This article explains a breif introduction of CNN and about how to build a model to classify images of clothing (like T-shirt, Trouser) using it in TensorFlow. If you are beginner, I would recommend to read following posts first:
This article will explain brief summary of linear regression and how to implement it using TensorFlow 2. If you are beginner, I would recommend to read following posts first:
– Setup Deep Learning environment: Tensorflow, Jupyter Notebook and VSCode
– Tensorflow 2: Build Your First Machine Learning Model with tf.keras
This article explains how to build a neural network and how to train and evaluate it with TensorFlow 2. It is assumed you know basics of machine & deep learning and want to build model in Tensorflow environment. We are going to use tf.keras APIs which allows to design, fit, evaluate, and use deep learning models to make predictions in just a few lines of code.
Few days back, I decided to setup development environment for deep learning on my Windows 10 laptop. In this article, I would share my experience in setting up a system typically for Data Science developers. Although I used Windows 10 but the steps will be same for Linux and Mac OS.
Being a developer, need IDE for coding and not fan of browser based editor. Jupyter Notebook is favourite tool for data scientist and we can’t skip that in case of data science. Fortunately, VS Code supports Jupyter notebook. You can now directly edit .ipynb files and get the interactivity of Jupyter notebooks with all of the power of VS Code. We will go through it.
This article explains how to perform different types of data visualizations in Python using Matplotlib – a Python 2D plotting library. Instead of covering features of the library, we will see the practical scenarios of data visualizations used in machine learning/deep learning. We will import pyplot function that allows us to interface with a MATLAB-like plotting environment.
To install Matplotlib, run following command in Python environment:
pip install matplotlib
I am using Python 3.7.6, Matplotlib 3.1.2 and Windows 10 environment for this article.
For a developer, Ternary operator is one of favorite thing to make the code compact and easily readable. This article explains different ways to implement Ternary operations.
<condition> ? <expression_on_true> : <expression_on_false>
Python 2.5+ added following syntax:
<expression_on_true> if <condition> else <expression_on_false>
expression_on_true will be evaluated if the condition is true, otherwise expression_on_false will be evaluated.