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How to Calculate Jaccard Similarity in Python
Understanding the similarity between two objects is a universal problem. In machine learning, you can use similarity measures for various issues. These include object detection, classification and segmentation tasks in computer vision and similarity between text...
Top 12 Python Libraries for Data Science and Machine Learning
Machine learning is the science of programming a computer to learn from different data and perform inference. Yesteryear, machine learning tasks involved manual coding all of the algorithms and mathematical and statistical formulae. Nowadays, we have fantastic...
The History of Reinforcement Learning
Reinforcement learning (RL) is an exciting and rapidly developing area of machine learning that significantly impacts the future of technology and our everyday lives. RL is a field separate from supervised and unsupervised learning focusing on solving problems through...
Introduction to Pandas: A Complete Tutorial for Beginners
Pandas is an open-source library providing high-performance, easy-to-use data structures, and data analysis tools for Python. It is one of the fundamental tools for data scientists and can be thought of like Python's Excel. With Pandas, you can work with many...
The Best Books For Machine Learning for Both Beginners and Experts
Machine learning (ML) is an exciting and rapidly expanding domain in Computer Science. ML is a field of study devoted to the automated improvement of computer algorithms through exposure to data. The knowledge base underneath ML consists of a broad range in topics in...
The History of Machine Learning
Machine learning is an exciting and rapidly developing field of study centered around the automated improvement (learning) of computer algorithms through experience with data. Through persistent innovation and research, the capabilities of machine learning are now in...
5 Significant Benefits of Online Learning for Data Science
The internet has made access to information very easy and affordable. Technology has been completely integrated into how we learn and how we work. It can all be supplemented or provided by online education from primary school to degree level. Learning within a...
7 Best Tips to Help Get a Data Scientist Job From Scratch
You have developed a passion and now you want to embark on a new career, but you are unsure where to start to enter the space of data science. This post will provide you with clear, practical steps to get on the road to a rewarding and stimulating career path. The...
Paper Reading #2: XLNet Explained
One of the most celebrated, recent advancements in language understanding is the XLNet model from Carnegie Mellon University and Google. It takes the "best-of-both-worlds" approach by combining auto-encoding and autoregressive language modeling to achieve...
9 Best Tips For Early Career Research Focused Data Scientists
Embarking on a career in data science is an exciting challenge that requires a lot of initiative and a desire to learn and apply knowledge quickly. For research scientists, there is an emphasis on experimentation and scientific discovery. The methods and objectives...
Understanding set.seed() in R: A Comprehensive Guide
Table of Contents Introduction The Basics of set.seed() Why Use set.seed()? Best Practices Common Use Cases Troubleshooting Advanced Usage: Random Number Generators (RNGs) in R Conclusion Introduction In R programming, reproducibility is crucial for scientific...
How To Solve PyTorch RuntimeError: Given groups, weight of size, expected input to have 3 channels, but got 4 channels instead
Contents Introduction Understanding the Error Common Causes Example to Reproduce Error Inception v3 Specific Requirements Complete Working Solution Best Practices Introduction When working with pre-trained models in PyTorch, particularly convolutional neural networks,...
How to Solve PyTorch ValueError: Expected 4-Dimensional Input
Contents Introduction Reproducing the Error Fixing the Error Visualizing the Batch Dimension Why Batch Dimensions Are Important Common Mistakes to Avoid Debugging Tensor Shapes Further Reading Summary Introduction One common error when working with pre-trained PyTorch...
Understanding the Difference Between reshape() and view() in PyTorch
Table of Contents Introduction Brief Definitions of reshape() and view() Key Differences Between reshape() and view() Visual Matrix Examples Common Operations and Best Practices Troubleshooting Conclusion Introduction In PyTorch, reshape() and view() are fundamental...
How to Solve PyTorch AttributeError: ‘_MultiProcessingDataLoaderIter’ object has no attribute ‘next’
Table of Contents Introduction Why Does This Error Occur? Replicating the Error Solution Example Solution Code Summary Introduction If you've encountered the error AttributeError: '_MultiProcessingDataLoaderIter' object has no attribute 'next' or AttributeError:...
Understanding torch.Tensor vs torch.tensor in PyTorch
Table of Contents Key Difference Behavior of torch.Tensor Behavior of torch.tensor Comparison Table Recommendation for Initialization of Tensors Further Reading Summary In PyTorch, both torch.Tensor and torch.tensor can be used to create tensors. However, there are...
Understanding unsqueeze() in PyTorch: A Beginner-Friendly Guide
Introduction unsqueeze() in PyTorch is a function that adds a dimension of size one to a tensor. While this might sound simple, understanding when and why to use it is crucial for many deep learning tasks, especially when working with neural networks and preparing...
Understanding gather() in PyTorch: A Beginner-Friendly Guide
Understanding gather() in PyTorch: A Beginner-Friendly Guide Table of Contents Introduction What is gather()? Syntax of gather() How the dim Argument Works Row-wise Selection with dim=1 Common Pitfalls and Errors Column-wise Selection with dim=0 The Book Shelf Analogy...
Mastering Custom Loss Functions in PyTorch: A Comprehensive Guide
This guide provides an in-depth look at creating custom loss functions in PyTorch, a skill valuable for those working with deep learning frameworks. Whether developing innovative models or exploring new functionalities, mastering custom loss functions in PyTorch...
Why is zero_grad() Called in PyTorch?
Contents Introduction Gradients in Neural Networks Backpropagation and Gradient Descent Without zero_grad() With zero_grad() Plotting Losses Monitoring Loss Summary When training neural networks in PyTorch, calling zero_grad() is essential before backpropagating...
Suf is a senior advisor in data science with deep expertise in Natural Language Processing, Complex Networks, and Anomaly Detection. Formerly a postdoctoral research fellow, he applied advanced physics techniques to tackle real-world, data-heavy industry challenges. Before that, he was a particle physicist at the ATLAS Experiment of the Large Hadron Collider. Now, he’s focused on bringing more fun and curiosity to the world of science and research online.