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Image Classification Using SingleStore DB, Keras, and Tensorflow

Abstract

Image classification can have many practical, valuable and life-saving benefits. The "Hello World" of image classification is often considered MNIST and, more recently, Fashion MNIST. This article will use Fashion MNIST and store the images in a SingleStore DB database. We'll also build an image classification model using Keras and Tensorflow and store the prediction results in SingleStore DB. Finally, we'll build a quick visual front-end to our database system using Streamlit that enables us to retrieve an image and determine if the model correctly identified it.

The SQL scripts, Python code and notebook files used in this article are available on GitHub. The notebook files are available in DBC, HTML and iPython formats.

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Automatic Machine Learning (AutoML) Infrastructure — Oracle Data Science Cloud Service

In this article, I will talk about AutoML, one of the features that come with the Oracle Cloud Data Science Service, and I hope it will be a useful article in terms of awareness.

As it is known and mentioned in my previous articles, Oracle recently added a new service called Data Science to cloud services. This service has been offered to users as a platform where many libraries come pre-installed. This platform, which includes many features like prototype development, project development, model management, to the production of produced models, contains many new features. Undoubtedly, one of the most interesting and useful features is the AutoML feature.

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A Complete Guide To Math And Statistics For Data Science

As Josh Wills once said,

“Data Scientist is a person who is better at statistics than any programmer and better at programming than any statistician.”

Math and Statistics for Data Science are essential because these disciples form the basic foundation of all the Machine Learning Algorithms. In fact, Mathematics is behind everything around us, from shapes, patterns, and colors, to the count of petals in a flower. Mathematics is embedded in each and every aspect of our lives.

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Top 10 Python Libraries You Must Know in 2019

In this article, we will discuss some of the top libraries in Python that can be used by developers to prase, clean, and represent data and implement machine learning in their existing applications.

We will be considering the following 10 libraries:

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TensorFlow With Keras (Part 2)

This article is in continuation to Part 1, Tensorflow for deep learning. Make sure you go through it for a better understanding of this case study.

Keras is a high-level neural network API written in Python and capable of running on top of Tensorflow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. In this article, we are going to cover one small case study for fashion mnist.

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PyTorch Neural Quirks

PyTorch uses some different software models than you might be used to, especially if you migrate to using it from something like Keras or TensorFlow. This first is, of course, the Tensor concept (something shared with TensorFlow, but not so obvious in Keras). The second is the nn.Module hierarchy you need to use when building a particular network. The final one is implied dimensionality and the channel concept. Of these, I'd really like to focus on the latter its own article, so let's get the first two out of the way first.

Tensors in PyTorch are really just values, and they mirror many of the methods available on NumPy arrays — like ones(), zeros(), etc. They have specific naming conventions on instances too. For example, Tensor::add_() will add to the calling addend and adding in place, while Tensor::add() will return a new Tensor with the new cumulative value. They support list-like indexing semantics, slicing, and comprehensions as well. They convert easily too and from NumPy arrays as well via the torch.from_numpy() and Tensor::numpy() methods. They also have a sense of location and are affiliated with a specific device, and this is where things can get tricky.