Video description
Deep learning has been especially successful in computer-vision tasks such as image classification because convolutional neural nets (CNNs) can create hierarchical levels of representations in an image. One of the most remarkable advances is ResNet, the CNN that surpassed human-level accuracy for the first time in history.
ImageNet competition has become the de facto benchmark for image classification in the research community. The “small” ImageNet data contains more than 1.2 million images distributed in 1,000 classes.
Miguel González-Fierro explains how to train a state-of-the-art deep neural network, ResNet, using Microsoft RSever and MXNet with the ImageNet dataset. (While most of the deep learning libraries are programmed in C++ and Python, only MXNet offers an API for R programmers.) Miguel then demonstrates how to operationalize this training for real-world business problems related to image classification.
Product information
- Title: Mastering computer vision problems with state-of-the-art deep learning architectures, MXNet, and GPU virtual machines
- Author(s):
- Release date: June 2018
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492037309
You might also like
audiobook
What's New in Software Architecture: Data Mesh and the AI Revolution with Zhamak Dehghani (Audio)
Join Neal Ford and Zhamak Dehghani for a discussion about the challenges of creating, sharing, and …
book
Mastering Computer Vision with TensorFlow 2.x
Apply neural network architectures to build state-of-the-art computer vision applications using the Python programming language Key …
video
Deep Learning with TensorFlow: Applications of Deep Neural Networks to Machine Learning Tasks
6+ Hours of Video Instruction is an introduction to Deep Learning that bring the revolutionary machine-learning …
video
Machine Learning Projects with TensorFlow 2.0
TensorFlow is the world’s most widely adopted framework for Machine Learning and Deep Learning. TensorFlow 2.0 …