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Generative Adversarial Networks | Lecture 13

Generative adversarial networks (GANs) are a class of artificial intelligence algorithms used in unsupervised machine learning, implemented by a system of two neural networks contesting with each other in a zero-sum game framework. They were introduced by Ian Goodfellow et al. in 2014. This technique can generate photographs that look at least superficially authentic to human observers, having many realistic characteristics (though in tests people can tell real from generated in many cases).  

Deep Unsupervised Learning | Lecture 12

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data. The clusters are modeled using a measure of similarity which is defined upon metrics such as Euclidean or probabilistic distance.  

Recurrent Neural Networks | Lecture 11

A recurrent neural network (RNN) is a class of artificial neural network where connections between nodes form a directed graph along a sequence. This allows it to exhibit temporal dynamic behavior for a time sequence. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.  

Optimization Tricks: momentum, batch-norm, and more | Lecture 10

Batch normalization is a technique for improving the performance and stability of artificial neural networks. It is a technique to provide any layer in a neural network with inputs that are zero mean/unit variance. Batch normalization was introduced in a 2015 paper. It is used to normalize the input layer by adjusting and scaling the activations.   Highlights: Stochastic Gradient Descent Momentum Algorithm Learning Rate Schedules Adaptive Methods: AdaGrad, RMSProp, and Adam Internal Covariate Shift Batch Normalization Weight Initialization Local Minima Saddle Points  

Transfer Learning | Lecture 9

Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. This area of research bears some relation to the long history of psychological literature on transfer of learning, although formal ties between the two fields are limited.  

How to Design a Convolutional Neural Network | Lecture 8

Convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery.   Designing a good model usually involves a lot of trial and error. It is still more of an art than science. The tricks and design patterns that I present in this video are mostly based on ‘folk wisdom’, my personal experience, and ideas that come from successful model architectures.  

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).  

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,…

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…

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