Mastering Accuracy Testing with Confusion Matrix in Machine Learning
- Authors
- Published on
- Published on
In this riveting video from Brandon Rohrer, the team delves into the exhilarating world of testing trained data. It's like strapping yourself into a high-performance racing car, eager to see how well it handles the twists and turns of the track. They kick things off by importing a batch of testing data, consisting of a whopping ten thousand handwritten images. Picture this: a confusion logger swoops in, capturing actual and predicted labels, setting the stage for a showdown in the form of a confusion matrix. It's like watching a high-octane race unfold, with every move meticulously calculated and executed.
As the adrenaline builds, the team introduces a nifty convenience function called load structure, streamlining the process of loading up a specified file name. They then set the number of testing iterations, seamlessly connecting testing data blocks and gearing up for the ultimate test run. The air is electric with anticipation as they initialize total loss and the trusty confusion logger, fine-tuning every aspect for peak performance. It's a symphony of precision and expertise, with every detail meticulously crafted for maximum impact.
With engines revving, the team embarks on a testing loop that will push their creation to the limits. Each iteration sees a forward pass on the network, no backward steps this time, just a relentless drive towards accuracy. The total loss climbs with each run, as values are logged into the confusion matrix with surgical precision. The team's dedication shines through as they meticulously track predicted and actual labels, ensuring every data point is accounted for. And when the dust settles, they unveil the average loss, a testament to their unwavering commitment to excellence.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Watch Build a 2D convolutional neural network, part 12: Testing loop on Youtube
Viewer Reactions for Build a 2D convolutional neural network, part 12: Testing loop
I'm sorry, but I am unable to access the specific video or channel you are referring to. If you provide me with the comments, I can summarize them for you.
Related Articles
Exploring Cottonwood: Updates, Version Control, and Enhanced Accessibility
Brandon Rohrer introduces updates to the Cottonwood machine learning framework, emphasizing version control and directory restructuring for improved accessibility. Experimental features and a diverse dataset collection hint at a promising future for Cottonwood users.
Mastering Neural Network Text Summarization and Visualization
Delve into neural network text summarization and custom visualization techniques. Learn the importance of documenting parameters for replication and organizing predictions for accurate analysis. Explore the meticulous process of ensuring correctness in machine learning code.
Mastering k-Nearest Neighbors: A Guide to Efficient Classification
Explore the power of the k-nearest neighbors algorithm in classification and regression tasks. Learn about feature scaling, distance metrics, and handling categorical data efficiently in machine learning. Brandon Rohrer's insightful guide sheds light on the nuances of this versatile tool.
Mastering Data Science: Simplified Concepts and Practical Insights
Brandon Rohrer simplifies data science concepts in this engaging keynote. Learn about data types, engineering, asking sharp questions, and predicting stock prices with historical data. Dive into the world of data analysis with practical insights and expert guidance.