English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 30m | 1.95 GB
Use Tensorflow's capabilities to perform efficient deep learning on Data sets
In this video you'll work with categorical data to predict loan performance. Categorical, structured data often appears in spreadsheets and relational databases, common data sources in business. This technique can be used to effectively predict performance or detect potential fraud.
You will also work with recurrent neural networks, which generate realistic test and placeholder data. This is useful to fill in systems with synthetic test data to simulate load and test the breadth of a working system andpredict one column from the others.
This course takes a step-by-step approach, helping you explore all the functioning of TensorFlow.
What You Will Learn
- Create data to predict loan performance
- Build a RESTful API to make predictions on table data
- Create a machine learning model for sentence generation
- Build a system to generate realistic data
1 The Course Overview
2 Setting up Your Environment
3 Importing CSV_SQL into Pandas
4 Generating Column Set Column Training and Testing
Keras Logistic Regression
5 TableModel Base Class
6 Logistic Regression
7 Deep Classification
Table Classification Service
8 Table Classification Configuration
9 REST API Definition
10 Trained Models in Docker Containers
11 Prediction Server in Docker Containers
12 The Name Generator
13 The Sentence Generator
14 A Machine Learning 'fortune'