An Introduction To Deep Learning & Computer Vision

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1.5 Hours | 433 MB

Genre: eLearning | Language: English

This course will get you started on two of the hottest topics in Machine Learning

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 🙂