Businesses worldwide are using artificial intelligence (AI) to solve their greatest challenges. Healthcare professionals use AI to enable more accurate, faster diagnoses in patients. Retail businesses use it to offer personalised customer shopping experiences. The automotive industry is using AI to make develop self driving cars and deliver services safer and more efficiently. Deep learning is a powerful approach to implementing AI that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Using deep learning, computers are now able to learn and recognise patterns from data that are considered too complex or subtle for expert-written software. Understanding how deep learning works and how it can be implemented is gaining in importance every day - the NVIDIA Deep Learning Institute exists to promote this knowledge.

AI Ideation Workshops with Scan & NVIDIA

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NVIDIA Deep Learning Institute

The NVIDIA Deep Learning Institute (DLI) offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning and accelerated computing. Through self-paced labs and instructor-led workshops, the DLI teaches the latest techniques for designing, training, and deploying neural networks across a variety of application domains. The DLI also teaches you how to optimise your code for performance using NVIDIA, CUDA and OpenACC.

DLI courses provide unique hands-on training using the latest AI frameworks, software development kits (SDKs), and GPU-accelerated technologies. Students get exposure to industry experts, along with the training and insights to help solve some of the world’s most challenging problems with deep learning and accelerated computing.

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Delivered by Scan Experts

As an NVIDIA Elite Solution provider, the Scan AI team has gained expertise throughout the industry in both hardware and software deep learning and AI solutions. Our DLI courses are delivered by fully qualified instructors certified by NVIDIA so we’re in the perfect position to pass on our knowledge and educate developers on how to apply deep learning and artificial intelligence models. Click the video to the left for an in-depth look at how we’re delivering our DLI courses.

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Online Instructor-Led Workshops and & Training

DLI Instructor-Led Workshop

Fundamentals of Deep Learning for Multi-GPUs
03/12/2020 Online With Scan

Modern deep learning challenges leverage increasingly larger datasets and more complex models. As a result, significant computational power is required to train models effectively and efficiently. In this course, you will learn how to scale deep learning training to multiple GPUs. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible. This course will teach you how to use multiple GPUs to train neural networks.

By participating in this is workshop you will learn
• Approaches to multi-GPU training
• Algorithmic and engineering challenges to large-scale training
• Key techniques used to overcome the challenges mentioned above
• Upon completion, you'll be able to effectively parallelise training of deep neural networks using Horovod.

Prerequisites
Competency in the Python programming language and experience training deep learning models in Python.

Tools, libraries, and frameworks
Python, Tensorflow

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DLI Instructor-Led Workshop

Fundamentals of Deep Learning
17/09/2020 Online With Scan

In this workshop, you’ll learn how deep learning works through hands-on exercises in computer vision and natural language processing. You will train deep learning models from scratch, learning tools and tricks to achieve highly accurate results. You’ll also learn to leverage freely available, state-of-the-art pre-trained models to save time and get your deep learning application up and running today.

By participating in this is workshop you will learn
• Practice the fundamental techniques and tools required to train a deep learning model
• Gain experience with common deep learning data types and model architectures
• Enhance datasets through data augmentation to improve model accuracy
• Leverage transfer learning between models to achieve efficient results with less data and computation
• Build confidence to take on your own project with a modern, deep learning framework

Prerequisites
Understanding of fundamental programming concepts in Python such as functions, loops, dictionaries, and arrays.

Tools, libraries, and frameworks
Tensorflow, Keras, Pandas, Numpy

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