Great introduce to algorithm design. (Best to learn in parallel with SICP)
Nonnegative matrix factorization, tensor decomposition, sparse coding, learning mixture models, matrix completion and inference in graphical models. et.c
Introduction to Mathematical Programming MIT(X)This course is an introduction to linear optimization and its extensions emphasizing the underlying mathematical structures, geometrical ideas, algorithms and solutions of practical problems. The topics covered include: formulations, the geometry of linear optimization, duality theory, the simplex method, sensitivity analysis, robust optimization, large scale optimization network flows, solving problems with an exponential number of constraints and the ellipsoid method, interior point methods, semidefinite optimization, solving real world problems problems with computer software, discrete optimization formulations and algorithms.
Scientific efficient computation (Bremen course)Nvidia lib for improve computations.
NVDLAThe NVIDIA Deep Learning Accelerator (NVDLA) is a free and open architecture that promotes a standard way to design deep learning inference accelerators. With its modular architecture, NVDLA is scalable, highly configurable, and designed to simplify integration and portability. The hardware supports a wide range of IoT devices. Delivered as an open source project under the NVIDIA Open NVDLA License, all of the software, hardware, and documentation will be available on GitHub. Contributions are welcome.
Nvidia Teaching KitsThis class is a hands-on, project-based introduction to building scalable and high-performance software systems. Topics include performance analysis, algorithmic techniques for high performance, instruction-level optimizations, cache and memory hierarchy optimization, parallel programming, and building scalable distributed systems.
CE459: Programming for Performance, version 3 (Caltech)In this program, you’ll learn the underlying math and programming concepts that drive pattern recognition, object and image classification tasks, and object tracking systems. This course will cover the latest in deep learning architectures used in industry, and you’ll combine current computer vision and deep learning techniques to power a variety of applications. With the practical skills you gain in this course, you’ll be able to program your own applications, extract information from any kind of image and spatial data, and solve real-world challenges.
Course HomeBecome an expert in neural networks, and learn to implement them in Keras and TensorFlow. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and more.
Course HomeHere are some documents that provide information on Haar cascades.
Practical recommendations for gradient-based training of deep architectures by Yoshua Bengio
Deep Learning book - chapter 11.4: Selecting Hyperparameters by Ian Goodfellow, Yoshua Bengio, Aaron Courville
Neural Networks and Deep Learning book - Chapter 3: How to choose a neural network's hyper-parameters? by Michael Nielsen
Efficient BackProp (pdf) by Yann LeCun
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