📌 Resources:. This video is a part of my course: Modern AI: Applications and Overview. https://courses.computing4all.com/courses/modern-ai-applications-and-overview/. . In this video, we discuss how to build a neural network from scratch using Python and PyTorch in solving a regression problem. 🧠🌰. . What You’ll Learn:. — How to preprocess and visualize the data, including the importance of standardization with StandardScaler.. — Step-by-step coding of a neural network, covering data preparation, model definition, training, and evaluation.. — Understanding all the crucial components of the training process, such as batching, loss computation with L1Loss, and optimization using the Adam optimizer.. — How to evaluate the model using unseen test data and make predictions on new data.. — Insights into the internal parameters (weights and biases) of the trained neural network.. — Practical examples showing how varying the model architecture and hyperparameters can affect performance.. . → Link to the code: (The file is NN_Intro.ipynb). https://github.com/mshossain/NN_Intro. The data file, Pecan.txt, is also available on the GitHub repository. . . 🔗 Related Videos:. Simple Neural Network Construction in 3 Lines of Code:. https://youtu.be/BBgDxvOlFG0. . If you found this video helpful, please give it a thumbs up, subscribe to the channel, and hit the notification bell to stay updated on the latest content!. . . Dr. Shahriar Hossain. https://computing4all.com. . #NeuralNetwork #PyTorch #MachineLearning #DeepLearning #DataScience #AI #Python
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