Understanding LLM Poisoning
Discover how LLM poisoning works, why even 0.01% poisoned data can compromise AI systems, and the steps to prevent backdoor attacks in models.
Pooling In Convolutional Neural Networks
Explore the whys and the hows behind the process of pooling in CNN architectures, and compare 2 common techniques: max and average pooling.
Understanding Adversarial Attacks Using Fast Gradient Sign Method
In this tutorial, we use Gradio to examine adversarial attacks and their potential for misdirecting models towards making inaccurate predictions.
Weather forecast using LSTM networks
In this post, we presented the LSTM subclass and used it to construct a weather forecasting model. We proved its effectiveness as a subgroup of RNNs designed to detect patterns in data sequences, including numerical time series data.
A Simple Guide to Building AI Agents Correctly
Learn how to design and build reliable AI agents with the right architecture, tools, memory, and evaluation strategies for real-world applications.
How to train and use a custom YOLOv7 model
We examine YOLOv7 & its features, learn how to prepare custom datasets for the model, and then build a YOLOv7 demo from scratch using NBA footage.
Understanding the BART Model for Accurate Text Summarization
Explore BART (Bidirectional and Auto-Regressive Transformers), a powerful seq2seq model for NLP tasks like text summarization and generation.
PyTorch 101: Understanding Hooks
We cover debugging and visualization in PyTorch. We explore PyTorch hooks, how to use them, visualize activations and modify gradients.
A Deeper Dive into Graph Neural Networks
Learn the fundamentals of Graph Neural Networks, how they work, and how to implement them using PyTorch. Explore key concepts and examples.
Solving Question-Answering on Tabular Data: A Comparison
Tapas & TableQA are libraries that allow users to input questions directly, as if using regular speech, to enact SQL-like queries on tabular data.
