The document provides an introduction to deep learning, covering its history, key concepts like convolutional neural networks, and research focuses in image segmentation. It discusses various applications of deep learning across fields such as computer vision, natural language processing, and robotics, as well as current activities at the University of Leeds. The text emphasizes the evolution of deep learning, addressing challenges and advancements including containerization for high-performance computing.

1. Introduction to
Deep Learning
Leo Pauly
1st year PhD Researcher in Computer vision & Deep Learning
University of Leeds

2. Contents
• What is deep learning ?
• Convolutional neural networks : explained
• My research : Deep learning based Image segmentation
• Applications in different domains
• Deep learning with HPC Leeds
• Future activities of Deep Learning @ Leeds

3. TLU : threshold logic unit
1943: Warren McCulloch and Walter Pitts create a computational model for neural networks based on mathematics and
algorithms called threshold logic.

4. Perceptron
1958: Frank Rosenblatt creates the perceptron, an algorithm for pattern recognition based on a two-layer
computer neural network using simple addition and subtraction
Activation function

5. Similarity with biological neurons

6. A simple neural network

7. How neural networks work
Optimisation algorithm :
Mini batch stochastic gradient
descendant algorithm

8. AI winter (90’s to early 2000)
Computational power
Difficulty to train larger network : Vanishing gradient problem
Lack of large dataset

9. Resurgence as Deep learning (Mid 2000-Present)

10. Resurgence as Deep learning (Mid 2000-Present)
Vanishing gradient problem : ReLUs [1]
[1] Nair, Vinod, and Geoffrey E. Hinton. "Rectified linear units improve restricted boltzmann machines." Proceedings of the 27th international conference on machine learning (ICML-10). 2010

11. Resurgence as Deep learning (Mid 2000-Present)
Computational powerVanishing gradient problem : ReLUs

12. Resurgence as Deep learning (Mid 2000-Present)
Computational powerVanishing gradient problem : ReLUs
Lack of large dataset

13. Resurgence as Deep learning (Mid 2000-Present)
Articles to read:
- A brief historyof neural nets and deep learning
- Welcome to the AI Conspiracy: The 'Canadian Mafia'
Yann LeCun,
New York University & Facebook
Yoshua Bengio,
Universite de Montreal
Geoffrey Hinton,
Google & University of Toronto
Jurgen Schmidhuber, Dalle Molle
Institutefor ArtificialIntelligence
Research

14. Deeper neural networks rebranded as deep learning

15. Deeper neural networks rebranded as deep learning

16. Deeper neural networks rebranded as deep learning

17. Revolution of depth

18. Amount of data vs performance

19. Types of networks used for deep learning
• Convolutional neural networks

20. Types of networks used for deep learning
• Convolutional neural networks
• Recurrent neural networks

21. Types of networks used for deep learning
• Convolutional neural networks
• Recurrent neural networks
• Long Short term memory (LSTM) networks

22. Types of networks used for deep learning
• Convolutional neural networks
• Recurrent neural networks
• Long Short term memory (LSTM) networks
• Deep Boltzmann machines

23. Types of networks used for deep learning
• Convolutional neural networks
• Recurrent neural networks
• Long Short term memory (LSTM) networks
• Deep Boltzmann machines
• Deep Q-networks

24. Types of networks used for deep learning
• Convolutional neural networks
• Recurrent neural networks
• Long Short term memory (LSTM) networks
• Deep Boltzmann machines
• Deep Q-networks
• Deep belief networks

25. Types of networks used for deep learning
• Convolutional neural networks
• Recurrent neural networks
• Long Short term memory (LSTM) networks
• Deep Boltzmann machines
• Deep Q-networks
• Deep belief networks
• Deep stacking networks

26. Convolutional Neural Networks

27. 1. Convolutional operation in convolutional layer

28. Link: chttps://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
3.Pooling operation in pooling layer2. ReLU operating in activation layer

30. My research
My research interests: Intersection of Computer vision, Machine Learning & Robotics
My current research focus for PhD: Image segmentation & Deep learning
Theoretical level:
- Conditional/ Parametric CNNs for segmentation
- Transfer learning for segmentation
Application level:
- Pavement crack segmentation

31. Parametric / Conditional CNN
Exploring the possibility of using extra parameter to steer the output of the network in one direction
Additional control parameter
Input image Output image

32. Transfer learning
Training machine learning models in one domain and deploying it in another domain:
Training:
Deploying:
Deep learning model
Deep learning model

33. Applications of Deep learning

34. Applications of Deep learning

35. Applications of Deep learning

36. Applications of Deep learning

37. Applications of Deep learning

38. Applications of Deep learning

39. Applications of Deep learning

40. Computer Vision
Object detection [2]
Image classification [1]
Image segmentation [3]
Edge detection [4]
[1] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
[2] Girshick, Ross. "Fast r-cnn." Proceedings of the IEEE International Conference on Computer Vision. 2015.
[3] Zheng, Shuai, et al. "Conditional random fields as recurrent neural networks." Proceedings of the IEEE International Conference on Computer Vision. 2015.
[4] Xie, Saining, and Zhuowen Tu. "Holistically-nested edge detection." Proceedings of the IEEE International Conference on Computer Vision. 2015.

41. [1]Kafle, Kushal, and Christopher Kanan. "Visual Question Answering: Datasets, Algorithms, and Future Challenges." arXiv preprint arXiv:1610.01465 (2016).
Natural Language processing
Visual question answering [1]

42. [1] Levine, Sergey, et al. "Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection." arXiv preprint arXiv:1603.02199 (2016).
[2] Chen, Chenyi, et al. "Deepdriving: Learning affordance for direct perception in autonomous driving." Proceedings of the IEEE International Conference on Computer
Vision. 2015.
Robotics
Grasping objects[1]
Autonomous driving[2]

43. [1] Esteva, Andre, et al. "Dermatologist-level classification of skin cancer with deep neural networks." Nature 542.7639 (2017): 115-118.
[2] Maninis, Kevis-Kokitsi, et al. "Deep retinal image understanding." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer International
Publishing, 2016.
[3] Ramsundar, Bharath, et al. "Massively multitask networks for drug discovery." arXiv preprint arXiv:1502.02072 (2015).
Medicine
Skin cancer classification[1]
Retinal vessel segmentation[2]
Drug Discovery[3]

44. Several other applications
- Agriculture
- Game playing systems : AlphaGo
- Language-Language translation
- Synthetic sound generation
- Deep reinforcement learning in robotics
- So on……..

45. Deep learning using containers at HPC, Leeds
What & Why containers ?
+ + +
OpenCv + NLTK+
Numpy + Anaconda = DS1 Container in HPC
What it means to user ?
- Load container engine : Singularity / Docker
- Load container image
- Start writing code
Courtesy : Martin Callaghan, HPC, University of Leeds

46. Deep learning using containers at HPC, Leeds
Courtesy : Martin Callaghan, HPC, University of Leeds
- Demo…!!
- Guide
- Course on June 26th : Dockers & Containers
- P100s coming up…..!!!!!

47. Discussion : Deep learning @ Leeds
• Regular meetings :
- Start with monthly meetings ( July- Oct)
- Followed by biweekly meetings (1 research group meeting + 1 talk by a speaker)
• Mailing list
• Lightening talks @ Departments
• Robotics away day
• General discussion…!!

48. cnlp@leeds.ac.uk