At version r1.5, Google's open source machine learning and neural network library is more capable, more mature, and easier to learn and use If you looked at TensorFlow as a deep learning framework ...

Overview NumPy and Pandas form the core of data science workflows. Matplotlib and Seaborn allow users to turn raw data into ...

At the start of May, I decided to get TensorFlow Developer Certified. So I set myself up with a curriculum to sharpen my skills and took the certification — turns out, I passed. Let me tell you how I ...

Getting computers to recognize objects has been a historically difficult problem in computer science, but with the rise of machine learning it is becoming easier to solve. One of the tools that can be ...

Besides putting a Raspberry Pi to work on a mini Mars rover, it's now going to be a lot easier to use Google's TensorFlow artificial-intelligence framework with the low-powered computer. Developers ...

Overview: The choice of deep learning frameworks increasingly reflects how AI projects are built, from experimentation to ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Digital sovereignty is about maintaining ...

Data science is often cited as one of the main reasons for Python's growing popularity. But while people are definitely using Python for data analysis and machine learning, not many of those using ...

Despite some of the inherent complexities of using FPGAs for implementing deep neural networks, there is a strong efficiency case for using reprogrammable devices for both training and inference.