HMS Warwick (D2. 5) - Wikipedia. HMS Warwick (D- 2. Admiralty 'W' classdestroyer built in 1. She saw service in both the First and Second World Wars, before being torpedoed and sunk in February 1. Construction. She was part of the Admiralty W Class of 2. Warwick was built by Hawthorn Leslie & Company, Hebburn, being laid down 1. March 1. 91. 7. She launched on 2. December 1. 91. 7, and completed 1. March 1. 91. 8. Service history.
She took part in the raid on Zeebrugge in April, the attempt by the RN to blockade Germany's U- boat force stationed in Flanders. She also participated in the second raid on Ostend in May and was heavily damaged by a mine. Warwick was present at Scapa Flow in November 1. Grand Fleet received the surrender of the German High Seas Fleet at the end of the war. Trustworthy anonymity Usenet.nl provides complete protection of your privacy and does not log IPs or any other data. Enjoy the diversity of Usenet in an anonymous setting. Nombre de usuario o correo electr Entertainment news, celebrity gossip and photos on MSN Entertainment msn back to msn home entertainment. Gears of War 4 Soundtrack to the game Top Albums 1 Views Drake EXPLICIT 2 Suicide Squad: The Album Various Artists EXPLICIT 3 Birds In 4. Mobile Uploading Get your files whenever you need right from your cellphone. Uploading Desktop Up to 10x better download speed. You should install it for sure. Save Your Money More users, lower price. Choose best plan for you. Inter- war years. With the onset of war in 1. Warwick was re- activated, re- commissioning in August, and joining the fleet at Plymouth in September. Second World War. In February 1. 94. Western Approaches Escort Force for Atlantic convoy defence. In this role she was engaged in all the duties performed by escort ships; protecting convoys, searching for and attacking U- boats which attacked ships in convoy, and rescuing survivors. In November 1. 94. EG. In December she was mined and spent the next four months in dock for repairs. In March 1. 94. 1 she rejoined Western Approaches Command and saw out the year in the Atlantic. In January 1. 94. US entry into the war and the opening of the U- boat offensive off the US east coast (Operation Drumbeat), Warwick she transferred there, on loan to the USN. In June 1. 94. 2 she was transferred to the West Indies serving with USN and RCN ships of the Caribbean Escort Force on anti- submarine patrol and convoy escort duty. In December Warwick returned to Britain for conversion as a long range escort. This entailed, among other modifications, removal of one of her boilers to allow extra fuel capacity, thus sacrificing speed for endurance and range. She remained in dock at Dundee for the first half of 1. In July she was on anti- submarine duties in the Bay of Biscay, supporting Operation Musketry, the RAF Coastal Command's Bay offensive In November she took part in Operation Alacrity, the establishment and supply of Allied air bases in the Azores which served to close the Mid- Atlantic gap. In January 1. 94. Britain, Warwick was assigned to lead an escort group operating in the South. West Approaches, guarding against attacks by German E- boats. It was while engaged in this she was torpedoed and sunk on 2. February 1. 94. 4. Denys Rayner, Warwick was torpedoed by U- 4. She sank in minutes, with the loss of over half her crew. References. Naval Weapons of World War II. Annapolis, Maryland: Naval Institute Press. Conway's All the World's Fighting Ships 1. Greenwich, UK: Conway Maritime Press. J.; Warlow, Ben (2. Ships of the Royal Navy: The Complete Record of all Fighting Ships of the Royal Navy (Rev. London: Chatham Publishing. ISBN 9. 78- 1- 8. Cocker, Maurice; Allan, Ian. Destroyers of the Royal Navy, 1. Friedman, Norman (2. British Destroyers From Earliest Days to the Second World War. Annapolis, Maryland: Naval Institute Press. ISBN 9. 78- 1- 5. Gardiner, Robert & Gray, Randal, eds. Conway's All the World's Fighting Ships: 1. Annapolis, Maryland: Naval Institute Press. British & Empire Warships of the Second World War. Annapolis, Maryland: Naval Institute Press. British Destroyers: A History of Development, 1. Drawn by Admiralty Permission From Official Records & Returns, Ships' Covers & Building Plans. London: Seeley Service. Preston, Antony (1. Raven, Alan & Roberts, John (1. London: Arms & Armour. Chronology of the War at Sea 1. The Naval History of World War Two (Third Revised ed.). Annapolis, Maryland: Naval Institute Press. The U- boat Peril: A Fight for Survival. Destroyers of World War 2. Annapolis, Maryland: Naval Institute Press. Ships Before, At and After Dunkirk. Gravesend, Kent: World Ship Society. Rayner, Denys : Escort: The Battle of the Atlantic 1. ISBN 1- 5. 57. 50- 6. Allen, David (2. 00.
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One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi- supervisedfeature learning and hierarchical feature extraction. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). Higher level features are derived from lower level features to form a hierarchical representation. 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. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of complicated propositional formulas. At each layer, the signal is transformed by a processing unit, like an artificial neuron, whose parameters are 'learned' through training. CAPs describe potentially causal connections between input and output and may vary in length. For a feedforward neural network, the depth of the CAPs, and thus the depth of the network, is the number of hidden layers plus one (the output layer is also parameterized). 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 > 1. 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. Diabetes Ketoacidosis Treatment Diabetes & Alternative Diabetes Treatment This is the preprint of an invited Deep Learning (DL) overview. One of its goals is to assign credit to those who contributed to the present state of the art. I acknowledge the limitations of attempting to achieve. We've finally made it. We've made it to what we've all been waiting for, Q-learning with neural networks. Since I'm sure a lot of people. LETTER doi:10.1038/nature14236 Human-level control through deep reinforcement learning Volodymyr Mnih1*, Koray Kavukcuoglu1*, David Silver1*, Andrei A. Rusu1, Joel Veness1, Marc G. Bellemare1, Alex Graves1, Martin Riedmiller 1. The Machine Learning Salon provides free information about Machine Learning and Artificial Intelligence. Its aim is to develop the understanding of Machine Learning Theory and its applications by providing a first. Varying numbers of layers and layer sizes can be used to provide different amounts of abstraction. 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. 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. It features inference. More specifically, the probabilistic interpretation considers the activation nonlinearity as a cumulative distribution function. The probabilistic interpretation led to the introduction of dropout as regularizer in neural networks. Other Deep Learning working architectures, specifically those built from artificial neural networks (ANN), date back to the Neocognitron introduced by Kunihiko Fukushima in 1. The challenge was how to train networks with multiple layers. In 1. 98. 9, Yann Le. Cun et al. Despite the success of applying the algorithm, the time to train the network on this dataset was approximately 3 days, making it impractical for general use. Cresceptron is a cascade of layers similar to Neocognitron. But while Neocognitron required a human programmer to hand- merge features, Cresceptron automatically learned an open number of unsupervised features in each layer, where each feature is represented by a convolution kernel. Cresceptron also segmented each learned object from a cluttered scene through back- analysis through the network. Max pooling, now often adopted by deep neural networks (e. Image. Net tests), was first used in Cresceptron to reduce the position resolution by a factor of (2x. Despite these advantages, simpler models that use task- specific handcrafted features such as Gabor filters and support vector machines (SVMs) were a popular choice in the 1. ANNs at the time, and a great lack of understanding of how the brain autonomously wires its biological networks. In the long history of speech recognition, both shallow and deep learning (e. Thus, most speech recognition researchers who understood such barriers moved away from neural nets to pursue generative modeling. An exception was at SRI International in the late 1. Funded by the US government's NSA and DARPA, SRI conducted research on deep neural networks in speech and speaker recognition. The speaker recognition team, led by Larry Heck, achieved the first significant success with deep neural networks in speech processing as demonstrated in the 1. NIST (National Institute of Standards and Technology) Speaker Recognition evaluation and later published in the journal of Speech Communication. In 2. 00. 3, LSTM started to become competitive with traditional speech recognizers on certain tasks. Results on commonly used evaluation sets such as TIMIT (ASR) and MNIST (image classification), as well as a range of large- vocabulary speech recognition tasks are constantly being improved with new applications of deep learning. In late 2. 00. 9, Li Deng invited Geoffrey Hinton to work with him and colleagues at Microsoft Research in Redmond, Washington to apply deep learning to speech recognition. They co- organized the 2. NIPS Workshop on Deep Learning for Speech Recognition. The workshop was motivated by the limitations of deep generative models of speech, and the possibility that the big- compute, big- data era warranted a serious try of deep neural nets (DNN). It was believed that pre- training DNNs using generative models of deep belief nets (DBN) would overcome the main difficulties of neural nets encountered in the 1. This finding was verified by several other major speech recognition research groups. The history of this significant development in deep learning has been described and analyzed in recent books and articles. In particular, powerful graphics processing units (GPUs) are well- suited for the kind of number crunching, matrix/vector math involved in machine learning. Artificial neural networks are inspired by the 1. Nobel laureates. David H. Hubel & Torsten Wiesel, who found two types of cells in the primary visual cortex: simple cells and complex cells. Many artificial neural networks can be viewed as cascading models. Max- pooling appeared to be first proposed by Cresceptron. Max- pooling helps, but does not guarantee, shift- invariance at the pixel level. Sepp Hochreiter's diploma thesis of 1. Recurrent networks are trained by unfolding them into very deep feedforward networks, where a new layer is created for each time step of an input sequence processed by the network. As errors propagate from layer to layer, they shrink exponentially with the number of layers, impeding the tuning of neuron weights which is based on those errors. To overcome this problem, several methods were proposed. Then the network is trained further by supervised back- propagation to classify labeled data. The deep model of Hinton et al. It uses a restricted Boltzmann machine (Smolensky, 1. Each new layer guarantees an increase on the lower- bound of the log likelihood of the data, thus improving the model, if trained properly. Once sufficiently many layers have been learned, the deep architecture may be used as a generative model by reproducing the data when sampling down the model (an . In 2. 01. 0, Dan Ciresan and colleagues. The method outperformed all other machine learning techniques on the old, famous MNIST handwritten digits problem of Yann Le. Cun and colleagues at NYU. At about the same time, in late 2. NIPS Workshop on Deep Learning for Speech Recognition. Intensive collaborative work between Microsoft Research and University of Toronto researchers demonstrated by mid- 2. Redmond that deep neural networks interfaced with a hidden Markov model with context- dependent states that define the neural network output layer can drastically reduce errors in large- vocabulary speech recognition tasks such as voice search. The same deep neural net model was shown to scale up to Switchboard tasks about one year later at Microsoft Research Asia. Even earlier, in 2. LSTM. Training is usually done without any unsupervised pre- training. Since 2. 01. 1, GPU- based implementations. ANNs were able to guarantee shift invariance to deal with small and large natural objects in large cluttered scenes, only when invariance extended beyond shift, to all ANN- learned concepts, such as location, type (object class label), scale, lighting. This was realized in Developmental Networks (DNs). Most of them are branched from some original parent architectures. It is not always possible to compare the performance of multiple architectures all together, because they are not all evaluated on the same data sets. Deep learning is a fast- growing field, and new architectures, variants, or algorithms appear every few weeks. Brief discussion of deep neural networks. DNN architectures, e. According to various sources. In 1. 96. 2, Stuart Dreyfus published a simpler derivation based only on the chain rule. 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Discography, line-up, biography. Fit For A King - Descendants; Fit For A King - Ancient Waters; All videos. BB King - Discography 1. King (born September 1. B. B. King, is an American blues guitarist and singer- songwriter. According to Edward M. King - Together again. Live @3. 20. 1. 98. Now Appering At Ole Miss (2 CD) @3. Great Moments With BB King @3. Royal Jam (Live BB King & The Crusaders) @3. John Lee Hooker Muddy Waters B.B. King Van Morrison Santana Miles Davis Blues Jazz Ry Cooder Keith Richards Johnny Winter Taj Mahal Rolling Stones Albert Collins Bonnie Raitt Carlos. John Lee Hooker Discography 1986.A Night in Cannes (BB King Pat Metheny Dave Brubeck) @3. A Night Of Red Hot Blues ( Ebony Theater LA) @3. Live At San Quentin (2. Live At The Apollo @3. When Love Comes To Town Live @3. Live at Newport (BB King Big Mama Thornton Muddy Waters) @3. How Blue Can You Get (1. CD) @3. 20. 1. 99. The King Biscuit Flower Hour Presents B B King @3. Live at Rumble in the Jungle @3. The Thrill Is Gone @3. Live At The BBC @3. His Best - The Electric B B King (1. Lucille Talks Back @3. Why I Sing The Blues (1. King Of The Blues (1. King Of The Blues (4 CD) @3. My Sweet Little Angel @3. Greatest Hits @3. B B King & Friends (2 CD) @3. The Best Of The Early Years @3. King Curtis Discography at Discogs. Saxophonist, songwriter and producer. Born 7 February 1. Fort Worth, Texas, died murdered by junkies on 1. August 1. 97. 1 in New York, New York. Curtis mainly played and composed rhythm and blues or soul but also some Rock and roll and great bop or soul jazz. He was inducted into the Rock & Roll Hall of Fame in 2. Franklin, Stevie Wonder, Brook Benton, The Isley Brothers and Rev. Jessie Jackson, who delivered the eulogy. He turned down a scholarship to pursue an opportunity as a member of Lionel Hampton. The Pirate Bay is the galaxy's most resilient BitTorrent site. TPB.TO Search Torrents . Torrent Download B B King - All -STUDIO- Discography - Discover King Curtis's full discography. King Curtis played on thousands of. Cutis, King, King Curtis And His Tenor Sax, M. Download John Mayer Discography - FLAC torrent or any other torrent from the. BB King - Discography 1957-2008 . King (born September 16, 1925), known by the stage name B.B. King, is an American blues guitarist and singer-songwriter. King Crimson - Discography (1969-2005. England Formed in 1969 Albums in this torrent: Title:In The Court Og King Crimson. Ben E King Discography Torrent Tpb rapidshare mediafire megaupload hotfile, Ben E King Discography Torrent Tpb torrent download, Ben E King Discography Torrent Tpb emule download, Ben E King Discography Torrent Tpb full free. King song information on AllMusic. King song information on AllMusic AllMusic. Featured New Releases; Editors' Choice; All New. Live - 192 1992 - The Great Deceiver 1994 - Vrooom 1995 - B'BOOM. |
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