Mastering Logistic Regression with Scikit-Learn: A Complete Guide
Understand logistic regression with Scikit-Learn. Learn key concepts, implementation steps, and best practices for predictive modeling.
How to Choose the Right GPU for vLLM Inference
Size and configure GPUs for vLLM inference. Master memory requirements, KV cache, quantization, and tensor parallelism for LLM deployment.
Stable Diffusion Tutorial Part 2: Using Textual Inversion Embeddings to gain substantial control over your generated images
Learn how to train textual inversion for Stable Diffusion in a Jupyter Notebook and generate samples that represent the features of the training images.
A Guide to the DataLoader Class and Abstractions in PyTorch
We will explore one of the biggest problems in the fields of Machine Learning and Deep Learning: the struggle of loading and handling different types of data.
Visual Question Answering: a Survey
URL: https://www.progressiverobot.com/introduction-to-visual-question-answering/ We've seen a lot of advancements in the last few years in many subdomains of machine learning. Computer vision tasks like object detection and image segmentation, as well as NLP tasks like entity recognition, language generation, and question answering, are now being solved by neural networks and approached much differently, with more speed […]
Stable Diffusion Tutorial Part 1: Run Dreambooth in Notebooks
In this article, we covered creating a Dreambooth concept from scratch, generating images from prompts, and exporting the concept as a model checkpoint.
Prepare a dataset for training and validation of a Large Language Model (LLM)
In this short tutorial, we will learn how to prepare a balanced dataset that can be used to train a large language model (LLM).
Fara7B Shows Power of Synthetic Data Scaling for Computer Use Agents
‘New Computer Use Agent Model, Fara7B showcases the effectiveness of scaling data with synthetic data generation engine FaraGen.’
The Swish Activation Function
This blogpost is an in-depth discussion of the Google Brain paper titled “Searching for activation functions” which has since revived research into activation functions.
Evaluating Object Detection Models Using Mean Average Precision (mAP)
URL: https://www.progressiverobot.com/mean-average-precision/ To evaluate object detection models like R-CNN and YOLO, the mean average precision (mAP) is used. The mAP compares the ground-truth bounding box to the detected box and returns a score. The higher the score, the more accurate the model is in its detections. In my last article we looked in detail at […]