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4 days ago · Deep learning models have been extensively employed in developing physical activity recognition systems. To improve these models, their hyperparameters need to be initialized with optimal values. However, tuning these hyperparameters manually is time-consuming and may lead to inaccurate results.
1 day ago · The History of Deep Learning. Although Deep Learning seems modern, its roots date back to the 1940s. Inspired by the functioning of the human brain and biological neural networks, Warren McCulloch and Walter Pitts proposed the first mathematical model of a neural network in 1943. Over the next few decades, this field underwent significant ...
4 days ago · Get a Degree and Take Courses. To land a job as a deep learning specialist, you’ll almost certainly need a degree. You’ll typically first need a bachelor’s degree in computer science; after that, you’ll likely go on to get a Master’s degree in a program such as machine learning or artificial intelligence.
5 days ago · DeepSpeed v0.3 includes new support for pipeline parallelism! Pipeline parallelism improves both the memory and compute efficiency of deep learning training by partitioning the layers of a model into stages that can be processed in parallel. DeepSpeed’s training engine provides hybrid data and pipeline parallelism and can be further combined with model parallelism such as Megatron-LM. An illustration of 3D parallelism is shown below. Our latest results demonstrate that this 3D parallelism ...
6 days ago · Transformers (BERT) [5] and its variants in 2018, transfer learning has become very popular in N.L.P. P.L.M.s work collaboratively, bidirectionally from left to right, on huge machines and large textual datasets to gain a deep language understanding and learn the context of the word based on the sentence and the surrounding words [10].
5 days ago · DeepSpeed-Inference introduces several features to efficiently serve transformer-based PyTorch models. It supports model parallelism (MP) to fit large models that would otherwise not fit in GPU memory. Even for smaller models, MP can be used to reduce latency for inference. To further reduce latency and cost, we introduce inference-customized ...
11 hours ago · The Vision Transformer (VIT) is a popular deep learning model for computer vision tasks. It is based on the Transformer architecture, which was originally developed for natural language processing tasks. The VIT model has been shown to achieve state-of-the-art performance on various image classification benchmarks.
