Designing Deep Learning Systems, Video Edition

Designing Deep Learning Systems, Video Edition

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 11h 57m | 1.63 GB

Designing Deep Learning Systems, Video Edition: A vital guide to building the platforms and systems that bring deep learning models to production.

In Designing Deep Learning Systems, Video Edition you will learn how to:

• Transfer your software development skills to deep learning systems
• Recognize and solve common engineering challenges for deep learning systems
• Understand the deep learning development cycle
• Automate training for models in TensorFlow and PyTorch
• Optimize dataset management, training, model serving and hyperparameter tuning
• Pick the right open-source project for your platform

Deep learning systems are the components and infrastructure essential to supporting a deep learning model in a production environment. Written especially for software engineers with minimal knowledge of deep learning’s design requirements, Designing Deep Learning Systems is full of hands-on examples that will help you transfer your software development skills to creating these deep learning platforms. You’ll learn how to build automated and scalable services for core tasks like dataset management, model training/serving, and hyperparameter tuning. This tutorial is the perfect way to step into an exciting and lucrative career as a deep learning engineer.

To be practically usable, a deep learning model must be built into a software platform. As a software engineer, you need a deep understanding of deep learning to create such a system. This video course gives you that depth.

Designing Deep Learning Systems: A software engineer’s guide teaches you everything you need to design and implement a production-ready deep learning platform. First, it presents the big picture of a deep learning system from the developer’s perspective, including its major components and how they are connected. Then, it carefully guides you through the engineering methods you’ll need to build your own maintainable, efficient, and scalable deep learning platforms.