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HC27-K1: Convolutional Neural Networks

This talk describes how convolutional neural networks work and can be used to make computers appear to “learn” and “think” in a way analogous to how the human brain works. Yann describes many practical applications of these networks, such as picture and facial recognition, text analysis, and the like.  

Meetup: Deep Learning – Theory and Applications

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.   Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human…

Artificial and robotic vision – lecture8_5 – more details on deep networks part 5

This course teaches the foundation of neural network models of the human visual system. The application is in synthetic and artificial vision, visual perception, visual intelligence for robots and automatic system. This course will teach how to use and write software models of the human visual system, retinal pre-processing, and vision sub-blocks. We will teach machine- and deep-learning neural networks system to learn to segment, track, categorize, classify, objects of interest in the scene. The course will also focus on techniques to perform full-scene understanding of a video stream, both with static and dynamic (motion) filters. We will discuss the…

Artificial and robotic vision – lecture3_2 – convolutional neural networks

This course teaches the foundation of neural network models of the human visual system. The application is in synthetic and artificial vision, visual perception, visual intelligence for robots and automatic system. This course will teach how to use and write software models of the human visual system, retinal pre-processing, and vision sub-blocks. We will teach machine- and deep-learning neural networks system to learn to segment, track, categorize, classify, objects of interest in the scene. The course will also focus on techniques to perform full-scene understanding of a video stream, both with static and dynamic (motion) filters. We will discuss the…

Neural networks [9.6] : Computer vision – convolutional network

A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus a neural network is either a biological neural network, made up of real biological neurons, or an artificial neural network, for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled as weights. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred as a linear combination. Finally, an activation function controls the amplitude of…

Developing Training Algorithms for Convolutional Neural Networks

During the last decades several visual recognition problems have been investigated. Image processing permits for example face detection, face recognition, facial expression analysis, car detection, optical character recognition, or hand written digit recognition. Neural networks (NN) have been found almost unavoidable in pattern recognition. In fact recognition systems are more efficient when they focus on learning techniques.   LeCun proposed convolutional neural networks (CNN) which are NN with three key architectural ideas: local receptive fields, weight sharing, and sub-sampling in the spatial domain. Networks are designed for the recognition of two-dimensional visual patterns. CNN have many strengths. Firstly, feature extraction…

Machine learning – Deep learning II, the Google autoencoders and dropout

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.  

Visual Perception with Deep Learning

A long-term goal of Machine Learning research is to solve highly complex “intelligent” tasks, such as visual perception auditory perception, and language understanding. To reach that goal, the ML community must solve two problems: the Deep Learning Problem, and the Partition Function Problem.   There is considerable theoretical and empirical evidence that complex tasks, such as invariant object recognition in vision, require “deep” architectures, composed of multiple layers of trainable non-linear modules. The Deep Learning Problem is related to the difficulty of training such deep architectures.   Several methods have recently been proposed to train (or pre-train) deep architectures in…

Recent Developments in Deep Learning

Deep networks can be learned efficiently from unlabeled data. The layers of representation are learned one at a time using a simple learning module that has only one layer of latent variables. The values of the latent variables of one module form the data for training the next module. Although deep networks have been quite successful for tasks such as object recognition, information retrieval, and modeling motion capture data, the simple learning modules do not have multiplicative interactions which are very useful for some types of data.   The talk will show how to introduce multiplicative interactions into the basic…

Bay Area Vision Meeting: Unsupervised Feature Learning and Deep Learning

Despite machine learning’s numerous successes, applying machine learning to a new problem usually means spending a long time hand-designing the input representation for that specific problem. This is true for applications in vision, audio, text/NLP, and other problems. To address this, researchers have recently developed “unsupervised feature learning” and “deep learning” algorithms that can automatically learn feature representations from unlabeled data, thus bypassing much of this time-consuming engineering. Building on such ideas as sparse coding and deep belief networks, these algorithms can exploit large amounts of unlabeled data (which is cheap and easy to obtain) to learn a good feature…

O’Reilly Webcast: Deep Learning – The Biggest Data Science Breakthrough of the Decade

Machine learning and AI have appeared on the front page of the New York Times three times in recent memory: 1) When a computer beat the world’s #1 chess player 2) When Watson beat the world’s best Jeopardy players 3) When deep learning algorithms won a chemo-informatics Kaggle competition.   We all know about the first two… but what’s that deep learning thing about? This happened in November of last year, and it represents a critical breakthrough in data science that every executive will need to know about and react to in the coming years. The NY Times said that…

Machine learning – Deep learning I

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.  

Trends in Deep Learning

This talk gives a brief history of deep learning architectures, moving into modern trends and research in the field. Key points of discussion are neural activation functions, weight optimization strategies, techniques for hyper-parameter selection, and example architectures for different problem sets. We finish with a few notable examples of “web scale” deep learning at work.   This talk will focus on (briefly) sklearn, Theano, pylearn2, theanets, and hyperopt.  

Machine Learning Discussion Group – Deep Learning w/ Stanford AI Lab (1 of 3)

Adam Coates will give an overview of some recent research projects from the Stanford Artificial Intelligence Lab and will do a presentation with open discussion on Deep Learning, an exciting recent addition to the machine learning algorithm family. The format will be interactive as Adam will answer questions from the group. So this will be a great opportunity to learn from one of the authorities on this exciting topic.  

Principles of Hierarchical Temporal Memory (HTM): Foundations of Machine Intelligence

Hierarchical temporal memory (HTM) is a biologically constrained theory (or model) of intelligence, originally described in the 2004 book On Intelligence by Jeff Hawkins with Sandra Blakeslee. HTM is based on neuroscience and the physiology and interaction of pyramidal neurons in the neocortex of the mammalian (in particular, human) brain.  

Deep Learning of Representations

Yoshua Bengio will give an introduction to the area of Deep Learning, to which he has been one of the leading contributors. It is aimed at learning representations of data, at multiple levels of abstraction. Current machine learning algorithms are highly dependent on feature engineering (manual design of the representation fed as input to a learner), and it would be of high practical value to design algorithms that can do good feature learning. The ideal features are disentangling the unknown underlying factors that generated the data. It has been shown both through theoretical arguments and empirical studies that deep architectures…

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