Today, I am excited to introduce Hisaab (Daily Expense Tracker) Android app which supports speech recognition for quick input and works OFFLINE also. It will help you to note your incomes/expenses and calculate the total amount quickly. You can classify income and expenses by categories and payment methods. Also, you can search and view different reports.
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.