Deep Learning & Artificial Intelligence 

Deep learning is the fastest-growing field in machine learning. As opposed to traditional machine learning, which uses model-specific machine learning algorithms and handwritten feature extraction to analyze voices and images, deep learning neural networks use algorithms, big data, and the computational power of the GPU to accelerate this process and learn with speeds, scale, and accuracy which are able to drive AI research and computing. With these advancements, the research community can tackle important issues such as speech recognition, natural language processing, image recognition, object identification, and more.

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Deep learning differs from traditional machine learning techniques in that they can automatically learn representations from data such as images, video or text, without hand-coded rules or human domain knowledge. Their highly flexible architectures can learn directly from raw data and can increase their predictive accuracy when provided more data.

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Deep learning is commonly used across apps in computer vision, conversational AI and recommendation systems. Computer vision apps use deep learning to gain knowledge from digital images and videos. Conversational AI apps help computers understand and communicate through natural language. Recommendation systems use images, language, and a user’s interests to offer meaningful and relevant search results and services.

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Deep learning has led to many recent breakthroughs in AI such as Google DeepMind’s AlphaGo, self-driving cars, intelligent voice assistants. With GPU-accelerated deep learning frameworks, researchers and data scientists can significantly speed up deep learning training, from days and weeks to just hours and days. When models are ready for deployment, developers can rely on GPU-accelerated inference platforms for the cloud, embedded device or self-driving cars, to deliver high-performance, low-latency inference for the most computationally-intensive deep neural networks.