Learn an intuitive approach to building the complex models that help machines solve real-world problems with human-like intelligence. 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.
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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.
What you will learn
- 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
Course Syllabus
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
Prerequisites:
- Basic programming skills
- Working knowledge of data science
- Skills equivalent to the following courses: DAT208x: Introduction to Python for Data Science
