A Review of Popular Deep Learning Architectures: AlexNet, VGG16, and GoogleNet
This review explores three foundational deep learning architectures—AlexNet, VGG16, and GoogleNet—that have significantly advanced the field of computer vision.
ReLU vs ELU: Picking the Right Activation for Deep Nets
Compare ReLU vs ELU activation functions in deep learning. Learn their differences, advantages, and how to choose the right one for your neural network.
Top Object Detection Models for Your Projects in 2025
Discover the best object detection models for your AI project. Learn how to compare speed, accuracy, and efficiency to select the right model.
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.
How to maximize GPU utilization by finding the right batch size
Explore how to use various tools to maximize GPU utilization by finding the right batch size for model training in Jupyter Notebooks.
Implementing Seq2Seq Models for Text Summarization With Keras
This series gives an advanced guide to different recurrent neural networks (RNNs). You will gain an understanding of the networks themselves, their architectures, applications, and how to bring them to life using Keras.
LLM Fine-Tuning: A Guide for Domain-Specific Models
Learn how LLM fine-tuning works and why it’s essential for building accurate domain-specific models. A beginner-friendly guide with key concepts and steps.
Deep Learning Architecture Review: DenseNet, ResNeXt, MnasNet & ShuffleNet v2
Understand the strengths and applications of popular deep learning architectures—DenseNet, ResNeXt, MnasNet, and ShuffleNet v2. Learn how these models enhance efficiency, accuracy, and performance in AI and computer vision tasks.
RF-DETR: Real-Time Object Detection with Transformers
RF-DETR, is a state-of-the-art real-time object detection model built on transformers. Learn how it achieves high accuracy, low latency, and adaptability.
Beyond Vector Databases: RAG Architectures Without Embeddings
Discover how Retrieval-Augmented Generation (RAG) architectures can work without embeddings. Learn approaches for knowledge retrieval in AI.
