Loading data onto GPUs for training has historically been a minor issue for most deep learning practitioners. Data read from a local spinning hard drive or NAS device would be preprocessed on the CPU, then shipped to the GPU for training. The data input pipeline rarely proved to be the bottleneck given the long number-crunching times involved. As GPUs improve and DL frameworks use them more…
]]>The detection of malicious software (malware) is an increasingly important cyber security problem for all of society. Single incidences of malware can cause millions of dollars in damage. The current generation of anti-virus and malware detection products typically use a signature-based approach, where a set of manually crafted rules attempt to identify different groups of known malware types.
]]>DigitalGlobe, CosmiQ Works and NVIDIA recently announced the launch of the SpaceNet online satellite imagery repository. This public dataset of high-resolution satellite imagery contains a wealth of geospatial information relevant to many downstream use cases such as infrastructure mapping, land usage classification and human geography estimation. The SpaceNet release is unprecedented: it’s the…
]]>The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning in the hands of data scientists and researchers. Using DIGITS you can perform common deep learning tasks such as managing data, defining networks, training several models in parallel, monitoring training performance in real time, and choosing the best model from the results browser.
]]>Today we’re excited to announce the availability of NVIDIA DIGITS 4. DIGITS 4 introduces a new object detection workflow and DetectNet, a new deep neural network for object detection that enables data scientists and researchers to train models that can detect instances of faces, pedestrians, traffic signs, vehicles and other objects in images. Object detection is one of the most challenging…
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