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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.
Python Activity: Simple Handwriting Recognition
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|Section 1: Look Long, Look Deep|
You, This Course, and Us!Preview
|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|
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.
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
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.
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'
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.
Continuing on with the handwritten digit recognition problem, we build a neural network and specify the training process.
We have a trained neural network, feed it some test data and check the accuracy.
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 :-)