Start Date: 11/17/2019
Course Type: Common Course |
Course Link: https://www.coursera.org/learn/intro-to-deep-learning
Explore 1600+ online courses from top universities. Join Coursera today to learn data science, programming, business strategy, and more.Welcome to the "Introduction to Deep Learning" course! In the first week you'll learn about linear models and stochatic optimization methods. Linear models are basic building blocks for many deep architectures, and stochastic optimization is used to learn every model that we'll discuss in our course.
The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding.
Article | Example |
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Deep learning | Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a class |
Deep learning | Recommendation systems have used deep learning to extract meaningful deep features for latent factor model for content-based recommendation for music. Recently, a more general approach for learning user preferences from multiple domains using multiview deep learning has been introduced. The model uses a hybrid collaborative and content-based approach and enhances recommendations in multiple tasks. |
Deep learning | A deep Q-network (DQN) is a type of deep learning model developed at Google DeepMind which combines a deep convolutional neural network with Q-learning, a form of reinforcement learning. Unlike earlier reinforcement learning agents, DQNs can learn directly from high-dimensional sensory inputs. Preliminary results were presented in 2014, with a paper published in February 2015 in Nature The application discussed in this paper is limited to Atari 2600 gaming, although it has implications for other applications. However, much before this work, there had been a number of reinforcement learning models that apply deep learning approaches (e.g.,). |
Deep learning | The probabilistic interpretation derives from the field of machine learning. It features inference, as well as the optimization concepts of training and testing, related to fitting and generalization respectively. More specifically, the probabilistic interpretation considers the activation nonlinearity as a cumulative distribution function. See Deep belief network. The probabilistic interpretation led to the introduction of dropout as regularizer in neural networks. |
Deep learning | Deep learning exploits this idea of hierarchical explanatory factors where higher level, more abstract concepts are learned from the lower level ones. These architectures are often constructed with a greedy layer-by-layer method. Deep learning helps to disentangle these abstractions and pick out which features are useful for learning. |
Deep learning | These definitions have in common (1) multiple layers of nonlinear processing units and (2) the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. The composition of a layer of nonlinear processing units used in a deep learning algorithm depends on the problem to be solved. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of complicated propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in Deep Belief Networks and Deep Boltzmann Machines. |
Deep learning | Such supervised deep learning methods also were the first artificial pattern recognizers to achieve human-competitive performance on certain tasks. |
Deep learning | Many deep learning algorithms are applied to unsupervised learning tasks. This is an important benefit because unlabeled data are usually more abundant than labeled data. Examples of deep structures that can be trained in an unsupervised manner are neural history compressors and deep belief networks. |
Deep learning | Deep learning algorithms are based on distributed representations. The underlying assumption behind distributed representations is that observed data are generated by the interactions of factors organized in layers. Deep learning adds the assumption that these layers of factors correspond to levels of abstraction or composition. Varying numbers of layers and layer sizes can be used to provide different amounts of abstraction. |
Deep learning | Deep learning algorithms transform their inputs through more layers than shallow learning algorithms. At each layer, the signal is transformed by a processing unit, like an artificial neuron, whose parameters are 'learned' through training. A chain of transformations from input to output is a "credit assignment path" (CAP). CAPs describe potentially causal connections between input and output and may vary in length – for a feedforward neural network, the depth of the CAPs (thus of the network) is the number of hidden layers plus one (as the output layer is also parameterized), but for recurrent neural networks, in which a signal may propagate through a layer more than once, the CAP is potentially unlimited in length. There is no universally agreed upon threshold of depth dividing shallow learning from deep learning, but most researchers in the field agree that deep learning has multiple nonlinear layers (CAP > 2) and Juergen Schmidhuber considers CAP > 10 to be very deep learning. |
Deep learning | Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. |
Deep learning | For supervised learning tasks, deep learning methods obviate feature engineering, by translating the data into compact intermediate representations akin to principal components, and derive layered structures which remove redundancy in representation. |
Deep learning | offering technical insights into how to integrate deep learning into the existing highly efficient, run-time speech decoding system deployed by all major players in speech recognition industry. The history of this significant development in deep learning has been described and analyzed in recent books and articles. |
Deep learning | If there is a lot of learnable predictability in the incoming data sequence, then the highest level RNN can use supervised learning to easily classify even deep sequences with very long time intervals between important events. In 1993, such a system already solved a "Very Deep Learning" task that requires more than 1000 subsequent layers in an RNN unfolded in time. |
Deep learning | Compound hierarchical-deep models compose deep networks with non-parametric Bayesian models. Features can be learned using deep architectures such as DBNs, DBMs, deep auto encoders, convolutional variants, ssRBMs, deep coding networks, DBNs with sparse feature learning, recursive neural networks, conditional DBNs, de-noising auto encoders. This provides a better representation, allowing faster learning and more accurate classification with high-dimensional data. However, these architectures are poor at learning novel classes with few examples, because all network units are involved in representing the input (a "distributed representation") and must be adjusted together (high degree of freedom). Limiting the degree of freedom reduces the number of parameters to learn, facilitating learning of new classes from few examples. "Hierarchical Bayesian (HB)" models allow learning from few examples, for example for computer vision, statistics, and cognitive science. |
Deep learning | Speech recognition has been revolutionised by deep learning, especially by Long short-term memory (LSTM), a recurrent neural network published by Sepp Hochreiter & Jürgen Schmidhuber in 1997. LSTM RNNs circumvent the vanishing gradient problem and can learn "Very Deep Learning" tasks that involve speech events separated by thousands of discrete time steps, where one time step corresponds to about 10 ms. |
Deep learning | In 2015, Blippar demonstrated a new mobile augmented reality application that makes use of deep learning to recognize objects in real time. |
Deep learning | Recently, a deep-learning approach based on an autoencoder artificial neural network has been used in bioinformatics, to predict Gene Ontology annotations and gene-function relationships. |
Deep learning | The first general, working learning algorithm for supervised deep feedforward multilayer perceptrons was published by Ivakhnenko and Lapa in 1965. A 1971 paper described a deep network with 8 layers trained by the Group method of data handling algorithm which is still popular in the current millennium. These ideas were implemented in a computer identification system "Alpha", which demonstrated the learning process. Other Deep Learning working architectures, specifically those built from artificial neural networks (ANN), date back to the Neocognitron introduced by Kunihiko Fukushima in 1980. The ANNs themselves date back even further. The challenge was how to train networks with multiple layers. |
Deep learning | In 2010, industrial researchers extended deep learning from TIMIT to large vocabulary speech recognition, by adopting large output layers of the DNN based on context-dependent HMM states constructed by decision trees. Comprehensive reviews of this development and of the state of the art as of October 2014 are provided in the recent Springer book from Microsoft Research. An earlier article reviewed the background of automatic speech recognition and the impact of various machine learning paradigms, including deep learning. |