We are going to see what is data augmentation, why is it necessary, what are types of data augmentation and how we can perform it in Python by using TensorFlow.
In this tutorial, we are going to learn about custom callbacks. we will see what are callbacks and how can we can build our own callbacks in TensorFlow with Python.
We will see how can we save and load a trained model in tensorflow, not only weight but also the architecture of the model. We will do these things by using TensorFlow in Python.
We will predict the disease of the grape plant by looking at their leaves using TensorFlow in Python, we have four classes of disease of grapes we will classify plant into infected or healthy.
Using Python and its libraries for Optical Character Recognition on images to recognize text using Pytesseract and identifying faces associated with the recognized text using OpenCV.
Implementing Linear Regression with Python using different libraries like Seaborn, NumPy, Scikit-learn, and TuriCreate. In the end, building a Linear Regression Model from scratch.
Now we can make audiobook from any PDF using, Python. Just rename the pdf name, and you got an audio book for no cost!
Implementing Natural Langauge Processing in Python using the Natural Language ToolKit library, Naive Bayes classifier from Scikit-learn, and the concept of TF-IDF for normalization.
The project involves scraping data from two websites MOHFW and Instagram using Python libraries
The words are formatted using the stemming process and a bag of words is created which is then trained using Naive Bayes which gives good accuracy to predict the message is spam or not in Python.
In this blog, I am going to explain how to evaluate postfix expression using the C++ standard template library.
The hand digit is captured, processed, and predicted using the model trained. Tensorflow and OpenCV are used majorly. project is done in python