function for NST
See you on Google Colab! https://drive.google.com/drive/folders/1ibVbqlz Xl9yrHPPzC2OJKHRe5cPykek7?usp=sharing

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Machine Learning Workshop

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