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Deep Learning: Intelligence from Big Data

A machine learning approach inspired by the human brain, Deep Learning is taking many industries by storm. Empowered by the latest generation of commodity computing, Deep Learning begins to derive significant value from Big Data. It has already radically improved the computer’s ability to recognize speech and identify objects in images, two fundamental hallmarks of human intelligence.   Industry giants such as Google, Facebook, and Baidu have acquired most of the dominant players in this space to improve their product offerings. At the same time, startup entrepreneurs are creating a new paradigm, Intelligence as a Service, by providing APIs that…

The wonderful and terrifying implications of computers that can learn

What happens when we teach a computer how to learn? Technologist Jeremy Howard shares some surprising new developments in the fast-moving field of deep learning, a technique that can give computers the ability to learn Chinese, or to recognize objects in photos, or to help think through a medical diagnosis. (One deep learning tool, after watching hours of YouTube, taught itself the concept of “cats.”) Get caught up on a field that will change the way the computers around you behave … sooner than you probably think.  

Making Sense of the World with Deep Learning – Adam Coates, Stanford University

Developing computer systems capable of understanding the world will require algorithms that learn patterns and high-level concepts without the extensive aid of humans. Though a great deal of progress has been made on applications by training deep artificial neural networks from human-provided annotations, recent research has also explored methods to train such networks from unlabeled data. These “unsupervised” learning methods attempt to discover useful features of the data that can be used for other machine learning tasks. In some cases, we find that neural networks trained in this way are able to detect meaningful patterns on their own without any…

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…

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