How to Evaluate Deep Learning Models: Key Metrics Explained
Learn to evaluate deep learning models using the confusion matrix, accuracy, precision, and recall. Covers binary, multi-class, and object detection with Scikit-learn.
PyTorch 101, Understanding Graphs, Automatic Differentiation and Autograd
In this article, we dive into how PyTorch’s Autograd engine performs automatic differentiation.
Audio Transcription Effortlessly with Distill Whisper AI
Discover Distil Whisper, a lightweight version of OpenAI’s Whisper model that delivers up to 6x faster audio transcription without sacrificing accuracy.
Apache Airflow Explained: A Beginner-Friendly Guide
Learn the basics of Apache Airflow in this beginner-friendly guide, including how workflows, DAGs, and scheduling work to simplify and automate data pipelines.
YOLOv9 Object Detection Features, Benefits, and Use Cases
Explore YOLOv9, known for the novel architecture GELAN and Reversible Network Architecture to address the unreliable gradient issue in Deep Neural Networks.
Fine-Tuning LLMs on a Budget: Using GPU cloud servers
Learn the tips and tricks to fine-tune large language models affordably using GPU cloud servers, PEFT, quantization, and open-source tools.
Introduction to Extractive and Abstractive Summarization Techniques
In this theory we cover the background theory behind a variety of methodologies for abstractive text summarization
Transformers for Language Translation, Classification and Segmentation challenge
in this tutorial, we look at how Transformers enables several classical NLP techniques like translation, classification, and segmentation of text.
Ensembling Neural Network Models
Discover how ensembling enhances neural network performance with step-by-step techniques, key advantages, and limitations.
Building a simple Generative Adversarial Network (GAN) using TensorFlow
Generative Adversarial Networks or GANs are one of the most active areas in deep learning research and development due to their incredible ability to generate synthetic results. In this blog, we will build out the basic intuition of GANs through a concrete example.
