Predictive Analytics with PyTorch

سرفصل های دوره

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.

1. Course Overview

1. Course Overview

2. Implementing Predictive Analytics with Numeric Data

01. Version Check

02. Prerequisites and Course Outline

03. Structural and Predictive Models

04. Demo - Install and Setup Pytorch

05. Demo - Preparing Data

06. Demo - Building a Simple Neural Network to Perform Regression

07. Demo - Exploring the Diamonds Dataset

08. Demo - Preparing and Processing Data

09. Demo - Building and Training a Regression Model

10. Demo - Exploring and Preprocessing Data

11. Demo - Defining the Neural Network and Helper Functions

12. Demo - Building and Training Custom Neural Networks for Classification

3. Implementing Predictive Analytics with Text Data

1. Text as Sequential Data

2. The Recurrent Neuron

3. RNN Training and Long Memory Cells

4. RNN to Generate Names in Languages

5. Demo - Loading and Preparing Training Data

6. Demo - Setting up Helper Functions

7. Demo - Defining the RNN

8. Demo - Training the RNN and Generating Names

4. Implementing Predictive Analytics with User Preference Data

01. Finding Patterns in Data

02. Association Rule Learning

03. Clustering

04. Content Based Approaches to Recommendations

05. Collaborative Filtering

06. Nearest Neighborhood

07. Matrix Factorization

08. Alternating Least Squares to Estimate the Ratings Matrix

09. Evaluation Metrics vs. Loss Metrics

10. Mean Average Precision @ K

11. Demo - Initializing the Ratings Matrix

12. Demo - Setting up the Neural Network

13. Demo - The Train Helper Function

14. Demo - The Evaluate Helper Function

15. Demo - Building and Training the Recommendation System Neural Network

16. Summary and Further Study

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تولید کننده: PluralSight