Deep Learning Explained

开始时间: 04/22/2022 持续时间: 6 weeks

所在平台: EdxArchive 课程类别: 其他类别 大学或机构: Microsoft 授课老师： Roland Fernandez Sayan Pathak PhD. Jonathan Sanito

课程主页: https://www.edx.org/archive/deep-learning-explained-microsoft-dat236x

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课程详情

Machine learning uses computers to run predictive models that learn from existing data to forecast future behaviors, outcomes, and trends. Deep learning is a sub-field of machine learning, where models inspired by how our brain works are expressed mathematically, and the parameters defining the mathematical models, which can be in the order of few thousands to 100+ million, are learned automatically from the data.

Deep learning is a key enabler of AI powered technologies being developed across the globe. In this deep learning course, you will learn an intuitive approach to building complex models that help machines solve real-world problems with human-like intelligence. The intuitive approaches will be translated into working code with practical problems and hands-on experience. You will learn how to build and derive insights from these models using Python Jupyter notebooks running on your local Windows or Linux machine, or on a virtual machine running on Azure. Alternatively, you can leverage the Microsoft Azure Notebooks platform for free.

This course provides the level of detail needed to enable engineers / data scientists / technology managers to develop an intuitive understanding of the key concepts behind this game changing technology. At the same time, you will learn simple yet powerful “motifs” that can be used with lego-like flexibility to build an end-to-end deep learning model. You will learn how to use the Microsoft Cognitive Toolkit — previously known as CNTK — to harness the intelligence within massive datasets through deep learning with uncompromised scaling, speed, and accuracy.

课程大纲

• The components of a deep neural network and how they work together
• The basic types of deep neural networks (MLP, CNN, RNN, LSTM) and the type of data each is designed for
• A working knowledge of vocabulary, concepts, and algorithms used in deep learning
• How to build:
  • An end-to-end model for recognizing hand-written digit images, using a multi-class Logistic Regression and MLP (Multi-Layered Perceptron)
  • A CNN (Convolution Neural Network) model for improved digit recognition
  • An RNN (Recurrent Neural Network) model to forecast time-series data
  • An LSTM (Long Short Term Memory) model to process sequential text data

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