Abstract: Jupyter notebooks have become central in data science, integrating code, text and output in a flexible environment. With the rise of machine learning (ML), notebooks are increasingly used ...

Already using NumPy, Pandas, and Scikit-learn? Here are seven more powerful data wrangling tools that deserve a place in your ...

Overview:  PyTorch is ideal for experimentation, TensorFlow and Keras excel at large-scale deployment, and JAX offers ...

While some of us may have learned C in order to interact with embedded electronics or deep with computing hardware of some ...

IBM Watson is a pretty big name in the AI world, and for good reason. It’s not just one tool, but more like a whole suite of ...

Overview: NumPy is ideal for data analysis, scientific computing, and basic ML tasks.PyTorch excels in deep learning, GPU ...

Learn how to build a digit recognition model from scratch using PyTorch! This beginner-friendly deep learning project walks you through loading the MNIST dataset, creating a neural network, training ...

Abstract: Simultaneous Localization and Mapping (SLAM) has become a critical technology for intelligent transportation systems and autonomous robots and is widely used in autonomous driving. However, ...

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

Introduction: The COVID-19 pandemic accelerated global online education, which faces “shallow learning” challenges. Deep learning is key to student competencies. Based on sociocultural theory, this ...

For the second year in a row, the Knicks project to field one of the NBA’s most dangerous three-point shooting rosters. And in Year 1 of the Mike Brown era, New York is expected to lean even harder ...