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deep learning makes it easy to increase model complexity to make a more efficient use of massive data. At the same time, studies have shown that the accuracy of deep learning models can increase with a larger size of data. As the field of MRC continues to evolve, more and more datasets ...
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 ...
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...
That region in the input image is called thelocal receptive fieldfor the hidden neuron. It's a little window on the input pixels. Each connection learns a weight. And the hidden neuron learns an overall bias as well. You can think of that particular hidden neuron as learning to analyze it...
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. Flexible...
Minerva: a fast and flexible tool for deep learning on multi-GPU. It provides ndarray programming interface, just like Numpy. Python bindings and C++ bindings are both available. The resulting code can be run on CPU or GPU. Multi-GPU support is very easy
DeepVoice is anLAM (Large Audio Model)of networks and libraries that are capable of life-like voice generation through text using AI and deep learning made for Unity. QUOTA (INCREASED) 80,000 charactersper month (up from 60k,33%+) of voice over and narration takes with DeepVoice. 80,000...
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...
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...