Dennis Núñez

PhD (c) in AI and Neuroimaging. CEA / Inria / Université Paris-Saclay

Artificial Intelligence


- "Deep Learning". Ian Goodfellow, Yoshua Bengio, Aaron Courville. [Link].

- "Machine Learning Yearning". Andrew Ng. [Link].

- "Deep Learning with Python". François Chollet. [Link].

- "Computer Vision: Models, Learning, and Inference". Simon J. D. Prince 2012. [Link].

- "Computer Vision: Theory and Application". Rick Szeliski 2010. [Link].

- "Computer Vision: A Modern Approach (2nd edition)". David Forsyth and Jean Ponce 2011. [Link].

- "Multiple View Geometry in Computer Vision". Richard Hartley and Andrew Zisserman 2004. [Link].

- "Computer Vision". Linda G. Shapiro 2001. [Link].

- "Visual Object Recognition synthesis lecture". Kristen Grauman and Bastian Leibe 2011. [Link].

- "High dynamic range imaging: acquisition, display, and image-based lighting". Reinhard, E., Heidrich, W., Debevec, P., Pattanaik, S., Ward, G., Myszkowski, K 2010. [Link].


- (great overview) "Deep learning". Y. LeCun, Y. Bengio, G. Hinton. [Link].

- (AlexNet paper) "ImageNet Classification with Deep Convolutional Neural Networks". A. Krizhevsky, I. Sutskever, G. Hinton. [Link].

- (GoogLeNet paper) "Going deeper with convolutions". Ch. Szegedy, W. Liu, Y.Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich. [Link].

Online courses

- Intro to Artificial Intelligence | Udacity. [Link].

- Practical Deep Learning For Coders | Harvard Business Review. [Link].

- Artificial Intelligence for Robotics | Udacity & Georgia Tech. [Link].

- Reinforcement Learning | Udacity & Georgia Tech. [Link].

- Machine Learning | Udacity & Georgia Tech. [Link].

- Artificial Intelligence (AI) | edX & Columbia University. [Link].

- Clasificación de imágenes: ¿cómo reconocer el contenido de una imagen? | Coursera. [Link].

- Mathematics for Machine Learning | Coursera. [Link].

- 6.S099: Artificial General Intelligence | MIT. [Link].

- Deep Reinforcement Learning | Udacity. [Link].

- CS231n: Convolutional Neural Networks for Visual Recognition | Stanford. [Link].

- ELEG 5040: Advanced Topics in Signal Processing (Introduction to Deep Learning) | CUHK. [Link].

- CS224d: Deep Learning for Natural Language Processing | Stanford. [Link].

- Deep Learning by Prof. Nando de Freitas | Oxford. [Link].

- Deep Learning by Prof. Yann LeCun | NYU. [Link].

- | Andrew Ng & Coursera. [Link].

- Yan LeCun's Lectures | Collège de France. [Link].

- EE-559 “Deep Learning” | École polytechnique fédérale de Lausanne. [Link].

- Deep Learning with TensorFlow | CognitiveClass. [Link].

- CS294-112: Deep Reinforcement Learning | UC Berkeley. [Link].

- 6.S191: Introduction to Deep Learning | MIT. [Link].

- Computer Vision | University of Washington. [Link].

- Computer Vision | NYU. [Link].

- Advances in Computer Vision | MIT. [Link].

- Computer Vision | RWTH Aachen University. [Link].

- Computer Vision | TU Dresden. [Link].

- Visual Object and Activity Recognition | UC Berkeley. [Link].

- Computer Vision: Foundations and Applications | Stanford. [Link].


- The Convergence of Machine Learning and Artificial Intelligence Towards Enabling Autonomous Driving. [Link].

- RI Seminar: Sergey Levine: Deep Robotic Learning. [Link].

- The Rise of Artificial Intelligence through Deep Learning | Yoshua Bengio | TEDxMontreal. [Link].

- Andrew Ng: Deep Learning, Self-Taught Learning and Unsupervised Feature Learning. [Link].

- Geoff Hinton: Recent Developments in Deep Learning. [Link].

- Yann LeCun: The Unreasonable Effectiveness of Deep Learning. [Link].

- Yoshua Bengio: Deep Learning of Representations. [Link].


- Tutorial on Deep Learning for Vision. [Link].


- Implementing Neural networks. [Link].


- Tensorflow: An open source software library for numerical computation using data flow graph by Google [Link].

- Torch7: Deep learning library in Lua, used by Facebook and Google Deepmind [Link].

- Torch-based deep learning libraries: [torchnet].

- Caffe: Deep learning framework by the BVLC [Link].

- Theano: Mathematical library in Python, maintained by LISA lab [Link].

- Theano-based deep learning libraries: [Pylearn2], [Blocks], [Keras], [Lasagne].

- MatConvNet: CNNs for MATLAB [Link].

- MXNet: A flexible and efficient deep learning library for heterog. distributed systems with multi-language support [Link].


- Neural Network Warehouse. [Link].

- UCI Machine Learning Repository Content (databases for your experiments). [Link].

- Neural Network labs around the world. [Link].

- Repo for the Deep Reinforcement Learning course from Udacity. [Link].

- Repo for Papers with code. Sorted by stars. Updated weekly. [Link].

- Awesome Deep Vision (GitHub). [Link].

- Awesome Capsule Networks (GitHub). [Link].

- Deep Learning Projects at CS230, Stanford. [Link].

- Uso libre del Supercomputador MANATI, Perú. [Link].

- Nvidia GPU Grant Program. [Link].

- Intel Movidius Neural Compute Stick. [Link].

- Edge TPU Devices for Embedded Devices. [Link].

- TensorFlow Lite for Microcontrollers. [Link].

- Deep Learning Drizzle at GitHub. [Link].

- OpenFace: Free and open source face recognition with deep neural networks. [Link].

- CVonline: Compendium of Computer Vision. [Link].

- Awesome Computer Vision (GitHub). [Link].