Solving A Problem In TensorFlow Using SoftMax

Kiran Gutha gives us a fairly simple solution to the MNIST digit data set using the SoftMax algorithm:

In this tutorial, we will train a machine learning model for predicting numbers in pictures. Our goal is not to design a world-class complex model (although we will give you the source code to implement first-rate predictive models later). Rather, this tutorial is to introduce how to use TensorFlow. So, we start here with a very simple mathematical model called Softmax Regression.

The implementation code for this tutorial is short, and the really interesting content is only contained in three lines of code. However, it is very important to understand the design ideas contained in these codes: the basic concepts of TensorFlow workflow and machine learning. Therefore, this tutorial will explain in detail the implementation of these codes.

This is about as easy as it gets with neural networks, but easy doesn’t mean wrong.

Related Posts

Sentiment Analysis with Python

Bruno Stecanella shows us how to use MonkeyLearn to perform sentiment analysis in Python: Sentiment analysis is a set of Natural Language Processing (NLP) techniques that takes a text (in more academic circles, a document) written in natural language and extracts the opinions present in the text. In a more practical sense, our objective here is to take a text […]

Read More

Scalable Anomaly Detection with Kafka and Cassandra

Paul Brebner wraps up a series on anomaly detection at scale: The complete machine for the biggest result (48 Cassandra nodes) has 574 cores in total.  This is a lot of cores! Managing the provisioning and monitoring of this sized system by hand would be an enormous effort. With the combination of the Instaclustr managed […]

Read More

Categories

June 2018
MTWTFSS
« May Jul »
 123
45678910
11121314151617
18192021222324
252627282930