Deep Learning with MXNet Cookbook

Gain practical, recipe-based insights into the world of deep learning using Apache MXNet for flexible and efficient research prototyping, training, and deployment to productionKey FeaturesCreate scalable deep learning applications using MXNet products with step-by-step tutorialsImplement tasks such as transfer learning, transformers, and more with the required speed and scalabilityAnalyze model performance and fine-tune for accuracy, scalability, and speedPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionExplore the capabilities of the open-source deep learning framework MXNet to train and deploy neural network models and implement state-of-the-art (SOTA) architectures in Computer Vision, natural language processing, and more. The Deep Learning with MXNet Cookbook is your gateway to constructing fast and scalable deep learning solutions using Apache MXNet.Starting with the different versions of MXNet, this book helps you choose the optimal version for your use and install your library. You’ll work with MXNet/Gluon libraries to solve classification and regression problems and gain insights into their inner workings. Venturing further, you’ll use MXNet to analyze toy datasets in the areas of numerical regression, data classification, picture classification, and text classification. From building and training deep-learning neural network architectures from scratch to delving into advanced concepts such as transfer learning, this book covers it all. You'll master the construction and deployment of neural network architectures, including CNN, RNN, LSTMs, and Transformers, and integrate these models into your applications.By the end of this deep learning book, you’ll wield the MXNet and Gluon libraries to expertly create and train deep learning networks using GPUs and deploy them in different environments.What you will learnGrasp the advantages of MXNet and Gluon librariesBuild and train network models from scratch using MXNetApply transfer learning for more complex, fine-tuned network architecturesAddress modern Computer Vision and NLP problems using neural network techniquesTrain state-of-the-art models with GPUs and leverage modern optimization techniquesImprove inference run-times and deploy models in productionWho this book is forThis book is for data scientists, machine learning engineers, and developers who want to work with Apache MXNet for building fast and scalable deep learning solutions. Python programming knowledge and access to a working coding environment with Python 3.6+ is necessary to get started. Although not a prerequisite, a solid theoretical understanding of mathematics for deep learning will be beneficial.