Cloud Academy – Getting Started With Deep Learning Improving Performance

继续学习从学习循环神经网络的原理中学到的知识，以及它们如何解决涉及排序的问题，通过这门关于提高性能的连贯课程。从让您回答正确问题的学习曲线开始，学习提高神经网络的性能。这可能需要更多数据，甚至需要构建更好的模型来提高性能。

在课程中，您将探索有关 bash 规范化、辍学和正则化的基础知识。

本课程还涉及数据增强及其允许您从初始训练数据构建新数据的能力，最终实现超参数优化。这是一个帮助您决定如何调整网络外部参数的工具。

MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Difficulty: Beginner | Genre: eLearning | Language: English | Duration: 17 Lectures (1h 2m) | Size: 876 MB

What you’ll learn:
Learn how to improve the performance of your neural networks.
Learn the skills necessary to make executive decisions when working with neural networks.

Description
Move on from what you learned from studying the principles of recurrent neural networks, and how they can solve problems involving sequencing, with this cohesive course on Improving Performace. Learn to improve the performance of your neural networks by starting with learning curves that allow you to answer the right questions. This could be needing more data or, even, building a better model to improve your performance.

Further into the course, you will explore the fundamentals around bash normalization, drop-out, and regularization.

This course also touches on data augmentation and its ability to allow you to build new data from your starting training data, culminating in hyper-parameter optimization. This is a tool to that aids in helping you to decide how to tune the external parameters of your network.

This course is made up of 13 lectures and three accompanying exercises. This Cloud Academy course is in collaboration with Catalit.