Computer Vision – Techiespedia.org

Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition

Lecture 1 gives an introduction to the field of computer vision, discussing its history and key challenges. We emphasize that computer vision encompasses a wide variety of different tasks, and ...that despite the recent successes of deep learning we are still a long way from realizing the goal of human-level visual intelligence.

Keywords: Computer vision, Cambrian Explosion, Camera Obscura, Hubel and Wiesel, Block World, Normalized Cut, Face Detection, SIFT, Spatial Pyramid Matching, Histogram of Oriented Gradients, PASCAL Visual Object Challenge, ImageNet Challenge

Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture1.pdf

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Convolutional Neural Networks for Visual Recognition

Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/

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 lecture collection 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. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Website:
http://cs231n.stanford.edu/

For additional learning opportunities please visit:
http://online.stanford.edu/

Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition

Lecture 1 gives an introduction to the field of computer vision, [...]

Lecture 2 | Image Classification

Lecture 2 formalizes the problem of image classification. We discuss [...]

Lecture 3 | Loss Functions and Optimization

Lecture 3 continues our discussion of linear classifiers. We introduce [...]

Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model’s predictions, and discuss two commonly used loss ...functions for image classification: the multiclass SVM loss and the multinomial logistic regression loss. We introduce the idea of regularization as a mechanism to fight overfitting, with weight decay as a concrete example. We introduce the idea of optimization and the stochastic gradient descent algorithm. We also briefly discuss the use of feature representations in computer vision.

Keywords: Image classification, linear classifiers, SVM loss, regularization, multinomial logistic regression, optimization, stochastic gradient descent

Slides:
http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture3.pdf

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Convolutional Neural Networks for Visual Recognition

Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/

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 lecture collection 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. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Website:
http://cs231n.stanford.edu/

For additional learning opportunities please visit:
http://online.stanford.edu/

Lecture 4 | Introduction to Neural Networks

In Lecture 4 we progress from linear classifiers to fully-connected [...]

Lecture 5 | Convolutional Neural Networks

In Lecture 5 we move from fully-connected neural networks to [...]

Lecture 6 | Training Neural Networks I

In Lecture 6 we discuss many practical issues for training modern [...]

Lecture 7 | Training Neural Networks II

Lecture 7 continues our discussion of practical issues for training [...]

Lecture 8 | Deep Learning Software

In Lecture 8 we discuss the use of different software packages for [...]

Lecture 10 | Recurrent Neural Networks

In Lecture 10 we discuss the use of recurrent neural networks for [...]

Lecture 9 | CNN Architectures

In Lecture 9 we discuss some common architectures for convolutional [...]

Lecture 11 | Detection and Segmentation

In Lecture 11 we move beyond image classification, and show how [...]

In Lecture 11 we move beyond image classification, and show how convolutional networks can be applied to other core computer vision tasks. We show how fully convolutional networks equipped with ...downsampling and upsampling layers can be used for semantic segmentation, and how multitask losses can be used for localization and pose estimation. We discuss a number of methods for object detection, including the region-based R-CNN family of methods and single-shot methods like SSD and YOLO. Finally we show how ideas from semantic segmentation and object detection can be combined to perform instance segmentation.

Keywords: Semantic segmentation, fully convolutional networks, unpooling, transpose convolution, localization, multitask losses, pose estimation, object detection, sliding window, region proposals, R-CNN, Fast R-CNN, Faster R-CNN, YOLO, SSD, DenseCap, instance segmentation, Mask R-CNN

Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture11.pdf

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Convolutional Neural Networks for Visual Recognition

Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/

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 lecture collection 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. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Website:
http://cs231n.stanford.edu/

For additional learning opportunities please visit:
http://online.stanford.edu/

Lecture 11 | Detection and Segmentation

In Lecture 11 we move beyond image classification, and show how [...]

Lecture 12 | Visualizing and Understanding

In Lecture 12 we discuss methods for visualizing and understanding the [...]

Lecture 13 | Generative Models

In Lecture 13 we move beyond supervised learning, and discuss [...]

Lecture 14 | Deep Reinforcement Learning

In Lecture 14 we move from supervised learning to reinforcement [...]

In Lecture 14 we move from supervised learning to reinforcement learning (RL), in which an agent must learn to interact with an environment in order to maximize its reward. We ...formalize reinforcement learning using the language of Markov Decision Processes (MDPs), policies, value functions, and Q-Value functions. We discuss different algorithms for reinforcement learning including Q-Learning, policy gradients, and Actor-Critic. We show how deep reinforcement learning has been used to play Atari games and to achieve super-human Go performance in AlphaGo.

Keywords: Reinforcement learning, RL, Markov decision process, MDP, Q-Learning, policy gradients, REINFORCE, actor-critic, Atari games, AlphaGo

Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture14.pdf

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Convolutional Neural Networks for Visual Recognition

Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/

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 lecture collection 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. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Website:
http://cs231n.stanford.edu/

For additional learning opportunities please visit:
http://online.stanford.edu/

Lecture 15 | Efficient Methods and Hardware for Deep Learning

In Lecture 15, guest lecturer Song Han discusses algorithms and [...]

In Lecture 15, guest lecturer Song Han discusses algorithms and specialized hardware that can be used to accelerate training and inference of deep learning workloads. We discuss pruning, weight sharing, ...quantization, and other techniques for accelerating inference, as well as parallelization, mixed precision, and other techniques for accelerating training. We discuss specialized hardware for deep learning such as GPUs, FPGAs, and ASICs, including the Tensor Cores in NVIDIA’s latest Volta GPUs as well as Google’s Tensor Processing Units (TPUs).

Keywords: Hardware, CPU, GPU, ASIC, FPGA, pruning, weight sharing, quantization, low-rank approximations, binary networks, ternary networks, Winograd transformations, EIE, data parallelism, model parallelism, mixed precision, FP16, FP32, model distillation, Dense-Sparse-Dense training, NVIDIA Volta, Tensor Core, Google TPU, Google Cloud TPU

Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture15.pdf

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Convolutional Neural Networks for Visual Recognition

Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/

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 lecture collection 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. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Website:
http://cs231n.stanford.edu/

For additional learning opportunities please visit:
http://online.stanford.edu/

Lecture 16 | Adversarial Examples and Adversarial Training

In Lecture 16, guest lecturer Ian Goodfellow discusses adversarial [...]

In Lecture 16, guest lecturer Ian Goodfellow discusses adversarial examples in deep learning. We discuss why deep networks and other machine learning models are susceptible to adversarial examples, and how ...adversarial examples can be used to attack machine learning systems. We discuss potential defenses against adversarial examples, and uses for adversarial examples for improving machine learning systems even without an explicit adversary.

Keywords: Adversarial examples, Fooling images, fast gradient sign method, Clever Hans, adversarial defenses, adversarial examples in the physical world, adversarial training, virtual adversarial training, model-based optimization

Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture16.pdf

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Convolutional Neural Networks for Visual Recognition

Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/

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 lecture collection 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. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Website:
http://cs231n.stanford.edu/

For additional learning opportunities please visit:
http://online.stanford.edu/