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Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities. In this course, you’ll learn the basics of deep learning by training and deploying neural networks. You’ll learn how to: • Implement common deep learning workflows, such as image classification and object detection • Experiment with data, training parameters, network structure, and other strategies to increase performance and capability • Deploy your neural networks to start solving real-world problems Upon completion, you’ll be able to start solving problems on your own with deep learning. Learn the fundamentals of generating high-performance deep learning models in the TensorFlow platform using built-in TensorRT library (TF-TRT) and Python.

You'll explore: • How to pre-process classifications models and freeze graphs and weights in order to perform optimization • Get familiar with fundamentals of graph optimization and quantization using FP32, FP16 and INT8 • Use TF-TRT API to optimize subgraphs and select optimization parameters that best fit your model • Design and embed custom operations in Python to mitigate the non-supporting layers problem and optimize detection models Upon completion, you'll understand how to utilize TF-TRT to achieve deployment-ready optimized models. The NVIDIA Docker plugin makes it possible to containerize production-grade deep learning workflows using GPUs. Learn to reduce host configuration and administration by: • Learning to work with Docker images and manage the container lifestyle • Accessing images on the public Docker image registry—DockerHub—for maximum reuse in creating composable lightweight containers • Training neural networks using both TensorFlow and MXNet frameworks Upon completion, you’ll be able to containerize and distribute pre-configured images for deep learning. Learn to train a neural network using the Microsoft Cognitive Toolkit framework. You’ll build and train increasingly complex networks to: • Compare the expression of a neural network using BrainScript’s “Simple Network Builder” vs. Kumpulan game ps2 iso compressed download. The more generalizable “Network Builder” • Visualize neural network graphs • Train and test a neural network to classify handwritten digits Upon completion, you’ll have basic knowledge of convolutional neural networks (CNNs) and be prepared to move to the more advanced usage of Microsoft Cognitive Toolkit.

Deep neural networks are better at classifying images than humans, which has implications beyond what we expect of computer vision. Learn how to convert radio frequency (RF) signals into images to detect a weak signal corrupted by noise. You’ll be trained how to: • Treat non-image data as image data • Implement a deep learning workflow (load, train, test, adjust) in DIGITS • Test performance programmatically and guide performance improvements Upon completion, you’ll be able to classify both image and image-like data using deep learning.

The CUDA computing platform enables the acceleration of CPU-only applications to run on the world’s fastest massively parallel GPUs. Experience C/C++ application acceleration by: • Accelerating CPU-only applications to run their latent parallelism on GPUs • Utilizing essential CUDA memory management techniques to optimize accelerated applications • Exposing accelerated application potential for concurrency and exploiting it with CUDA streams • Leveraging command line and visual profiling to guide and check your work Upon completion, you’ll be able to accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA tools and techniques. Learn the basics of OpenACC, a high-level programming language for programming on GPUs. This course is for anyone with some C/C++ experience who is interested in accelerating the performance of their applications beyond the limits of CPU-only programming.

In this course, you’ll learn: • Four simple steps to accelerating your already existing application with OpenACC • How to profile and optimize your OpenACC codebase • How to program on multi-GPU systems by combining OpenACC with the message passing interface (MPI) Upon completion, you’ll be able to build and optimize accelerated heterogeneous applications on multiple GPU clusters using a combination of OpenACC, CUDA-aware MPI, and NVIDIA profiling tools. Learn how to accelerate your C/C++ or Fortran application using OpenACC to harness the massively parallel power of NVIDIA GPUs. OpenACC is a directive-based approach to computing where you provide compiler hints to accelerate your code, instead of writing the accelerator code yourself. Get started on the four-step process for accelerating applications using OpenACC: • Characterize and profile your application • Add compute directives • Add directives to optimize data movement • Optimize your application using kernel scheduling Upon completion, you will be ready to use a profile-driven approach to rapidly accelerate your C/C++ applications using OpenACC directives. Learn how to accelerate your C/C++ application using drop-in libraries to harness the massively parallel power of NVIDIA GPUs. You'll work through three exercises, including how to: • Use cuBLAS to accelerate a basic matrix multiply • Combine libraries by adding some cuRAND API calls to the previous cuBLAS calls • Use nvprof to profile code and optimize with some CUDA Runtime API calls Upon completion, you'll be ready to utilize several CUDA enabled libraries for rapid application acceleration in your existing CPU-only C/C++ programs.