network and deep learning
Neural networks and deep learning with Microsoft Azure GPU
The rise of neural networks and deep learning is correlated with increased computational power introduced by general purpose GPUs. The reason is that the optimisation problems being solved to train a complex statistical model, are demanding and the computational resources available are crucial to the final solution. Using a conventional CPU, one could spend weeks of waiting for a simple neural network to be trained. This problem is amplified when one is trying to spawn multiple experiments to select optimal parameters of a model. Having computational resources such as a high-end GPU is an important aspect when one begins to experiment with deep learning models as this allows a rapid gain in practical experience.
Delving into neural networks and deep learning
Machine learning is coming to the data center both to improve internal IT management and embed intelligence into key business processes. You have probably heard of a mystical deep learning, threatening to infuse everything from systems management to self-driving cars. Is this deep learning some really smart artificial intelligence that was just created and about to be unleashed on the world, or simply marketing hype aiming to re-launch complex machine learning algorithms in a better light? As DevOps is slowly taking over the IT landscape, its vital that IT pros understand it before jumping right into the movement. In this complimentary guide, discover an expert breakdown of how DevOps impacts day-to-day operations management in modern IT environments.
Delving into neural networks and deep learning
Machine learning is coming to the data center both to improve internal IT management and embed intelligence into key business processes. You have probably heard of a mystical deep learning, threatening to infuse everything from systems management to self-driving cars. Is this deep learning some really smart artificial intelligence that was just created and about to be unleashed on the world, or simply marketing hype aiming to re-launch complex machine learning algorithms in a better light? It definitely fires the imagination, but it's actually not that complicated. At a technical level, deep learning mostly refers to large compute-intensive neural networks running at scale.