python_tutorials
Python

What’s New in Tensorflow 2.0?

Introduction If you are a Machine Learning Engineer, Data Scientist, or a hobbyist developing Machine Learning Models from time to time just for fun, then it is very likely that you are familiar with Tensorflow. Tensorflow is an open-source and a free framework developed by Google Brain Team written in Python, C++, and CUDA. It is used to develop, test, and deploy Machine Learning models. Initially, Tensoflow did not have full support for multiple platforms and programming languages, and it […]

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python_tutorials
Python

Guide to Basic Data Types in Python with Examples

Introduction to Python Data Types In this article, we’ll be diving into the Basic Data Types in Python. These form some of the fundamental ways you can represent data. One way to categorize these basic data types is in one of four groups: Numeric: int, float and the less frequently encountered complex Sequence: str (string), list and tuple Boolean: (True or False) Dictionary: dict(dictionary) data type, consisting of (key, value) pairs It’s important to point out that Python usually doesn’t […]

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python_tutorials
Python

Deep Learning Models in Keras – Exploratory Data Analysis (EDA)

Introduction Deep learning is one of the most interesting and promising areas of artificial intelligence (AI) and machine learning currently. With great advances in technology and algorithms in recent years, deep learning has opened the door to a new era of AI applications. In many of these applications, deep learning algorithms performed equal to human experts and sometimes surpassed them. Python has become the go-to language for Machine Learning and many of the most popular and powerful deep learning libraries […]

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python_tutorials
Python

Deep Learning in Keras – Data Preprocessing

Introduction Deep learning is one of the most interesting and promising areas of artificial intelligence (AI) and machine learning currently. With great advances in technology and algorithms in recent years, deep learning has opened the door to a new era of AI applications. In many of these applications, deep learning algorithms performed equal to human experts and sometimes surpassed them. Python has become the go-to language for Machine Learning and many of the most popular and powerful deep learning libraries […]

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python_tutorials
Python

Integrating H2 with Python and Flask

Introduction H2 is a lightweight database server written in Java. It can be embedded in Java applications, or run as a standalone server. In this tutorial, we’ll review why H2 can be a good option for your projects. We’ll also learn how to integrate H2 with Python by building a simple Flask API. The Features of H2 H2 was built with performance in mind. “H2 is a combination of: fast, stable, easy to use, and features”. Although H2 is prominent […]

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EDGE DETECTION, Machine Learning

A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch

This work presents Kornia, an open source computer vision library built upon a set of differentiable routines and modules that aims to solve generic computer vision problems. The package uses PyTorch as its main backend, not only for efficiency but also to take advantage of the reverse auto-differentiation engine to define and compute the gradient of complex functions… Inspired by OpenCV, Kornia is composed of a set of modules containing operators that can be integrated into neural networks to train […]

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Machine Learning, OPTICAL CHARACTER RECOGNITION

PP-OCR: A Practical Ultra Lightweight OCR System

The Optical Character Recognition (OCR) systems have been widely used in various of application scenarios, such as office automation (OA) systems, factory automations, online educations, map productions etc. However, OCR is still a challenging task due to the various of text appearances and the demand of computational efficiency… In this paper, we propose a practical ultra lightweight OCR system, i.e., PP-OCR. The overall model size of the PP-OCR is only 3.5M for recognizing 6622 Chinese characters and 2.8M for recognizing […]

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Machine Learning

Exploring Intensity Invariance in Deep Neural Networks for Brain Image Registration

Image registration is a widely-used technique in analysing large scale datasets that are captured through various imaging modalities and techniques in biomedical imaging such as MRI, X-Rays, etc. These datasets are typically collected from various sites and under different imaging protocols using a variety of scanners… Such heterogeneity in the data collection process causes inhomogeneity or variation in intensity (brightness) and noise distribution. These variations play a detrimental role in the performance of image registration, segmentation and detection algorithms. Classical […]

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Machine Learning

Tabling Optimization for Contextual Abduction

Tabling for contextual abduction in logic programming has been introduced as a means to store previously obtained abductive solutions in one context to be reused in another context. This paper identifies a number of issues in the existing implementations of tabling in contextual abduction and aims to mitigate the issues… We propose a new program transformation for integrity constraints to deal with their proper application for filtering solutions while also reducing the table memory usage. We further optimize the table […]

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ANOMALY DETECTION, Machine Learning

Automating Outlier Detection via Meta-Learning

Given an unsupervised outlier detection (OD) task on a new dataset, how can we automatically select a good outlier detection method and its hyperparameter(s) (collectively called a model)? Thus far, model selection for OD has been a “black art”; as any model evaluation is infeasible due to the lack of (i) hold-out data with labels, and (ii) a universal objective function… In this work, we develop the first principled data-driven approach to model selection for OD, called MetaOD, based on […]

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