Gain practical, recipe-based insights into the world of deep learning using Apache MXNet for flexible and efficient research prototyping, training, and deployment to production.
Key Features:
- A step-by-step tutorial towards using MXNet products to create scalable deep learning applications
- Implement tasks such as transfer learning, transformers, and more with the required speed and scalability
- Analyze the performance of models and fine-tune them for accuracy, scalability, and speed
Book Description:
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.
What You Will Learn:
- Understand MXNet and Gluon libraries and their advantages
- Build and train network models from scratch using MXNet
- Apply transfer learning for more complex, fine-tuned network architectures
- Solve modern Computer Vision and NLP problems using neural network techniques
- Train and evaluate models using GPUs and learn how to deploy them
- Explore state-of-the-art models with GPUs and leveraging modern optimization techniques
- Improve inference run-times and deploy models in production
Who this book is for:
This book is ideal for Data scientists, machine learning engineers, and developers who want to work with Apache MXNet for building fast, scalable deep learning solutions. The reader is expected to have a good understanding of Python programming and a working environment with Python 3.6+. A good theoretical understanding of mathematics for deep learning will be beneficial.
Andrés Pérez Torres is a Data Scientist with a specialization in Computer Vision, Geometry, and Deep Learning SOTA models. He has a strong background in Mathematics and Physics. His professional experience includes serving as a Software Development Manager at Amazon Primer Air, leading UK Engineering activities and specializing in CV algorithm design with a full data life-cycle from data acquisition, annotation to model evaluation. He has extensive experience in Amazon training programs and Udacity MOOCs in Deep Learning and Computer Vision.
