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.
edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.
Week 1: Introduction to deep learning and a quick recap of machine learning concepts. Week 2: Building a simple multi-class classification model using logistic regression Week 3: Detecting digits in hand-written digit image, starting by a simple end-to-end model, to a deep neural network Week 4: Improving the hand-written digit recognition with convolutional network Week 5: Building a model to forecast time data using a recurrent network Week 6: Building text data application using recurrent LSTM (long short term memory) units
Sayan Pathak PhD., Roland Fernandez and Jonathan Sanito
completed this course, spending 5 hours a week on it and found the course difficulty to be medium.
Very interesting and enjoyed taking the course. Good way to get started with CNTK. I will however not recommend using Azure notebooks for the assignments as its painfully slow especially for later assignments. Setup locally or use the Docker image on Mac as I did.