In this article, we will talk about a blessing like TensorBoard, which I use very often with you. Easily host, track, and share your ML experiments for free
Easily host, track, and share your ML experiments for free
Hello everyone, we continue the pleasant journey of artificial intelligence. In this article, we will talk about a blessing like TensorBoard, which I use very often with you. It is very important in terms of its intelligibility that you at least know about TensorFlow before reading my article💡
In the deep learning projects we work with, we often want to learn about accuracy metrics. In previous times, this situation was based on a little more manpower, while now it is very practical thanks to TensorBoard. TensorBoard refers to when talking about yourself:
It provides a managed TensorBoard experience that lets you upload and share your machine learning experiment results with everyone.
First, let’s get some information, and then we can quickly realize our experiences in Colab.
For a project I did earlier, we will install TensorBoard step by step to visually see the accuracy metrics. But for this, you must first have a colab membership. I also did not define a membership for this. Instead, by connecting via Drive, I had the opportunity to use my files there and practice quickly.
This article will simply explain the concept which will help you understand the difference between Machine Learning and Deep Learning.
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