An Introduction To Deep Learning & Computer Vision
3.2 (14 ratings)
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An Introduction To Deep Learning & Computer Vision

This course will get you started on two of the hottest topics in Machine Learning
3.2 (14 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
164 students enrolled
Created by Loony Corn
Last updated 3/2016
English
Current price: $10 Original price: $20 Discount: 50% off
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Includes:
  • 2 hours on-demand video
  • 11 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What Will I Learn?
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
View Curriculum
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.
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.


Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2-3 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!


Who 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
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Curriculum For This Course
Expand All 9 Lectures Collapse All 9 Lectures 01:50:46
+
Look Long, Look Deep
9 Lectures 01:50:46

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
Artificial Neural Networks:Perceptrons Introduced
11:18

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.

Preview 18:08

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

Perceptron Revisited
16:00

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.

Deep Learning Networks Introduced
17:01

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'

Installing Python - Anaconda and Pip
09:00

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.

Code Along - Handwritten Digit Recognition -I
14:29

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

Code Along - Handwritten Digit Recognition - II
17:35

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

Code Along - Handwritten Digit Recognition - III
06:01
About the Instructor
Loony Corn
4.3 Average rating
3,304 Reviews
26,382 Students
65 Courses
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 :-)