NN#8 — Neural Networks Decoded (Build your first NN in Python)

Author(s): RSD Studio.ai

Originally published on Towards AI.

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Welcome to the eighth article in our Neural Networks Decoded series! So far, we’ve covered the theoretical foundations of neural networks — from perceptrons to activation functions, backpropagation and evaluation metrics. Today, we’ll bridge theory and practice by building a complete neural network from scratch using Python.

By the end of this tutorial, you’ll have created, trained, validated and deployed a neural network for a real-world classification problem. Let’s take this journey step-by-step, connecting each practical component to the concepts we’ve explored in previous articles.

First, let’s install and import the necessary libraries:

# Basic data manipulation and visualization librariesimport numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns# PyTorch for building our neural networkimport torchimport torch.nn as nnimport torch.optim as optimfrom torch.utils.data import Dataset, DataLoader# Scikit-learn for data preprocessing and evaluationfrom sklearn.preprocessing import StandardScalerfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import confusion_matrix, classification_report, accuracy_score# Set random seeds for reproducibilitynp.random.seed(42)torch.manual_seed(42)# For visualizing the model architecturefrom torchviz import make_dot# Enable interactive plots in Jupyter%matplotlib inlineplt.style.use('fivethirtyeight')

In our setup, we’re using several libraries (You can install it using pip or conda):

NumPy and Pandas: For data manipulation, just as we’d need mathematical operations in our… Read the full blog for free on Medium.

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Published via Towards AI