Practical Machine Learning and Data Processing

  • Simple and comprehensive tutorials for machine learning and data preprocessing
  • Built on NumPy, scikit-learn, scikit-image, OpenCV, SciPy, and Tensorflow

Machine Learning
& Data Science

Data Processing and knowledge discovery in data for learning models and patterns from data for reasoning.

Image Processing
& Computer Vision

Extracting and processing the image information and leverage that for scene analysis and understanding.

Speech Processing
& Voice Analysis

Speech signal processing and feature extraction for speaker recognition and automatic speech recognition.

Deep Learning

Using massive amount of data and computational power for accurate and robust reasoning based on data


Autoencoders and their implementations in TensorFlow

by Hadi Kazemi on 2017-06-20 10:09:19

In this post, you will learn the concept behind Autoencoders as well how to implement an autoencoder in TensorFlow.

Logistic Regression using TensorFlow

by Amirsina Torfi on 2017-06-11 23:43:46

In this tutorial, the objective to decide whether the input image is digit "0" or digit "1" using Logistic Regression. In we are aimed to implement logistic regression for binary classification.

Linear Regression using TensorFlow

by Amirsina Torfi on 2017-06-04 23:51:27

In the linear regression, the linear relationships will be modeled by a predictor function which its parameters will be estimated by the data and is called a Linear Model. In this tutorial, we will introduce how to train a linear model using TensorFLow and how to showcase the generated model.

Introduction to TensorFlow Variables: Creation, Initialization

by Amirsina Torfi on 2017-05-13 13:40:45

The defined variables in TensorFlow are just tensors with certain shapes and types. The tensors must be initialized with values to become valid. In this tutorial, we are going to explain how to define and initialize variables.