In the current situation of Covid19, the whole world is experiencing unprecedented scenarios everywhere, which often everyone is terming as the “new normal”. But before becoming the “new normal”, these abnormal or anomalous outcomes can result in positive or negative impact for any organization and are important to keep track…

A detailed walk-through for applied computer vision for absolute beginners with basic knowledge of Python. — Throughout my career as a Data Scientist and AI Researcher, it is the image data domain that has attracted me the most. …

Read this story to learn from my experiences as I continuously push myself outside my comfort zone Every new year brings a lot of hope and positive energy to progress continuously in our journey called “life” to fulfill our goals and dreams. Like the past few years, I spend some…

Utilize the power of AI to reimagine how fictional characters should look like — Stable Diffusion models and Dall-E are one of the most latest hot topics of discussion in the world of AI. It is always fascinating to see how AI is already matching and even exceeding human beings in terms of generating artwork and content that requires a lot of creativity and…

Top 9 Python frameworks for applying Explainable AI in practice — Explainable Artificial Intelligence is the most effective practice to ensure that AI and ML solutions are transparent, trustworthy, responsible, and ethical so that all regulatory requirements on algorithmic transparency, risk mitigation, and a fallback plan are addressed efficiently. AI and ML explainability techniques provide the necessary visibility into how these…

Utilize Anomalib from Intel OpenVinoToolkit to benchmark, develop, and deploy deep learning based image anomaly detection — Detecting image anomalies is even more difficult than detecting anomalies in structured datasets or from time series data. It is partly because visual features are more difficult to capture than numerical features in structured datasets. That is where Deep Learning (DL) techniques come in, as deep learning models can perform…

Learn how to apply Grad-CAM using Keras and TensorFlow for explaining deep learning-based image classifiers — Classical machine learning (ML) algorithms are not efficient as compared to deep learning (DL) algorithms when applied to unstructured data such as images and text. Due to the benefit of automatic feature extraction in DL as compared to manual feature engineering in classical ML, DL algorithms are more efficient in…

Learn how to apply the popular explainable AI (XAI) method LIME for explaining image classifiers — Local Interpretable Model-agnostic Explanations (LIME) is one of the most popular Explainable AI (XAI) methods used for explaining the working of machine learning and deep learning models. LIME can provide model-agnostic local explanations for solving both regression and classification problems and it can be applied with both structured datasets and…

Are you still confused about the working of SHAP and Shapley values? Let me provide the most simple and intuitive explanation of SHAP and Shapley values in this article. — SHapley Additive exPlanation (SHAP), which is another popular Explainable AI (XAI) framework that can provide model-agnostic local explainability for tabular, image, and text datasets. SHAP is based on Shapley values, which is a concept popularly used in Game Theory. Although the mathematical understanding of Shapley values can be complicated, I…