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Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
Pixel-wise regression using U-Net and its application on pansharpening; [15] 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation; [16] TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation. [17] Image-to-image translation to estimate fluorescent stains [18] In binding site prediction of protein ...
In theory, classic RNNs can keep track of arbitrary long-term dependencies in the input sequences. The problem with classic RNNs is computational (or practical) in nature: when training a classic RNN using back-propagation, the long-term gradients which are back-propagated can "vanish", meaning they can tend to zero due to very small numbers creeping into the computations, causing the model to ...
Unfortunately, these early efforts did not lead to a working learning algorithm for hidden units, i.e., deep learning. In 1965, Alexey Grigorevich Ivakhnenko and Valentin Lapa published Group Method of Data Handling, the first working deep learning algorithm, a method to train arbitrarily deep neural networks.
A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]
The goal of any supervised learning algorithm is to find a function that best maps a set of inputs to their correct output. The motivation for backpropagation is to train a multi-layered neural network such that it can learn the appropriate internal representations to allow it to learn any arbitrary mapping of input to output.
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The observation [2] that DBNs can be trained greedily, one layer at a time, led to one of the first effective deep learning algorithms. [4]: 6 Overall, there are many attractive implementations and uses of DBNs in real-life applications and scenarios (e.g., electroencephalography, [5] drug discovery [6] [7] [8]).