How to Train Your First TensorFlow Model in PyCharm | The PyCharm Blog

This is a guest post from Iulia Feroli, founder of the Back To Engineering community on YouTube. TensorFlow is a powerful open-source framework for building machine learning and deep learning systems. At its core, it works with tensors (a.k.a multi‑dimensional arrays) and provides high‑level libraries (like Keras) that make it easy to transform raw data into models you can train, evaluate, and deploy. TensorFlow helps you handle the full pipeline: loading and preprocessing data, assembling models from layers and activations, training with optimizers and loss functions, and exporting for serving or even running on edge devices (including lightweight TensorFlow Lite models on Raspberry Pi and other microcontrollers).  If you want to make data-driven applications, prototyping neural networks, or ship models to production or devices, learning TensorFlow gives you a consistent, well-supported toolkit to go from idea to deployment. If you’re brand new to TensorFlow, start by watching th