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Some useful resources for learning about e-agriculture in practice are the World Bank's e-sourcebook ICT in agriculture – connecting smallholder farmers to knowledge, networks and institutions (2011), [2] ICT uses for inclusive value chains (2013), [3] ICT uses for inclusive value chains (2013) [4] and Success stories on information and ...
Caffe is being used in academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Yahoo! has also integrated Caffe with Apache Spark to create CaffeOnSpark, a distributed deep learning framework.
[4] [5] The goal of precision agriculture research is to define a decision support system for whole farm management with the goal of optimizing returns on inputs while preserving resources. [6] [7] Among these many approaches is a phytogeomorphological approach which ties multi-year crop growth stability/characteristics to topological terrain ...
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.
Many AI platforms use Wikipedia data, [272] mainly for training machine learning applications. There is research and development of various artificial intelligence applications for Wikipedia such as for identifying outdated sentences, [273] detecting covert vandalism [274] or recommending articles and tasks to new editors.
U-Net is a convolutional neural network that was developed for image segmentation. [1] The network is based on a fully convolutional neural network [2] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation.
The 2010s marked a significant shift in the development of AI, driven by the advent of deep learning and neural networks. [31] Open-source deep learning frameworks such as TensorFlow (developed by Google Brain) and PyTorch (developed by Facebook's AI Research Lab) revolutionized the AI landscape by making complex deep learning models more ...
In machine learning, knowledge distillation or model distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have more knowledge capacity than small models, this capacity might not be fully utilized. It can be just as ...