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In the last blog “How to use TensorFlow with GPU on Windows for minimal tasks — in the most simple way(2024)” I discussed how to use Microsoft TensorFlow-DirectML Plugin to make GPU available to TensorFlow(TF) in Windows, it is good for lightweight programming tasks that requiring GPU, but not good for programs that take too much graphics memory and requires heavy loading(as these frameworks need drivers and applications like CUDA and cuDNN to allocate memory for processing the data and task). So in this blog, we are going to deal with downloading and installing the correct versions of TensorFlow, CUDA, cuDNN, Visual Studio Integration, and other driver files to make GPU accessible to TensorFlow, for doing heavy tasks like Video Processing, High-Resolution Image Processing, and Deep Learning*.

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Python’s Virtual Environment(venv) is a self-contained directory tree that contains a Python installation for a particular version of Python, with several additional packages.

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Accelerating machine learning code using your system’s GPU will make the code run much faster and save a lot of time. In this blog we are going to do a quick setup to make TensorFlow access our PC/Laptop’s GPU, just like installing 3–4 libraries in python using pip.

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In the dynamic landscape of technology, innovation becomes a beacon that guides creators to captivate audiences with unique and engaging projects. For me, this journey began with a challenge from my college’s technical fest, Asthra 8.0. The head of the department tasked me with crafting something special, something that would not only capture attention but also evoke joy. Thus, the inception of Emotion2Emoji — a lightweight system that predicts and displays emojis from facial emotions in a live webcam feed.

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The journey of dual-booting Windows and Ubuntu (or any other OS) on a high-end laptop can be a bit tricky. But still, it can be an exciting endeavor — challenges and problem-solving. This article presents a comprehensive guide for addressing two common issues users face while dual-booting high-end laptops: setting the storage configuration and resolving sound output problems.

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How to work faster in jupyter notebooks — the answer is Keyboard shortcuts.

Keyboard shortcuts are a great way to do digital work faster, when it comes to programming it’s more relevant. With this article, I am sharing some of my daily keyboard shortcuts while using jupyter notebook.