Introduction to Deep learning
- 1. Deep Learning Introduction, Design and Case Studies Massimiliano Patacchiola, PhD Student in Cognitive Robotics Centre for Robotics and Neural Systems , Plymouth University 1
- 2. Introduction What is deep Learning? 2
- 3. What is Deep Learning? “Deep Learning is a subfield of machine learning which uses supervised learning methods to train large artificial neural networks.” Machine Learning: discipline based on pattern recognition and learning theory which explores algorithms that can learn to make predictions. Supervised Learning: inferring a function from labeled training data (examples). 3
- 4. Artificial Neural Networks An Artificial Neural Network (ANN) is a network made by units (often called neurons) grouped in layers. Most of the time each unit in a layer is connected with all the units in the previous layer. Each connection is represented by a real value which is called weight or parameter. 4
- 5. Perceptron It is the simplest ANN, invented by Frank Rosenblatt in 1957. It has an input and an output layer and it can learn to discriminate only linearly separable functions. http://playground.tensorflow.org/ 5
- 6. Multilayer Perceptron The Multilayer Perceptron add one more layer to the perceptron. The intermediate layer is called hidden layer. The additional layer allows the network to solve the XOR problem. http://playground.tensorflow.org/ 6
- 7. Deep Neural Networks If the ANN has more than one hidden layer then it is a deep neural network (DNN). A DNN can model more complex spaces but is harder to train. Only recently thanks to Graphical Processing Units (GPUs), large datasets and new training techniques has been possible to use deep networks. 7
- 8. Convolutional Neural Networks Nowadays the most used deep architecture is the Convolutional Neural Network (CNN). The CNN take as input a three-dimensional image matrix which is processed with kernels and subsampling operations. The training search for the kernels that give the best prediction. 8
- 10. Training: Backpropagation 10 Training a Deep Neural Network is complicated and can require a long time (days) to get convergence. The standard approach for training DNN is backpropagation which treat learning as an optimisation problem. at each epoch the weights are adjusted in order to obtain an output which is more similar to the target.
- 11. Deep Design How to build a Deep Network? 11
- 12. Tools for Deep Learning 12 Tensorflow C++, Python Linux, Mac, Windows (next release) Keras Python Linux, Mac, Windows Caffe C++ Linux, Mac, Windows, Android Theano Python Linux, Mac, Windows CNTK C++ Linux, Windows Torch C, lua Linux, Mac, Windows, Android, iOS
- 13. Tensorflow TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. It deploys computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. Developed by researchers and engineers working on the Google Brain Team. 13
- 14. Multilayer Perceptron with Tensorflow 14https://www.kaggle.com/c/digit-recognizer
- 15. Convolutional Neural Network with Tensorflow 15https://www.kaggle.com/uciml/iris
- 16. Case Studies How is Deep Learning used? 16
- 17. Case Study: Image classification Given an input image the CNN returns the probability that the object present in that image belongs to a specific category. CIFAR-10 is an established computer-vision dataset used for object recognition. It is a subset of the 80 million tiny images dataset and consists of 60,000 32x32 color images containing one of 10 object classes. The best results have been achieved using CNNs, in particular recently a fractional max-pooling method achieved an accuracy of 96.53% 17
- 18. Case Study: Image colorization 18
- 19. Case Study: Deep Reinforcement Learning Reinforcement Learning: Given an environment with a set of states and an agent, the agent can move from state to state performing an action. Executing an action in a specific state provides the agent with a reward. Using RL it is possible to find the policy that maximise the long term reward. Deep Reinforcement Learning: Developed by Google DeepMind the first artificial agents to achieve human-level performance across many challenging domains. The key idea was to use deep neural networks to predict total reward. It achieved human-level performance in almost half of the 50 games to which it was applied, far beyond any previous method! 19
- 20. Case Study: Head Pose Estimation in the Wild 20“Head Pose Estimation in the Wild Using Convolutional Neural Networks and Adaptive Gradient Methods” Patacchiola, M., Cangelosi, A., (under review), 2016 We investigated the use of CNNs for head pose estimation. It is an hard classification task. We used three different CNNs for estimating separately roll, pitch and yaw. We obtained the best results ever reported on three datasets.
- 21. Case Study: Head Pose Estimation in the Wild 21“Head Pose Estimation in the Wild Using Convolutional Neural Networks and Adaptive Gradient Methods” Patacchiola, M., Cangelosi, A., (under review), 2016 Deepgaze is a python library for people detection and tracking which uses Convolutional Neural Networks (CNNs) to estimate the Focus of Attention (FOA) of users. The FOA can be approximately estimated finding the head orientation. This is particularly useful when the eyes are covered, or when the user is too far from the camera to grab the eye region with a good resolution. When the eye region is visible it is possible to estimate the gaze direction, which is much more informative and can give a good indication of the FOA. https://github.com/mpatacchiola/deepgaze
- 22. Thank you Email: [email protected] Github: mpatacchiola 22