An Introduction To Deep Learning & Computer Vision

This course will get you started on two of the hottest topics in Machine Learning
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  • Lectures 9
  • Length 2 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
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About This Course

Published 3/2016 English

Course Description

Note: This course is a subset of our 20+ hour course 'From 0 to 1: Machine Learning & Natural Language Processing' so please don't sign up for both:-)

Deep Learning is one of the hottest buzzwords out there in Machine Learning today - this class will get beyond the hype, and help you understand what it's all about! And along the way, you will write a Python program that recognizes handwritten digits!

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.

Mail us about anything, and we will always reply :-)

What are the requirements?

  • 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.

What am I going to 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

What is the target audience?

  • Nope! Please don't enroll for this class if you have already enrolled for our 21-hour course 'From 0 to 1: Machine Learning and NLP in Python'
  • Yep! Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning
  • Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving
  • Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
  • Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing
  • Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Look Long, Look Deep
You, This Course, and Us!
Preview
01:14
11:18
Artificial Neural Networks are much misunderstood because of the name. We will see the Perceptron (a prototypical example of ANNs) and how it is analogous to Support Vector Machine
18:08

A quick intro 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.

16:00

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. The perceptron is the simplest of artificial neural networks - it becomes a building block for other complex networks

17:01

Multilayer perceptrons build upon the idea of a perceptron. These have layers of perceptrons that process the input and feed them forward to other layers.

09:00

Anaconda's iPython is a Python IDE. The best part about it is the ease with which one can install packages in iPython - 1 line is virtually always enough. Just say '!pip'

14:29

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.

17:35

Continuing on with the handwritten digit recognition problem, we build a neural network and specify the training process.

06:01

We have a trained neural network, feed it some test data and check the accuracy.

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Instructor Biography

Loony Corn, A 4-person team;ex-Google; Stanford, IIM Ahmedabad, IIT

Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) working in tech, in the Bay Area, New York, Singapore and Bangalore.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum

Navdeep: longtime Flipkart employee too, and IIT Guwahati alum

We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy!

We hope you will try our offerings, and think you'll like them :-)

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