Recent developments in Deep Learning

The presentation provided an overview of different approaches to object detection using deep learning including sliding window detection, region-based detection, and fully convolutional networks. It demonstrated how to set up object detection workflows in Caffe and DIGITS and discussed challenges in object detection such as background clutter and occlusion.

This document discusses quantization techniques for convolutional neural networks to improve performance. It examines quantizing models trained with floating point precision to fixed point to reduce memory usage and accelerate inference. Tensorflow and Caffe Ristretto quantization approaches are described and tested on MNIST and CIFAR10 datasets. Results show quantization reduces model size with minimal accuracy loss but increases inference time, likely due to limited supported operations.

A simplified way of approaching machine learning and deep learning from the ground up. The case for deep learning and an attempt to develop intuition for how/why it works. Advantages, state-of-the-art, and trends. Presented at NYU Center for Genomics for NY Deep Learning Meetup

Yinyin Liu presents at SD Robotics Meetup on November 8th, 2016. Deep learning has made great success in image understanding, speech, text recognition and natural language processing. Deep Learning also has tremendous potential to tackle the challenges in robotic vision, and sensorimotor learning in a robotic learning environment. In this talk, we will talk about how current and future deep learning technologies can be applied for robotic applications.

This is a 2 hours overview on the deep learning status as for Q1 2017. Starting with some basic concepts, continue to basic networks topologies , tools, HW/Accelerators and finally Intel's take on the the different fronts.

This presentation walks through the process of building an image classifier using Keras with a TensorFlow backend. It will give a basic understanding of image classification and show the techniques used in industry to build image classifiers. The presentation will start with building a simple convolutional network, augmenting the data, using a pretrained network, and finally using transfer learning by modifying the last few layers of a pretrained network. The classification will be based on the classic example of classifying cats and dogs. The code for the presentation can be found at https://github.com/rajshah4/image_keras, and the presentation will discuss how to extend the code to your own pictures to make a custom image classifier.

For the full video of this presentation, please visit: http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit For more information about embedded vision, please visit: http://www.embedded-vision.com Nagesh Gupta, CEO and Founder of Auviz Systems, presents the "Trade-offs in Implementing Deep Neural Networks on FPGAs" tutorial at the May 2015 Embedded Vision Summit. Video and images are a key part of Internet traffic—think of all the data generated by social networking sites such as Facebook and Instagram—and this trend continues to grow. Extracting usable information from video and images is thus a growing requirement in the data center. For example, object and face recognition are valuable for a wide range of uses, from social applications to security applications. Deep neural networks are currently the most popular form of convolutional neural networks (CNN) used in data centers for such applications. 3D convolutions are a core part of CNNs. Nagesh presents alternative implementations of 3D convolutions on FPGAs, and discusses trade-offs among them.

What is Deep Learning Why Now How it Wok Deep Learning Models Deep Learning Applications Applying Deep Learning

This document discusses small deep neural networks, their advantages, and their design. It notes that computer vision tasks now work well due to advances in deep learning. Small neural networks have advantages for applications requiring low power usage and real-time performance, such as in gadgets. Their smaller size allows for faster training, easier deployment on embedded devices, and continuous updating over-the-air. Recent advances in small networks like SqueezeNet achieve similar accuracy as larger networks but with much smaller size and parameters.

Wave Computing is a startup that has developed a new dataflow architecture called the Dataflow Processing Unit (DPU) to accelerate deep learning training by up to 1000x. Their initial market focus is on machine learning in the datacenter. They have invented a Coarse Grain Reconfigurable Array architecture that can statically schedule dataflow graphs onto a massive array of processors. Wave is now accepting qualified customers for its Early Access Program to provide select companies early access to benchmark Wave's machine learning computers before official sales begin.

CTO of Nervana, Amir Khosrowshahi presents at New Frontiers in Computing 2016, Cognitive Computing: to the Singularity and Beyond at Stanford.

This document discusses deep learning, including its relationship to artificial intelligence and machine learning. It describes deep learning techniques like artificial neural networks and how GPUs are useful for deep learning. Applications mentioned include computer vision, speech recognition, and bioinformatics. Both benefits like robustness and weaknesses like long training times are outlined. Finally, common deep learning algorithms, libraries and tools are listed.

深度學習圖像分割 課程目標與介紹 許多重要的應用程式在圖像中偵測一個以上的對象，這時候就必須將圖像分割成小空間區域並且標明不同種類。圖像分割常被應用在醫學圖像分析或是自主駕駛車等等領域。本課程將帶領學員透過 Tensorflow 架構，實際操作整理好的醫學影像與自主駕駛車資料庫做學習，您將有機會熟悉如何在複雜的醫療影像中分解不同類型的人體組織，血管或異常細胞，並進一步由原始影像分割出特定器官。

Large-scale deep learning with TensorFlow allows storing and performing computation on large datasets to develop computer systems that can understand data. Deep learning models like neural networks are loosely based on what is known about the brain and become more powerful with more data, larger models, and more computation. At Google, deep learning is being applied across many products and areas, from speech recognition to image understanding to machine translation. TensorFlow provides an open-source software library for machine learning that has been widely adopted both internally at Google and externally.

This document provides an overview and agenda for a Deep Learning with MXNet workshop. It begins with background on deep learning basics like biological and artificial neurons. It then introduces Apache MXNet and discusses its key features like scalability, efficiency, and programming models. The remainder of the document provides hands-on examples for attendees to train their first neural network using MXNet, including linear regression, MNIST digit classification using a multilayer perceptron, and convolutional neural networks.

Nervana's deep learning platform provides unprecedented computing power through specialized hardware. It includes a fast deep learning framework called Neon that is 10 times faster than other frameworks on GPUs. Neon also includes pre-trained models and is under active development to improve capabilities like distributed computing and integration with other frameworks. Nervana aims to make deep learning more accessible and applicable across industries like healthcare, automotive, finance, and more.

Slides from my talk on deep learning for computer vision at PyConZA on 2017/10/06. Description: The state-of-the-art in image classification has skyrocketed thanks to the development of deep convolutional neural networks and increases in the amount of data and computing power available to train them. The top-5 error rate in the ImageNet competition to predict which of 1000 classes an image belongs to has plummeted from 28% error in 2010 to just 2.25% in 2017 (human level error is around 5%). In addition to being able to classify objects in images (including not hotdogs), deep learning can be used to automatically generate captions for images, convert photos into paintings, detect cancer in pathology slide images, and help self-driving cars ‘see’. The talk will give an overview of the cutting edge and some of the core mathematical concepts and will also include a short code-first tutorial to show how easy it is to get started using deep learning for computer vision in python…

The document discusses Intel's quad-core processors, which contain four processing cores on a single chip. This allows higher performance with lower power consumption compared to single-core chips. Quad-core processors are designed to improve performance for applications like workstations, servers, gaming, and datacenter virtualization while reducing total cost of ownership. An example application described is a 3D mapping software that combines topographical and satellite data to model natural disasters, which would benefit from a multi-core platform.

The document describes the development and testing of a novel mathematical computing architecture called MaPU. Key highlights include a multi-granularity parallel storage system that enables simultaneous matrix row and column access, a high dimension data model, and a cascading pipeline with a state machine-based program model. The first MaPU chip was implemented on a 40nm process with 4 MaPU cores. Testing showed the MaPU core was up to 6.94 times faster than a similar TI C66x DSP core for various algorithms like FFT and matrix multiplication. Power analysis indicated tested power was within 8% of estimated power.

For the full video of this presentation, please visit: http://www.embedded-vision.com/industry-analysis/video-interviews-demos/overcoming-barriers-consumer-adoption-vision-enabled-produc For more information about embedded vision, please visit: http://www.embedded-vision.com John Feland, CEO and Founder of Argus Insights, presents the "Overcoming Barriers to Consumer Adoption of Vision-enabled Products and Services" tutorial at the May 2015 Embedded Vision Summit. Visual intelligence is being deployed in a growing range of consumer products, including smartphones, tablets, security cameras, laptops (especially with Intel’s RealSense push), and even smartwatches. The demos are always cool. But does vision work for regular consumers? Do consumers see vision as a value add or just another feature to be ignored? In this talk, John investigates the best and worst of consumer product embedded vision implementations as told by real consumers, based on Argus Insights’ extensive portfolio of consumer data. John examines where current products fall short of consumers’ needs. And, he illuminates successful implementations to show how their vision capabilities create value in the lives of consumers. Case studies will include examples from Dropcam, Intel RealSense, HTC’s M8, and vision-enabled drones such as the DJI Phantom 2 Vision+.

This document provides an overview of clustering and classification techniques. It defines clustering as organizing objects into groups of similar objects and discusses common clustering algorithms like k-means and hierarchical clustering. It also provides examples of how k-means works and references for further information.

This document describes the CIFAR-10 dataset for classifying images into 10 categories. It contains 60,000 32x32 color images split into 50,000 training and 10,000 test images. Two methods are proposed: Method 1 extracts patches and features from each image and uses SVM/kNN, while Method 2 uses LoG and HoG features to preserve shape before SVM/kNN classification. Experiments test different parameters, with the best accuracy around 42% using a 13-dimensional Fisher vector and RBF SVM kernel.

Unsupervised image classification is the process by which each image in a dataset is identified to be a member of one of the inherent categories present in the image collection without the use of labelled training samples. Unsupervised categorisation of images relies on unsupervised machine learning algorithms for its implementation. This paper identifies clustering algorithms and dimension reduction algorithms as the two main classes of unsupervised machine learning algorithms needed in unsupervised image categorisation, and then reviews how these algorithms are used in some notable implementation of unsupervised image classification algorithms.

The document discusses neural networks and how they can be viewed as functions. It describes how neural networks take input data and produce output predictions or classifications. The document outlines how neural networks have a layered structure where each layer is a function, and how the layers are composed together. It explains that neurons are the basic units of computation in each layer and how they operate. The document also discusses how neural network training works by optimizing the weights and biases in each layer to minimize error, and how matrix operations in neural networks can benefit from parallel processing on GPUs.

The document discusses the inefficiencies and dangers of the current transportation system and envisions how new technologies could lead to a safer, more efficient system. It notes that currently transportation involves a huge waste of resources, with cars spending most of their time parked and accidents causing many deaths each year due to human error. The vision is that autonomous, connected, shared, and electric vehicles could reduce accidents by 90%, increase vehicle utilization to 50%, and make drivetrains 85% efficient. This could lead to an 8 cent per mile transportation system.

This document discusses three types of hardware multithreading: coarse-grained, fine-grained, and simultaneous multithreading (SMT). Coarse-grained multithreading allows another thread to run during long stalls of the first thread. Fine-grained multithreading interleaves instructions from multiple threads in a round-robin fashion to hide stalls. SMT issues instructions from multiple threads in the same cycle by using register renaming and dynamic scheduling to maximize utilization.

For the full video of this presentation, please visit: http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2014-embedded-vision-summit-khronos For more information about embedded vision, please visit: http://www.embedded-vision.com Neil Trevett, President of Khronos and Vice President at NVIDIA, presents the "OpenVX Hardware Acceleration API for Embedded Vision Applications and Libraries" tutorial at the May 2014 Embedded Vision Summit. This presentation introduces OpenVX, a new application programming interface (API) from the Khronos Group. OpenVX enables performance and power optimized vision algorithms for use cases such as face, body and gesture tracking, smart video surveillance, automatic driver assistance systems, object and scene reconstruction, augmented reality, visual inspection, robotics and more. OpenVX enables significant implementation innovation while maintaining a consistent API for developers. OpenVX can be used directly by applications or to accelerate higher-level middleware with platform portability. OpenVX complements the popular OpenCV open source vision library that is often used for application prototyping.

Pythonによりコンピュータビジョンアルゴリズムを実装する内容。本スライドは作者が短期間で学んだ内容につき、誤りを含む可能性がございます。あらかじめご了承ください。

For the full video of this presentation, please visit: http://www.embedded-vision.com/platinum-members/altera/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit For more information about embedded vision, please visit: http://www.embedded-vision.com Deshanand Singh, Director of Software Engineering at Altera, presents the "Efficient Implementation of Convolutional Neural Networks using OpenCL on FPGAs" tutorial at the May 2015 Embedded Vision Summit. Convolutional neural networks (CNN) are becoming increasingly popular in embedded applications such as vision processing and automotive driver assistance systems. The structure of CNN systems is characterized by cascades of FIR filters and transcendental functions. FPGA technology offers a very efficient way of implementing these structures by allowing designers to build custom hardware datapaths that implement the CNN structure. One challenge of using FPGAs revolves around the design flow that has been traditionally centered around tedious hardware description languages. In this talk, Deshanand gives a detailed explanation of how CNN algorithms can be expressed in OpenCL and compiled directly to FPGA hardware. He gives detail on code optimizations and provides comparisons with the efficiency of hand-coded implementations.

For the full video of this presentation, please visit: http://www.embedded-vision.com/platinum-members/bdti/embedded-vision-training/videos/pages/may-2014-embedded-vision-summit-techni-0 For more information about embedded vision, please visit: http://www.embedded-vision.com Jeff Bier, President and co-founder of BDTI and founder of the Embedded Vision Alliance, presents the "Trends and Recent Developments in Processors for Vision" tutorial at the May 2014 Embedded Vision Summit. Processor suppliers are investing intensively in new processors for vision applications, employing a diverse range of architecture approaches to meet the conflicting requirements of high performance, low cost, energy efficiency, and ease of application development. In this presentation, Bier draws from BDTI's ongoing processor evaluation work to highlight significant recent developments in processors for vision applications, including mobile application processors, graphics processing units, and specialized vision processors. He also explores what BDTI considers to be the most significant trends in processors for vision—such as the increasing use of heterogeneous architectures—and the implications of these trends for system designers and application developers.

CVPR2016にてシモセラ・エドガー氏が発表した、StyleNetの紹介資料です。 "Fashion Style in 128 Floats: Joint Ranking and Classification using Weak Data for Feature Extraction," Edgar Simo-Serra and Hiroshi Ishikawa, in CVPR2016. 論文情報 http://hi.cs.waseda.ac.jp/~esimo/publications/SimoSerraCVPR2016.pdf プロジェクトページ http://hi.cs.waseda.ac.jp/~esimo/ja/research/stylenet/

For the full video of this presentation, please visit: http://www.embedded-vision.com/platinum-members/ceva/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-siegel For more information about embedded vision, please visit: http://www.embedded-vision.com Yair Siegel, Director of Segment Marketing at CEVA, presents the "Fast Deployment of Low-power Deep Learning on CEVA Vision Processors" tutorial at the May 2016 Embedded Vision Summit. Image recognition capabilities enabled by deep learning are benefitting more and more applications, including automotive safety, surveillance and drones. This is driving a shift towards running neural networks inside embedded devices. But, there are numerous challenges in squeezing deep learning into resource-limited devices. This presentation details a fast path for taking a neural network from research into an embedded implementation on a CEVA vision processor core, making use of CEVA’s neural network software framework. Siegel explains how the CEVA framework integrates with existing deep learning development environments like Caffe, and how it can be used to create low-power embedded systems with neural network capabilities.

For the full video of this presentation, please visit: http://www.embedded-vision.com/platinum-members/arm/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-iodice For more information about embedded vision, please visit: http://www.embedded-vision.com Gian Marco Iodice, Software Engineer at ARM, presents the "Using SGEMM and FFTs to Accelerate Deep Learning" tutorial at the May 2016 Embedded Vision Summit. Matrix Multiplication and the Fast Fourier Transform are numerical foundation stones for a wide range of scientific algorithms. With the emergence of deep learning, they are becoming even more important, particularly as use cases extend into mobile and embedded devices. In this presentation, lodice discusses and analyzes how these two key, computationally-intensive algorithms can be used to gain significant performance improvements for convolutional neural network (CNN) implementations. After a brief introduction to the nature of CNN computations, Iodice explores the use of GEMM (General Matrix Multiplication) and mixed-radix FFTs to accelerate 3D convolution. He shows examples of OpenCL implementations of these functions and highlights their advantages, limitations and trade-offs. Central to the techniques explored is an emphasis on cache-efficient memory accesses and the crucial role of reduced-precision data types.

For the full video of this presentation, please visit: http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2016-member-meeting-uofw For more information about embedded vision, please visit: http://www.embedded-vision.com Professor Jeff Bilmes of the University of Washington delivers the presentation "Image and Video Summarization" at the December 2016 Embedded Vision Alliance Member Meeting. Bilmes provides an overview of the state of the art in image and video summarization.

The document outlines the objectives, methodology, and work accomplished for a project involving designing an efficient convolutional neural network architecture for image classification. The objectives were to classify images using CNNs and design an effective CNN architecture. The methodology involved designing convolution and pooling layers, and using gradient descent to train the network. Work accomplished included GPU configuration, designing CNN architectures for CIFAR-10 and MNIST datasets, and tracking training loss, validation loss, and accuracy over epochs.

The document summarizes the key features and specifications of the Intel Core 2 Duo processor. It is a 64-bit dual-core processor introduced in 2006 as the successor to the Core Duo. Each of its cores are based on the Pentium M microarchitecture and have shorter pipelines, allowing for higher performance at lower clock speeds compared to previous architectures like the Pentium 4. The Core 2 Duo comes in desktop and notebook versions with performance about 20% lower in notebooks due to lower voltages and bus speeds.