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
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
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
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
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…!!
The interactions of neurons is not merely electrical, though, but electro-chemical. Each axon terminal contains thousands of membrane-bound sacs called vesicles, which in turn contain thousands of neurotransmitter molecules each. Neurotransmitters are chemical messengers which relay, amplify and modulate signals between neurons and other cells. The two most common neurotransmitters in the brain are the amino acids glutamate and GABA
Loss function : need not be a convex fucntion
Vanishing gradient problem : 1992 with his student; Sepp Hochreiter ; Jurgen Schmidhuber
ReLU : even a small idea can bring a large change
HGX-1 with 8 tesla V-100; DGX-1 with 8 tesla P100 with $129k ; P100s in HPC
Inspiring story for young researchers : Never give up what you believe in;
Yann Lecunn – CVPR story