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Deep learning it is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations. ...
The remainder of the chapter discusses deep learning from a broader and less detailed perspective. We'llbriefly survey other models of neural networks, such as recurrent neural nets and long short-term memory units, and how such models can be applied to problems in speech recognition, natural la...
We'll train this neuron to do something ridiculously easy: take the input11to the output00. Of course, this is such a trivial task that we could easily figure out an appropriate weight and bias by hand, without using a learning algorithm. However, it turns out to be illuminating to use ...
As data becomes the driving force of the modern world, pretty much everyone has stumbled upon such terms as data science, machine learning, artificial intelligence, deep learning, and data mining at some point. But what exactly do these terms mean? What differences and relationships exist betwe...
TensorRTx aims to implement popular deep learning networks with TensorRT network definition API. Why don't we use a parser (ONNX parser, UFF parser, caffe parser, etc), but use complex APIs to build a network from scratch? I have summarized the advantages in the following aspects. ...
Deep Transfer Learning refers to transferring knowledge from a pre-trained deep neural network to a target domain, requiring less data and training time compared to traditional methods. It has been shown to outperform both traditional machine learning and deep learning in terms of overall performance...
2. Definition We define end-to-end deep learning as a machine learning technique where we train a single neural network for complex tasks using as input directly the raw input data without any manual feature extraction. Due to the creation of large-scale datasets, end-to-end deep learning ha...
Getting started with deep learning has turned out to be pretty easy! How deep are these networks, anyway? Counting the convolutional-pooling layers as single layers, our final architecture has 44 hidden layers. Does such a network really deserve to be called a deep network? Of course, 44 ...
Table 3. Loss functions of commonly used deep learning models. Empty CellNameEquationVariable definition Image classification Cross-Entropy l(y,y^)=−∑inyilogy^i • n number of classes • y is ground truth (GT) classes Binary cross-entropy(log loss) l(y,y^)=−(ylog(y^)+(1...