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How Amazon SageMaker is revolutionizing machine learning workflow
Amazon SageMaker streamlines the ML lifecycle by automating workflows, reducing costs, and enhancing scalability. It integrates with AWS services to simplify model development, deployment, and monitoring.
Haris Riaz
Mar 19 · 2025
Top 20 Google DeepMind interview questions
Ace your Google DeepMind interview! Get insights on key stages, top AI/ML questions, and expert prep tips to land your dream role in cutting-edge AI research.
Adeel Qayyum
Feb 13 · 2025
How Does Reinforcement Learning Work
Reinforcement Learning (RL) is a type of machine learning in which an agent learns by interacting with an environment and receiving feedback in the form of rewards or penalties. In this article, we explain how RL works, using the example of the CartPole problem, where the agent learns to balance a pole. We also highlight real-world applications of RL to show its practical use in solving complex problems.
Hamna Waseem
Feb 10 · 2025
Google layoffs: Everything developers need to know
Google’s recent layoffs indicate where the industry is headed: AI, cloud infrastructure, and automation. For developers globally, these layoffs are more than just buzzwords: they’re a reality check for what it will take to get hired, and succeed, at Google. This blog will explore what's next for developers who are setting their sights on the company.
Zarish Khalid
Jan 15 · 2025
Linear Regression vs. Logistic Regression
Understand the key differences between the linear regression and the logistic regression. Understand how the logistic regression model works and look at some of the applications of logistic regression in machine learning.
Khawaja Muhammad Fahd
Jan 10 · 2025
Making sense of Kolmogorov-Arnold Networks (KANs)
This blog explores Kolmogorov-Arnold Networks (KANs), an innovative neural network architecture that resembles traditional fully-connected neural networks but replaces weights and node-based activation functions with edge-based activation functions. We examine the learnability of these functions, compare KANs with traditional neural networks based on early experimental findings, and investigate their potential for greater interpretability and continual learning.
Mehvish Poshni
Jan 6 · 2025
How to use convolutional neural networks (CNNs) for images
Convolutional Neural Networks (CNNs) power groundbreaking innovations like facial recognition, self-driving cars, and medical imaging. This blog breaks down how CNNs work, exploring their core layers—convolutional layers, pooling layers, and fully connected layers— and explaining their training process with backpropagation, making the concepts accessible even to machine learning beginners. You’ll also explore a hands-on example of building a simple CNN with TensorFlow and Keras.
Hamna Waseem
Jan 1 · 2025
A guide to anomaly detection in health care with machine learning
Explore the role of machine learning in revolutionizing healthcare by detecting anomalies in vital signs, sensor data, and medical imaging. This guide covers supervised, unsupervised, and semi-supervised techniques, tailored for structured, unstructured, real-time, and imbalanced datasets. With hands-on examples, learn to build models for detecting patient falls or heart arrhythmias using tools like scikit-learn, TensorFlow, and Keras, enabling timely life-saving interventions.
Hamna Waseem
Dec 10 · 2024
Machine learning interview preparation tips
Machine learning (ML) is a crucial part of every large company's operations across various industries, and its ability to efficiently solve complex problems has made it a sought-after technology globally. Specialists in this domain are in demand now more than ever, and preparing for a machine learning interview can become daunting. In this blog, we will explore all the areas you must cover during your interview preparation.
Zarish Khalid
Sep 5 · 2024