- Microsoft word 2013 introductory book pdf free

Looking for:

Deep learning - Wikipedia.Microsoft Word Part 1: Introduction download free tutorial in pdf 

Click here to DOWNLOAD

     

Microsoft Blog | Latest Product Updates and Insights.[PDF] Introduction to Microsoft Word

 

Deep learning also known as deep structured learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised , semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks , deep belief networks , deep reinforcement learning , recurrent neural networks , convolutional neural networks and Transformers have been applied to fields including computer vision , speech recognition , natural language processing , machine translation , bioinformatics , drug design , medical image analysis , climate science , material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.

Artificial neural networks ANNs were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, artificial neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic plastic and analogue.

The adjective "deep" in deep learning refers to the use of multiple layers in the network. Early work showed that a linear perceptron cannot be a universal classifier, but that a network with a nonpolynomial activation function with one hidden layer of unbounded width can. Deep learning is a modern variation which is concerned with an unbounded number of layers of bounded size, which permits practical application and optimized implementation, while retaining theoretical universality under mild conditions.

In deep learning the layers are also permitted to be heterogeneous and to deviate widely from biologically informed connectionist models, for the sake of efficiency, trainability and understandability, whence the "structured" part. For example, in image processing , lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.

Most modern deep learning models are based on artificial neural networks , specifically convolutional neural networks CNN s, although they can also include propositional formulas or latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep Boltzmann machines. In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation.

In an image recognition application, the raw input may be a matrix of pixels; the first representational layer may abstract the pixels and encode edges; the second layer may compose and encode arrangements of edges; the third layer may encode a nose and eyes; and the fourth layer may recognize that the image contains a face. Importantly, a deep learning process can learn which features to optimally place in which level on its own.

This does not eliminate the need for hand-tuning; for example, varying numbers of layers and layer sizes can provide different degrees of abstraction. The word "deep" in "deep learning" refers to the number of layers through which the data is transformed. More precisely, deep learning systems have a substantial credit assignment path CAP depth.

The CAP is the chain of transformations from input to output. CAPs describe potentially causal connections between input and output.

For a feedforward neural network , the depth of the CAPs is that of the network and is the number of hidden layers plus one as the output layer is also parameterized. For recurrent neural networks , in which a signal may propagate through a layer more than once, the CAP depth is potentially unlimited.

CAP of depth 2 has been shown to be a universal approximator in the sense that it can emulate any function. Deep learning architectures can be constructed with a greedy layer-by-layer method. For supervised learning tasks, deep learning methods eliminate feature engineering , by translating the data into compact intermediate representations akin to principal components , and derive layered structures that remove redundancy in representation.

Deep learning algorithms can be applied to unsupervised learning tasks. This is an important benefit because unlabeled data are more abundant than the labeled data. Examples of deep structures that can be trained in an unsupervised manner are deep belief networks. Deep neural networks are generally interpreted in terms of the universal approximation theorem [16] [17] [18] [19] [20] or probabilistic inference. The classic universal approximation theorem concerns the capacity of feedforward neural networks with a single hidden layer of finite size to approximate continuous functions.

The universal approximation theorem for deep neural networks concerns the capacity of networks with bounded width but the depth is allowed to grow. Lu et al. The probabilistic interpretation [21] derives from the field of machine learning.

It features inference, [8] [9] [10] [12] [15] [21] 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. The probabilistic interpretation was introduced by researchers including Hopfield , Widrow and Narendra and popularized in surveys such as the one by Bishop. Some sources point out that Frank Rosenblatt developed and explored all of the basic ingredients of the deep learning systems of today.

The first general, working learning algorithm for supervised, deep, feedforward, multilayer perceptrons was published by Alexey Ivakhnenko and Lapa in The term Deep Learning was introduced to the machine learning community by Rina Dechter in , [28] and to artificial neural networks by Igor Aizenberg and colleagues in , in the context of Boolean threshold neurons. In , Yann LeCun et al. While the algorithm worked, training required 3 days.

Independently in , Wei Zhang et al. Each layer in the feature extraction module extracted features with growing complexity regarding the previous layer. In , Brendan Frey demonstrated that it was possible to train over two days a network containing six fully connected layers and several hundred hidden units using the wake-sleep algorithm , co-developed with Peter Dayan and Hinton.

Since , Sven Behnke extended the feed-forward hierarchical convolutional approach in the Neural Abstraction Pyramid [44] by lateral and backward connections in order to flexibly incorporate context into decisions and iteratively resolve local ambiguities. Simpler models that use task-specific handcrafted features such as Gabor filters and support vector machines SVMs were a popular choice in the s and s, because of artificial neural network 's ANN computational cost and a lack of understanding of how the brain wires its biological networks.

Both shallow and deep learning e. Most speech recognition researchers moved away from neural nets to pursue generative modeling. An exception was at SRI International in the late s. The speaker recognition team led by Larry Heck reported significant success with deep neural networks in speech processing in the National Institute of Standards and Technology Speaker Recognition evaluation.

The principle of elevating "raw" features over hand-crafted optimization was first explored successfully in the architecture of deep autoencoder on the "raw" spectrogram or linear filter-bank features in the late s, [52] showing its superiority over the Mel-Cepstral features that contain stages of fixed transformation from spectrograms.

The raw features of speech, waveforms , later produced excellent larger-scale results. Many aspects of speech recognition were taken over by a deep learning method called long short-term memory LSTM , a recurrent neural network published by Hochreiter and Schmidhuber in In , LSTM started to become competitive with traditional speech recognizers on certain tasks.

In , publications by Geoff Hinton , Ruslan Salakhutdinov , Osindero and Teh [59] [60] [61] showed how a many-layered feedforward neural network could be effectively pre-trained one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine , then fine-tuning it using supervised backpropagation.

Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision and automatic speech recognition ASR. The NIPS Workshop on Deep Learning for Speech Recognition was motivated by the limitations of deep generative models of speech, and the possibility that given more capable hardware and large-scale data sets that deep neural nets DNN might become practical. It was believed that pre-training DNNs using generative models of deep belief nets DBN would overcome the main difficulties of neural nets.

DNN models, stimulated early industrial investment in deep learning for speech recognition, [69] eventually leading to pervasive and dominant use in that industry. That analysis was done with comparable performance less than 1.

In , 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. Advances in hardware have driven renewed interest in deep learning. In , a team led by George E. Dahl won the "Merck Molecular Activity Challenge" using multi-task deep neural networks to predict the biomolecular target of one drug.

Significant additional impacts in image or object recognition were felt from to In October , a similar system by Krizhevsky et al. In November , Ciresan et al. Image classification was then extended to the more challenging task of generating descriptions captions for images, often as a combination of CNNs and LSTMs. Some researchers state that the October ImageNet victory anchored the start of a "deep learning revolution" that has transformed the AI industry. In March , Yoshua Bengio , Geoffrey Hinton and Yann LeCun were awarded the Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.

Artificial neural networks ANNs or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains. Such systems learn progressively improve their ability to do tasks by considering examples, generally without task-specific programming. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the analytic results to identify cats in other images.

They have found most use in applications difficult to express with a traditional computer algorithm using rule-based programming. An ANN is based on a collection of connected units called artificial neurons , analogous to biological neurons in a biological brain. Each connection synapse between neurons can transmit a signal to another neuron. The receiving postsynaptic neuron can process the signal s and then signal downstream neurons connected to it. Neurons may have state, generally represented by real numbers , typically between 0 and 1.

Neurons and synapses may also have a weight that varies as learning proceeds, which can increase or decrease the strength of the signal that it sends downstream. Typically, neurons are organized in layers. Different layers may perform different kinds of transformations on their inputs.

Signals travel from the first input , to the last output layer, possibly after traversing the layers multiple times. The original goal of the neural network approach was to solve problems in the same way that a human brain would. Over time, attention focused on matching specific mental abilities, leading to deviations from biology such as backpropagation , or passing information in the reverse direction and adjusting the network to reflect that information.

Neural networks have been used on a variety of tasks, including computer vision, speech recognition , machine translation , social network filtering, playing board and video games and medical diagnosis. As of , neural networks typically have a few thousand to a few million units and millions of connections. Despite this number being several order of magnitude less than the number of neurons on a human brain, these networks can perform many tasks at a level beyond that of humans e.

A deep neural network DNN is an artificial neural network ANN with multiple layers between the input and output layers. For example, a DNN that is trained to recognize dog breeds will go over the given image and calculate the probability that the dog in the image is a certain breed. The user can review the results and select which probabilities the network should display above a certain threshold, etc.

Each mathematical manipulation as such is considered a layer, [ citation needed ] and complex DNN have many layers, hence the name "deep" networks.

DNNs can model complex non-linear relationships. DNN architectures generate compositional models where the object is expressed as a layered composition of primitives.

Deep architectures include many variants of a few basic approaches. Each architecture has found success in specific domains. It is not always possible to compare the performance of multiple architectures, unless they have been evaluated on the same data sets.

DNNs are typically feedforward networks in which data flows from the input layer to the output layer without looping back. At first, the DNN creates a map of virtual neurons and assigns random numerical values, or "weights", to connections between them. The weights and inputs are multiplied and return an output between 0 and 1.