Of late, Deep Learning has become quite popular because organisations have gained capabilities wherein they can capture and mine even unstructured data. We are not just talking about big industry giants such as Amazon, Google, and SAS here, but rather, about everybody who has access to huge amounts of data, which paves the way for deep learning simulations.<\/span><\/p>\n Deep Learning can also offer an array of algorithms which helps in several artificial intelligence executions.<\/span><\/p>\n In reality, deep learning models are large-sized models containing several neural network layers and 50 million or more parameters. These models need big data to find answers and hidden patterns. The more data you can include, the better your deep learning simulations will be.\u00a0<\/span><\/p>\n Deep learning can be compiled in two operations: backward propagation and forward propagation. Both these propagations are nothing but matrix multiplications, which are processed using graphical processing units or GPUs.\u00a0<\/span><\/p>\n Earlier, CPU’s or central processing units were used to train deep learning algorithms. However, CPUs were less efficient due to fewer complex cores, single-thread performance, and transistor space, which was based on ILP.<\/p>\n GPUs, on the other hand, remove these drawbacks with several simple cores, multiple hardware threads running concurrently, and maximised throughput of floating-point. Further, GPUs are cost-effective when compared to CPUs, in terms of building them.<\/span><\/p>\n Using GPUs, scientists can reduce the time needed for running an experiment with accuracy.\u00a0<\/span><\/p>\n Even after using thousands of cores, GPUs have the ability to run faster than CPUs. These units have better capabilities of processing huge amounts of data and parallelly executing several tasks. This makes GPUs a perfect fit for the requirements of deep learning models containing thousands of datasets.<\/p>\n References Used:<\/b><\/p>\n https:\/\/blogs.sas.com\/content\/subconsciousmusings\/2018\/10\/16\/why-you-need-gpus-for-your-deep-learning-platform\/<\/span><\/p>\n https:\/\/www.analyticsvidhya.com\/blog\/2017\/05\/gpus-necessary-for-deep-learning\/<\/span><\/p>\n\n
Deep Learning: How it Works?<\/b><\/h4>\n
The Right GPU for your Deep Learning Platform<\/b><\/h4>\n
Based on Complexity<\/b><\/h5>\n
\n
Based on Hardware<\/b><\/h5>\n
\n
Why GPU for Deep Learning?<\/b><\/h4>\n
\n
Conclusion\u00a0<\/b><\/h3>\n