MXNet is an open-source deep learning framework that allows you to train and deploy neural network models and implement state-of-the-art (SOTA) architectures in CV, NLP, and more. With this cookbook, you will be able to construct fast, scalable deep learning solutions using Apache MXNet.

This book will start by showing you the different versions of MXNet and what version to choose before installing your library. You will learn to start using MXNet/Gluon libraries to solve classification and regression problems and get an idea on the inner workings of these libraries. This book will also show how to use MXNet to analyze toy datasets in the areas of numerical regression, data classification, picture classification, and text classification. You'll also learn to build and train deep-learning neural network architectures from scratch, before moving on to complex concepts like transfer learning. You'll learn to construct and deploy neural network architectures including CNN, RNN, LSTMs, Transformers, and integrate these models into your applications.

By the end of the book, you will be able to utilize the MXNet and Gluon libraries to create and train deep learning networks using GPUs and learn how to deploy them efficiently in different environments.

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Spis treści

Deep Learning with MXNet Cookbook. Discover an extensive collection of recipes for creating and implementing AI models on MXNet eBook -- spis treści

• 1. Up and Running with MXNet
• 2. Working with MXNet and Visualizing Datasets – Gluon and DataLoader
• 3. Solving Regression Problems
• 4. Solving Classification Problems
• 5. Analyzing Images with Computer Vision
• 6. Understanding Text with Natural Language Processing
• 7. Optimizing Models with Transfer Learning and Fine-Tuning
• 8. Improving Training Performance with MXNet
• 9. Improving Inference Performance with MXNet