Explainable AI: Making AI Decisions Transparent

Explainable AI (XAI) makes AI decisions more interpretable.

Tools like SHAP and LIME provide insights into model predictions.

Example: Using SHAP for Model Explainability

import shap
import xgboost as xgb

model = xgb.XGBClassifier()
explainer = shap.Explainer(model)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test)

This code generates a SHAP summary plot to interpret feature importance.

AI in Edge Computing: Bringing Intelligence Closer to Devices

AI at the edge allows real-time decision-making on devices like smartphones and cameras. This reduces latency and reliance on cloud computing. Example: Running AI Inference on a Raspberry Pi This script loads a TensorFlow Lite model for edge computing. Read More
How AI is Reshaping Healthcare: Predicting Disease with Data

AI is improving healthcare by diagnosing diseases and recommending treatments. Deep learning models can analyze medical images to detect anomalies. Example: Using CNNs for Medical Image Classification This builds a simple Convolutional Neural Network (CNN) for image classification. Read More
MLOps: Bridging the Gap Between ML Models and Deployment

MLOps streamlines the deployment and monitoring of ML models in production. Tools like Docker, Kubernetes, and TensorFlow Serving are commonly used. Example: Serving a Model with FastAPI This creates a simple API to serve ML model predictions. Read More
Building Smarter AI Assistants: The Role of LLMs

Large Language Models (LLMs) like ChatGPT process and generate human-like text. They can assist with tasks like coding, summarization, and customer support. Example: Summarizing Text with an LLM This script summarizes a long text input using an AI model. Read More

Explainable AI: Making AI Decisions Transparent

AI in Edge Computing: Bringing Intelligence Closer to Devices

How AI is Reshaping Healthcare: Predicting Disease with Data

MLOps: Bridging the Gap Between ML Models and Deployment

Building Smarter AI Assistants: The Role of LLMs