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TIFF/pdf Source device identification, forgery detection, Classification,.. 2020 [199] C. Ben Rabah et al. Density functional theory quantum simulations of graphene Labelled images of raw input to a simulation of graphene Raw data (in HDF5 format) and output labels from density functional theory quantum simulation
CSV and PDF Natural language processing, QnA 2021 The Atticus Project: Vietnamese Image Captioning Dataset (UIT-ViIC) Vietnamese Image Captioning Dataset 19,250 captions for 3,850 images CSV and PDF Natural language processing, Computer vision 2020 [113] Lam et al. Vietnamese Names annotated with Genders (UIT-ViNames)
The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and computer vision.
5. Pytorch tutorial Both encoder & decoder are needed to calculate attention. [42] Both encoder & decoder are needed to calculate attention. [48] Decoder is not used to calculate attention. With only 1 input into corr, W is an auto-correlation of dot products. w ij = x i x j. [49] Decoder is not used to calculate attention. [50]
Modern RNN networks are mainly based on two architectures: LSTM and BRNN. [32] At the resurgence of neural networks in the 1980s, recurrent networks were studied again. They were sometimes called "iterated nets". [33] Two early influential works were the Jordan network (1986) and the Elman network (1990), which applied RNN to study cognitive ...
Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos.From the perspective of engineering, it seeks to automate tasks that the human visual system can do.
The torch package also simplifies object-oriented programming and serialization by providing various convenience functions which are used throughout its packages. The torch.class(classname, parentclass) function can be used to create object factories ().
The library NumPy can be used for manipulating arrays, SciPy for scientific and mathematical analysis, Pandas for analyzing table data, Scikit-learn for various machine learning tasks, NLTK and spaCy for natural language processing, OpenCV for computer vision, and Matplotlib for data visualization. [3]