Predictive Analytics with PyTorch

This course covers the use of PyTorch to build various predictive models, using Recurrent Neural Networks, long-memory neurons in text prediction, and evaluating them using a metric known as the Mean Average Precision @ K.
Course info
Level
Intermediate
Updated
May 1, 2020
Duration
2h 31m
Table of contents
Course Overview
Implementing Predictive Analytics with Numeric Data
Implementing Predictive Analytics with Text Data
Implementing Predictive Analytics with User Preference Data
Description
Course info
Level
Intermediate
Updated
May 1, 2020
Duration
2h 31m
Description

PyTorch is fast emerging as a popular choice for building deep learning models owing to its flexibility, ease-of-use and built-in support for optimized hardware such as GPUs. In this course, Predictive Analytics with PyTorch, you will see how to build predictive models for different use-cases, based on the data you have available at your disposal, and the specific nature of the prediction you are seeking to make.

First, you will start by learning how to build a linear regression model using sequential layers. Next, you will explore how to leverage recurrent neural networks (RNNs) to capture sequential relationships within text data. Then, you will apply such an RNN to the problem of generating names - a typical example of the kind of predictive model where deep learning far out-performs traditional natural language processing techniques. Finally, you will see how a recommendation system can be implemented in several different ways - relying on techniques such as content-based filtering, collaborative filtering, as well as hybrid methods.

When you are finished with this course, you will have the skills to build, evaluate, and use a wide array of predictive models in PyTorch, ranging from regression, through classification, and finally extending to recommendation systems.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
[Autogenerated] Hi, My name is Johnny Ravi and welcome to the scores on Predictive Analytics with Pytorch a little about myself. I have a master's degree in electrical engineering from Stanford and have worked at companies just Microsoft, Google and Flip Card at Google and was one of the first engineers working on real time collaborative editing in Google Dogs and I hold four patterns for its underlying technologies. I currently work on my own startup lunatic on a studio for high quality video content. In this course, you will see how to build predictive models for different use cases based on the data that you have available at our disposal on the specific nature of the prediction you're seeking. To me, you start off by learning how to build a linear regression model. Using sequential years, you'll understand different activation functions on dropout that can be added to your pytorch. Neural networks finally will explore how to build classifications, models and pytorch. Next, you will learn how to leverage recurrent neural networks or arguments to capture sequential relationships within text data. Finally, you will see how a recommendation system can be implemented in several different ways, using techniques such as content based filtering, collaborative filtering as well as hybrid methods. You will explore how to build a recommend the system in pytorch by modeling it as a regression model for ratings estimation. Importantly, you'll also see how such a recommend ER system can be evaluated using a complex metric known as the mean average position at gate. When you're finished with this course, you will have the skills and knowledge to build, evaluate and use a wide array of predictive models and pytorch ranging from regression through classification and finally extending to recommendation systems.