Deep Learning with MXNet Cookbook
Discover an extensive collection of recipes for creating and implementing AI models on MXNet
Created by Andrés P. Torres
Dive into practical deep learning with MXNet and Gluon, exploring how to build, train, and deploy powerful neural network models. You'll work hands-on with real-world tasks in computer vision and natural language processing, learning to implement and optimize state-of-the-art architectures.
Packt | Dec 2023 | 370 min
What You Will Learn
You will start by setting up MXNet and Gluon, then move on to hands-on coding exercises that walk you through building and training models from scratch. With each project, you'll tackle real datasets and apply techniques like transfer learning, optimization, and deployment, reinforcing your skills with practical examples.
Key Features
- Build and train deep neural networks for vision and NLP tasks using MXNet and Gluon
- Apply transfer learning and advanced architectures like CNNs, RNNs, and Transformers
- Optimize, fine-tune, and deploy models for fast, scalable real-world applications
Target Audience
Ideal for data scientists, machine learning engineers, and developers with Python experience who want to build scalable deep learning solutions. If you already understand basic deep learning concepts and want to apply them using MXNet in real projects, you'll find clear guidance and actionable skills here.
