collection curated by charger https://academictorrents.com/collection/courses @article{, title= {[Coursera] - (Operating Systems) by Professor Chen Xiangqun (Peking University)}, keywords= {}, journal= {}, author= {Coursera}, year= {}, url= {}, license= {}, abstract= {}, superseded= {}, terms= {} } https://academictorrents.com/download/3fca7db35ba4773d416cc6a297a196cec41fa9f4 @article{, title= {[Coursera] Web Intelligence and Big Data by Dr. Gautam Shroff}, keywords= {}, journal= {}, author= {Coursera}, year= {}, url= {}, license= {}, abstract= {}, superseded= {}, terms= {} } https://academictorrents.com/download/f871965e36ec21677d44375930d6cbb0dab7bfb3 Course Description Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge. https://academictorrents.com/download/46c5af9e2075d9af06f280b55b65cf9b44eb9fe7 Here is the list of topics covered in the course, segmented over 10 weeks. Each week is associated with explanatory video clips and recommended readings. 0. Introduction and math revision 1. Feedforward neural network 2. Training neural networks 3. Conditional random fields 4. Training CRFs 5. Restricted Boltzmann machine 6. Autoencoders 7. Deep learning 8. Sparse coding 9. Computer vision 10. Natural language processing https://academictorrents.com/download/e046bca3bc837053d1609ef33d623ee5c5af7300