Detailed NLP Basics with Hands-on Implementation in Python (Part-1)
Google Image (NLP)
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Author: Deep Learner
Aiding Farm Operations with Artificial Intelligence Chatbot
Samia S. Azim1, Varun Aggrawal2, and Dharmendra Sarsawat3 1Department of Computer Science, Institute of Business Administration Karachi 2Elmore School of Electrical and Computer Engineering
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Python’s doctest: Document and Test Your Code at Once
Are you interested in writing usage examples for your code that work as documentation and test cases simultaneously? If your answer is yes, then Python’s doctest module is for you. This module provides a testing framework that doesn’t have too steep a learning curve. It’ll allow you to use code examples for two purposes: documenting and testing your code. Apart from allowing you to use your code’s documentation for testing the code itself, doctest will help you keep your code […]
Read moreWhat went wrong with Tay, the Twitter bot that turned racist?
Of late, we’ve been hearing about Twitter bots in the news due to the whole saga of Elon Musk buying Twitter. One of the reasons the deal took so long to pan out was Musk’s concerns about the number of spam bots running rampant on the platform. While Musk believes that bots make up more than 20% of accounts on Twitter, Twitter states that the number of bots on its platform is marginal. So, what’s this Twitter bot thing? A […]
Read moreThe Business Case For AI: A Review by Customer Contact Week Conference & Magazine
“Stop using AI.” This is how Dr. Kavita Ganesan, an AI expert since 2005, begins her book The Business Case for AI. In a refreshingly direct tone, Ganesan goes on to deliver the news that, yes, you probably need to rethink your use of AI and, no, it does not need to be this difficult or this expensive. While AI is a necessary tool for businesses to remain competitive, many find themselves worried about the investment, and the consequences– what […]
Read moreWhat are Precision & Recall in Machine Learning?
Precision and recall are commonly used metrics to measure the performance of machine learning models or AI solutions in general. It helps understand how well models are making predictions. Let’s use an email SPAM prediction example. Say you have a model that looks at an email and decides whether it’s SPAM or NOT SPAM. To see how well it’s doing, you want to compare it with human-generated labels, which we will call the actual labels. To demonstrate this, the table […]
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Complete Tutorial for torch.sum() to Sum Tensor Elements in PyTorch
Introduction In this tutorial, we will do an in-depth understanding of how to use torch.sum() function to sum the elements in PyTorch tensor. We will first understand its syntax and then cover its functionalities with various examples and illustrations to make it easy for beginners. What is torch.sum() function in PyTorch The torch sum() function is used to sum up the elements inside the tensor in PyTorch along a given dimension or axis. On the surface, this may look like […]
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How to use torch.add() to Add Tensors in PyTorch
First Tensor: tensor([[[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]], [[12, 13, 14], [15, 16, 17], [18, 19, 20], [21, 22, 23]]]) Second Tensor: tensor([[[ 4, 5, 6], [ 7, 8, 9], [10, 11, 12], [13, 14, 15]], [[16, 17, 18], [19, 20, 21], [22, 23, 24], [25, 26, 27]]]) Sum of Tensor1 and Tensor2: tensor([[[ 4, 6, 8], [10, 12, 14], [16, 18, 20], [22, 24, 26]], [[28, 30, 32], [34, […]
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How to use torch.sub() to Subtract Tensors in PyTorch
First Tensor: tensor([[[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]], [[12, 13, 14], [15, 16, 17], [18, 19, 20], [21, 22, 23]]]) Second Tensor: tensor([[[ 4, 5, 6], [ 7, 8, 9], [10, 11, 12], [13, 14, 15]], [[16, 17, 18], [19, 20, 21], [22, 23, 24], [25, 26, 27]]]) Tensor1 minus Tensor2: tensor([[[-4, -4, -4], [-4, -4, -4], [-4, -4, -4], [-4, -4, -4]], [[-4, -4, -4], [-4, -4, -4], [-4, […]
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