Convolutional Neural Networks Explained | Lecture 7

In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery.   CNNs use a variation of multilayer perceptrons designed to require minimal preprocessing. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics. &nbsp Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. Individual cortical neurons respond to stimuli only in a restricted region of the visual field known as…

Data Collection and Preprocessing | Lecture 6

Data preprocessing is a data mining technique. It involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. It is a proven method of resolving such issues. It prepares raw data for further processing.   Data preprocessing is used database-driven applications such as customer relationship management and rule-based applications (like neural networks).  

Regularization | Lecture 5

Regularization is a technique which makes slight modifications to the learning algorithm such that the model generalizes better. This in turn improves the model’s performance on the unseen data as well.  

Overfitting, Underfitting, and Model Capacity | Lecture 4

Overfitting refers to a model that models the training data too well.   Underfitting refers to a model that can neither model the training data nor generalize to new data.   Model capacity is the amount of elements that a model can fit.  

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…