Deep Learning: Hands On Tutorials

A joint activity of 
METU Electrical and Electronics Engineering
METU-HU Neuroscience and Neurotechnology Ph. D. Program
Intel Nervana AI Academy METU Branch
METU VISION Research Lab
Tutorials by:
Egemen SERT, Barkın TUNCER, Sefa Burak OKCU, Eren HALICI, Fourough GHARBALCHI
Ugur HALICI, Prof. Dr.
A 12-week-long Deep Learning tutorial series including workshops for practice will be initiated on 
October 25, 2017 Wednesday (18:40-20:30 at U3). 
The tutorial on October 25 will be held at 19.00 due to maintenance on U3.

The topics starts from the Deep Learning Basics and will cover a broad range. 
For participants, familiarity with Multivariate Calculus, Linear Algebra and Python would be a plus; 
however, related topics will be covered in the class, hence familiarity is not a prerequisite.

Throughout the turotial series, participants will learn how to build object classifiers, text-to-text translators, 
Style Transfer models (https://github.com/fzliu/style-transfer) and so on.

Participants are required to fill the participation form. Students are welcome.

If you filled the form, please join our Deep Learning Community via the link below.

For workshops, bringing computers is a must; however, for regular classes, computers are not required.

The tentative topics for each week can be found below.

1. Introduction: What is Deep Learning? Why do we need data? 
            What can be done with DL? What is loss function intro
2. Neural Networks: What is regression, classification? 
            What is Neural Network, What does the Neural Network learn?
3. Neural Networks Training: What is Backpropagation? What is Gradient Descent? 
            How does the Neural Network learn?
4. WORKSHOP I: NN implementation, SGD, Dropout, Batch Normalization, Adam Optimizer,
            Train/Test Splits, Training CIFAR-10
5. Convolutional Neural Networks: What is Convolution? What is kernel/filter? What is pooling? 
            What is dilation? What is segmentation?
6. CNN Architectures: AlexNet, VGGNet, ResNet, GoogLeNet, Highway Net, UNet, DenseNet, 
            R-CNN, Faster-R-CNN…
7. WORKSHOP II: segmentation example? face detection example? (with Keras Framework)
8. Recurrent Neural Networks: What is an RNN? Why do the gradients vanish? How stable is vanilla RNN? 
            What is gating? What is LSTM & GRU?
9. Attention: What is Attention? What is External Memory? Neural Machine Translation, Image Captioning (w/ attn)…
10. WORKSHOP III: English-French translation, video search, image captioning (with Keras Framework)
11. Autoencoders: What is AE, GAN, VAE, Style Transfer
12. Deep Reinforcement Learning