WebMD explains the different types of dementia, a syndrome that affects a person's thinking, behavior, and memory. 23andMe is the first and only genetic service available directly to you that includes reports that meet FDA standards. Request an Appointment. Please fill out the form below and click on the submit button at the bottom of the page to send. A representative will then contact you with. Explore Gru's 23andMe genetic reports. See Despicable Me 3, in theaters June 30. The General Dynamics–Grumman EF-111A Raven was an electronic warfare aircraft designed to replace the B-66 Destroyer in the United States Air Force. Steven Universe has kept much of the Gem Homeworld shrouded in mystery, save for bits and pieces of information. In last night’s hour-long Wanted special, though. The Grumman C-2 Greyhound is a twin-engine, high-wing cargo aircraft, designed to carry supplies, mail, and passengers to and from aircraft carriers of the United. The first trailer for Star: Trek Discovery made a big deal of lead character Michael Burnham’s enigmatic connection to the Vulcans—and specifically Spock’s. Grumman C- 2 Greyhound - Wikipedia. The Grumman C- 2 Greyhound is a twin- engine, high- wing cargo aircraft, designed to carry supplies, mail, and passengers to and from aircraft carriers of the United States Navy. Its primary mission is carrier onboard delivery (COD). The aircraft provides critical logistics support to carrier strike groups. The aircraft is mainly used to transport high- priority cargo, mail and passengers between carriers and shore bases, and can also deliver cargo like jet engines and special stores. Prototype C- 2s first flew in 1. The initial Greyhound aircraft were overhauled in 1. In 1. 98. 4, more C- 2. As were ordered under the name Reprocured C- 2. A or C- 2. A(R). The C- 2. As received updated propellers (from four to eight blades) and navigation. Design and development. The first of two prototypes flew in 1. The above policy is based on the following references: World Health Organization (WHO). Assessment of fracture risk and its application to screening for. After successful testing, Grumman began production of the aircraft in 1. The C- 2 replaced the piston- engined Grumman C- 1 Trader in the COD role. The original C- 2. WebMD explains the causes, symptoms, and treatment for Lewy Body Dementia, one of the most common types of dementia.A aircraft were overhauled to extend their operational life in 1. It can also carry litter patients in medical evacuation missions. A cage system or transport stand restrains cargo during carrier launch and landing. The large aft cargo ramp and door and a powered winch allow straight- in rear cargo loading and unloading for fast turnaround. The Greyhound's ability to airdrop supplies and personnel, fold its wings, and generate power for engine starting and other uses provide an operational versatility found in no other cargo aircraft. The C- 2 has four vertical stabilizers, of which three are fitted with rudders. A single vertical stabilizer large enough for adequate directional control would have made the aircraft too tall to fit on an aircraft carrier hangar deck. The four- stabilizer configuration has the advantage of placing the outboard rudder surfaces directly in line with the propeller wash, providing effective yaw control down to low airspeeds, such as during takeoff and landing. The inner- left stabilizer lacks a rudder, and has been called the . Dubbed the Reprocured C- 2. A or C- 2. A(R) due to the similarity to the original, the new aircraft has airframe improvements and better avionics. The older C- 2. As were phased out in 1. Upgrades. The C- 2. A(R)'s lifespan was 1. C- 2. A to perform its mission supporting battle group operational readiness through 2. The lower landing limit was approaching for most airframes, and the SLEP will increase their projected life to 1. Once complete, the SLEP will allow the 3. The SLEP includes structural improvements to the center wing, an eight- bladed NP2. GPS and the dual CAINS II Navigation System, the addition of crash- survivable flight incident recorders, and a Ground Proximity Warning System. The first upgraded C- 2. A(R) left NAVAIR Depot North Island on 1. September 2. 00. 5, after sitting on the ground for three and a half years while the SLEP was developed and installed. All aircraft will receive SLEP by 2. Northrop Grumman worked on an upgraded C- 2 version, and offered to modernize the fleet with components common to the E- 2. D Hawkeye. The squadron delivered 2,0. European and Mediterranean theaters. The C- 2. A(R) also served the carrier battle groups during Operations Desert Shield and Desert Storm during the Gulf War, as well as Operation Enduring Freedom during the War in Afghanistan. The two aircraft were stationed at Toulon- Hy. After 1. 6 days, both aircraft returned to the US via Shannon Airport, Ireland on 1. June 2. 01. 1. The USN was exploring a replacement for the C- 2 in September 2. Northrop Grumman proposed modernizing the C- 2 by installing the same wings, glass cockpit, and engines as the E- 2. D Advanced Hawkeye in two phases. The first would replace the engines and avionics, and the second would replace the wing structures. Installing the Rolls Royce T5. A engines would cut fuel consumption by 1. Adopting the E- 2. D's cockpit would deliver a 1. One of the Greyhound's most important features is its internal volume of 8. While returning to Naval Air Station Cubi Point from USS Kitty Hawk operating in Southeast Asia, a failure in the engine gearbox and propeller assembly resulted in the loss of the entire port propeller assembly and substantial portions of the gearbox and nacelle. The separated propeller penetrated the fuselage, causing decompression at over 2. Kennedy and his copilot secured the engine, descended to a lower altitude, and returned to Cubi Point. All aboard were killed and are officially listed as missing in action as their bodies were never recovered. February 2. 00. 9. Retrieved 1. 1 June 2. Navy’s COD. Air. Forces Monthly (Key Publishing), Issue 2. August 2. 01. 1, p. ISSN 0. 95. 5- 7. Retrieved: 4 October 2. Tilghman, Andrew (2. September 2. 00. 9). Retrieved 2. 1 April 2. Defensemedianetwork. August 2. 01. 3.^. Inside. Defense. com, 2. April 2. 01. 4.^Navy 2. Budget Funds V- 2. COD Buy, Carrier Refuel - Breakingdefense. February 2. 01. 5^Mass Communication Specialist 3rd Class Renee Candelario, USN (8 October 2. NNS1. 21. 00. 8- 1. USS Nimitz Public Affairs. Retrieved 2. 2 January 2. Retrieved 2. 1 April 2. Retrieved 2. 9 April 2. Retrieved 2. 9 April 2. Retrieved 2. 9 April 2. Retrieved 2. 9 April 2. Retrieved 2. 9 April 2. Retrieved 2. 9 April 2. Standard Aircraft Characteristics, Reprocured C- 2. A. Retrieved 2. 1 April 2. Deep Learning Glossary – Wild. MLThis glossary is work in progress and I am planning to continuously update it. If you find a mistake or think an important term is missing, please let me know in the comments or via email. Deep Learning terminology can be quite overwhelming to newcomers. This glossary tries to define commonly used terms and link to original references and additional resources to help readers dive deeper into a specific topic. The boundary between what is Deep Learning vs. I am trying to keep the glossary specific to Deep Learning, but these decisions are somewhat arbitrary. For example, I am not including “cross- validation” here because it’s a generic technique uses all across Machine Learning. However, I’ve decided to include terms such as softmax or word. Deep Learning even though they are not Deep Learning techniques. Activation Function. To allow Neural Networks to learn complex decision boundaries, we apply a nonlinear activation function to some of its layers. Commonly used functions include sigmoid, tanh, Re. LU (Rectified Linear Unit) and variants of these. Adadelta. Adadelta is a gradient descent based learning algorithm that adapts the learning rate per parameter over time. It was proposed as an improvement over Adagrad, which is more sensitive to hyperparameters and may decrease the learning rate too aggressively. Adadelta It is similar to rmsprop and can be used instead of vanilla SGD. Adagrad. Adagrad is an adaptive learning rate algorithms that keeps track of the squared gradients over time and automatically adapts the learning rate per- parameter. It can be used instead of vanilla SGD and is particularly helpful for sparse data, where it assigns a higher learning rate to infrequently updated parameters. Adam. Adam is an adaptive learning rate algorithm similar to rmsprop, but updates aredirectly estimated using a running average of the first and second moment of the gradient and also include a bias correction term. Affine Layer. A fully- connected layer in a Neural Network. Affine means that each neuron in the previous layer is connected to each neuron in the current layer. In many ways, this is the “standard” layer of a Neural Network. Affine layers are often added on top of the outputs of Convolutional Neural Networks or Recurrent Neural Networks before making a final prediction. An affine layer is typically of the form y = f(Wx + b) where x are the layer inputs, W the parameters, b a bias vector, and f a nonlinear activation function. Attention Mechanism. Attention Mechanisms are inspired by human visual attention, the ability to focus on specific parts of an image. Attention mechanisms can be incorporated in both Language Processing and Image Recognition architectures to help the network learn what to “focus” on when making predictions. Alexnet. Alexnet is the name of the Convolutional Neural Network architecture that won the ILSVRC 2. CNNs for Image Recognition. It consists of five convolutional layers, some of which are followed by max- pooling layers, and three fully- connected layers with a final 1. Alexnet was introduced in Image. Net Classification with Deep Convolutional Neural Networks. Autoencoder. An Autoencoder is a Neural Network model whose goal is to predict the input itself, typically through a “bottleneck” somewhere in the network. By introducing a bottleneck, we force the network to learn a lower- dimensional representation of the input, effectively compressing the input into a good representation. Autoencoders are related to PCA and other dimensionality reduction techniques, but can learn more complex mappings due to their nonlinear nature. A wide range of autoencoder architectures exist, including Denoising Autoencoders, Variational Autoencoders, or Sequence Autoencoders. Average- Pooling. Average- Pooling is a pooling technique used in Convolutional Neural Networks for Image Recognition. It works by sliding a window over patches of features, such as pixels, and taking the average of all values within the window. It compresses the input representation into a lower- dimensional representation. Backpropagation. Backpropagation is an algorithm to efficiently calculate the gradients in a Neural Network, or more generally, a feedforward computational graph. It boils down to applying the chain rule of differentiation starting from the network output and propagating the gradients backward. The first uses of backpropagation go back to Vapnik in the 1. Learning representations by back- propagating errors is often cited as the source. Backpropagation Through Time (BPTT)Backpropagation Through Time (paper) is the Backpropagation algorithm applied to Recurrent Neural Networks (RNNs). BPTT can be seen as the standard backpropagation algorithm applied to an RNN, where each time step represents a layer and the parameters are shared across layers. Because an RNN shares the same parameters across all time steps, the errors at one time step must be backpropagated “through time” to all previous time steps, hence the name. When dealing with long sequences (hundreds of inputs), a truncated version of BPTT is often used to reduce the computational cost. Truncated BPTT stops backpropagating the errors after a fixed number of steps. Batch Normalization. Batch Normalization is a technique that normalizes layer inputs per mini- batch. It speed up training, allows for the usage of higher learner rates, and can act as a regularizer. Batch Normalization has been found to be very effective for Convolutional and Feedforward Neural Networks but hasn’t been successfully applied to Recurrent Neural Networks. Bidirectional RNNA Bidirectional Recurrent Neural Network is a type of Neural Network that contains two RNNs going into different directions. The forward RNN reads the input sequence from start to end, while the backward RNN reads it from end to start. The two RNNs are stacked on top of each others and their states are typically combined by appending the two vectors. Bidirectional RNNs are often used in Natural Language problems, where we want to take the context from both before and after a word into account before making a prediction. Caffe. Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center. Caffe is particularly popular and performant for vision tasks and CNN models. Categorical Cross- Entropy Loss. The categorical cross- entropy loss is also known as the negative log likelihood. It is a popular loss function for categorization problems and measures the similarity between two probability distributions, typically the true labels and the predicted labels. It is given by L = - sum(y * log(y. The canonical examples are images, which have red, green and blue color channels. A image can be represented as a 3- dimensional Tensor with the dimensions corresponding to channel, height, and width. Natural Language data can also have multiple channels, in the form of different types of embeddings for example. Convolutional Neural Network (CNN, Conv. Net)A CNN uses convolutions to connected extract features from local regions of an input. Most CNNs contain a combination of convolutional, pooling and affine layers. CNNs have gained popularity particularly through their excellent performance on visual recognition tasks, where they have been setting the state of the art for several years. Deep Belief Network (DBN)DBNs are a type of probabilistic graphical model that learn a hierarchical representation of the data in an unsupervised manner. DBNs consist of multiple hidden layers with connections between neurons in each successive pair of layers. DBNs are built by stacking multiple RBNs on top of each other and training them one by one. Deep Dream. A technique invented by Google that tries to distill the knowledge captured by a deep Convolutional Neural Network. The technique can generate new images, or transform existing images and give them a dreamlike flavor, especially when applied recursively. Dropout. Dropout is a regularization technique for Neural Networks that prevents overfitting. It prevents neurons from co- adapting by randomly setting a fraction of them to 0 at each training iteration. Dropout can be interpreted in various ways, such as randomly sampling from an exponential number of different networks. Dropout layers first gained popularity through their use in CNNs, but have since been applied to other layers, including input embeddings or recurrent networks. Embedding. An embedding maps an input representation, such as a word or sentence, into a vector. A popular type of embedding are word embeddings such as word. Glo. Ve. We can also embed sentences, paragraphs or images. For example, by mapping images and their textual descriptions into a common embedding space and minimizing the distance between them, we can match labels with images. Embeddings can be learned explicitly, such as in word. Sentiment Analysis. Often, the input layer of a network is initialized with pre- trained embeddings, which are then fine- tuned to the task at hand. Exploding Gradient Problem. The Exploding Gradient Problem is the opposite of the Vanishing Gradient Problem. In Deep Neural Networks gradients may explode during backpropagation, resulting number overflows. A common technique to deal with exploding gradients is to perform Gradient Clipping. Fine- Tuning. Fine- Tuning refers to the technique of initializing a network with parameters from another task (such as an unsupervised training task), and then updating these parameters based on the task at hand. For example, NLP architecture often use pre- trained word embeddings like word. Sentiment Analysis. Gradient Clipping. Gradient Clipping is a technique to prevent exploding gradients in very deep networks, typically Recurrent Neural Networks. There exist various ways to perform gradient clipping, but the a common one is to normalize the gradients of a parameter vector when its L2 norm exceeds a certain threshold according to new. Glo. Ve vectors serve the same purpose as word.
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