Compare MXNet vs. PyTorch

MXNet is ranked 13th in AI Development Platforms while PyTorch is ranked 12th in AI Development Platforms. MXNet is rated 0, while PyTorch is rated 0. On the other hand, MXNet is most compared with , whereas PyTorch is most compared with .
Cancel
You must select at least 2 products to compare!
MXNet Logo
4 views|4 comparisons
PyTorch Logo
5 views|4 comparisons
Ranking
13th
Views
4
Comparisons
4
Reviews
0
Average Words per Review
0
Avg. Rating
N/A
12th
Views
5
Comparisons
4
Reviews
0
Average Words per Review
0
Avg. Rating
N/A
Top Comparisons
Learn
Apache
PyTorch
Video Not Available
Overview

Apache MXNet is a lean, flexible, and ultra-scalable deep learning framework that supports state of the art in deep learning models, including convolutional neural networks (CNNs) and long short-term memory networks (LSTMs).

We've built this course as an introduction to deep learning. Deep learning is a field of machine learning utilizing massive neural networks, massive datasets, and accelerated computing on GPUs. Many of the advancements we've seen in AI recently are due to the power of deep learning. This revolution is impacting a wide range of industries already with applications such as personal voice assistants, medical imaging, automated vehicles, video game AI, and more.

In this course, we'll be covering the concepts behind deep learning and how to build deep learning models using PyTorch. We've included a lot of hands-on exercises so by the end of the course, you'll be defining and training your own state-of-the-art deep learning models.

Offer
Learn more about MXNet
Learn more about PyTorch
Sample Customers
Pioneer, nvidia, acer, dely, gumgum, ciao, affable, intel, clusterone, europace, comet, magnet, basler
Information Not Available
We monitor all AI Development Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.