Week 6 – Hands-On with PyTorch

This week focused on PyTorch and how to actually build and train machine learning models. I learned that PyTorch is a powerful and flexible library that allows developers to create neural networks using tensors, which are essentially multi-dimensional arrays. One thing that stood out to me is how beginner-friendly PyTorch feels compared to other tools, especially because of its clear structure and ability to debug easily. In Google Colab, I followed along with building a neural network that predicts housing prices using the Boston Housing dataset. This process helped me understand the full workflow of training a model, including splitting the data, running multiple epochs, calculating loss, and improving predictions over time. Learning about concepts like Mean Squared Error (MSE) and Stochastic Gradient Descent (SGD) gave me a better understanding of how models actually improve. At first, the terminology was a little overwhelming, but breaking it down step-by-step made it much more manageable. What really clicked for me this week is how these models can be turned into real tools. For example, a trained model could be placed into a web app where users input data and receive predictions instantly. This made me realize the business potential behind AI, especially in areas like real estate, marketing analytics, and forecasting. Instead of just learning theory, I feel like I’m starting to understand how to build something that could actually be used in the real world.

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