Artificial Intelligence

Artificial Neural Networks: Going Deeper | Lecture 3

Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. The neural network itself is not an algorithm. It is a framework for many different machine learning algorithms to work together and process complex data inputs. Such systems “learn” to perform tasks by considering examples, generally without being programmed with any task-specific rules.  

Artificial Neural Networks Demystified | Lecture 2

Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. The neural network itself is not an algorithm. It is a framework for many different machine learning algorithms to work together and process complex data inputs. Such systems “learn” to perform tasks by considering examples, generally without being programmed with any task-specific rules.  

Deep Learning Crash Course: Introduction | Lecture 1

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 models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains (especially human brains), which make them incompatible with neuroscience evidences.  

Deep Reinforcement Learning Through Policy Optimization

Reinforcement Learning (Deep RL) has seen several breakthroughs in recent years. In this tutorial we will focus on recent advances in Deep RL through policy gradient methods and actor critic methods. These methods have shown significant success in a wide range of domains, including continuous-action domains such as manipulation, locomotion, and flight. They have also achieved the state of the art in discrete action domains such as Atari. Fundamentally, there are two types of gradient calculations: likelihood ratio gradients (aka score function gradients) and path derivative gradients (aka perturbation analysis gradients). We will teach policy gradient methods of each type,…

Introduction to Artificial Neural Networks and Deep Learning

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This tutorial will teach you many of the core concepts behind neural networks and deep learning.  

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…

How Does Your Phone Know This Is A Dog?

What do voice search, machine translation, and ATMs have in common? Answer is Machine Learning. Learn more about it in this video.  

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.  

Adam Coates — Demystifying Unsupervised Feature Learning — UC Berkeley

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…

MIT 6.S094: Deep Reinforcement Learning for Motion Planning

This lecture introduces types of machine learning, the neuron as a computational building block for neural nets, q-learning, deep reinforcement learning, and the Deep Traffic simulation that utilizes deep reinforcement learning for the motion planning task.  

MIT 6.S094: Introduction to Deep Learning and Self-Driving Cars

Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms.   A self-driving car, also known as a robot car, autonomous car, or driverless car, is a vehicle that is capable of sensing its environment and moving with little or no human input.  

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.  

Introduction to Deep Learning with Python

Alec Radford, Head of Research at indico Data Solutions, speaking on deep learning with Python and the Theano library. The emphasis of the talk is on high performance computing, natural language processing using recurrent neural nets, and large scale learning with GPUs.  

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.