Online course for an Introduction To Deep Learning & Computer Vision

About Course

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  Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

  Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

  • Deep Learning Networks are the cutting edge solution for the handwritten digit recognition problem and many others in computer vision. These are often large artificial neural networks.
  • A quick introduction to Computer Vision, and one of the most popular starter problems - identifying handwritten digits using the MNIST database. We also talk about feature extraction from images.
  • Perceptron Reintroduced: The perceptron is the simplest of artificial neural networks - it becomes a building block for other complex networks

  Python Activity: Simple Handwriting Recognition

  • Train a neural network to classify handwritten digits in Python. First start by downloading and unzipping the MNIST database images to create some training and test datasets.
  • Then we build a neural network and specify the training process.
  • We now have a trained neural network, feed it some test data and check the accuracy.

Curriculam

• Section 1: Look Long, Look Deep
  	Lecture 1	You, This Course, and Us!	01:14
  	Lecture 2	Artificial Neural Networks:Perceptrons Introduced	11:18
  	Lecture 3	Computer Vision - An Introduction	18:08
  	Lecture 4	Perceptron Revisited	16:00
  	Lecture 5	Deep Learning Networks Introduced	17:01
  	Lecture 6	Installing Python - Anaconda and Pip	09:00
  	Lecture 7	Code Along - Handwritten Digit Recognition -I	14:29
  	Lecture 8	Code Along - Handwritten Digit Recognition - II	17:35
  	Lecture 9	Code Along - Handwritten Digit Recognition - III	06:01

What you will get from this course?

• • Design and Implement a simple computer vision use-case: digit recognition
  • Grasp the theory underlying deep learning and computer vision
  • Confidently move on to more complex and comprehensive material on these topics
  • Understand use-cases for computer vision as well as deep learning

Who should buy this course?

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  • Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning
  • Engineers who want to understand or learn machine learning and apply it to problems they are solving
  • Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
  • Tech executives and investors who are interested in big data, machine learning or natural language processing
  • MBA graduates or business professionals who are looking to move to a heavily quantitative role