Machine & Deep Learning

Neural Networks Fundamentals in Practice: The Code and Commands That Really Matter

Neural Networks Fundamentals: The Essentials in One Article — Real Code, Diagrams and Concrete Steps, Excerpts from a 45-Lesson Course.

REHOUMA Haythem

• 9 min read
Neural Networks Fundamentals in Practice: The Code and Commands That Really Matter

No endless theory here: open the terminal and practice. Here's the essentials of Neural Networks Fundamentals, extracted directly from a complete 45-lesson course — with real code you can copy-paste right now.

tl;dr
  • Introduction and Installation
  • The Artificial Neuron
  • From Perceptron to Multilayer Network
  • Learning Loss and Gradient
  • Backpropagation Explained
~$ cat ./parcours.md # Neural Networks Fundamentals — 10 chapters
01
Introduction and Installation
→ Course presentation and why deep learning?→ Install Python, TensorFlow and Keras (or Google Colab)+ 1 more lessons
02
The Artificial Neuron
→ From biological neuron to artificial neuron→ Weights, bias, weighted sum+ 2 more lessons
03
From Perceptron to Multilayer Network
→ MLP architecture: input, hidden, output→ Forward pass: from input vector to prediction+ 2 more lessons
04
Learning Loss and Gradient
→ Cost functions: MSE, Cross-Entropy→ Gradient descent: geometric intuition+ 2 more lessons
05
Backpropagation Explained
→ Chain rule and composite derivatives→ Backprop on a 2-layer network (pen-and-paper)+ 2 more lessons
06
Optimization and Convergence
→ Momentum and Nesterov→ RMSProp, Adam, AdamW+ 2 more lessons
07
Regularization and Generalization
→ Overfitting vs underfitting: diagnosis→ Dropout: intuition and implementation+ 2 more lessons
08
Building Networks with Keras
→ Sequential API: build an MLP in 10 lines→ Functional API for more complex architectures+ 2 more lessons
🏁
Final project (+ 2 chapters along the way)
→ You leave with a concrete and demonstrable project

First "Hello World" network on MNIST

NOTEObjective — Build, train and evaluate your very first neural network in fewer than 20 lines of code, on the MNIST handwritten-digits dataset. You won't understand every detail yet: that's normal, we'll explore them throughout the course.

Learning objectives

TIPBy the end of this module
  • Load the MNIST dataset with Keras
  • Understand image normalization
  • Build a simple Sequential network
  • Train the model and read the accuracy curve
  • Evaluate on the test data and make a prediction

The MNIST dataset

MNIST is the "Hello World" of deep learning. It contains 70 000 grayscale images of handwritten digits (0 to 9), 28×28 pixels each. 60 000 are used for training, 10 000 for testing. The task: predict which digit is written on each image.

Input

A 28×28 image = 784 pixels, each with a value from 0 (black) to 255 (white).

Task

10-class classification: the network must choose a digit from 0 to 9.

Output

A 10-element probability vector; we keep the most probable class.

Step 1: load and prepare the data

Make a prediction

Evaluation, confusion matrix and conclusion

NOTEObjective — Close the project: evaluate the model on the test set, analyze errors with a confusion matrix, compute per-class precision and recall, and draw conclusions from the full journey.

Learning objectives

TIPBy the end of this module
  • Evaluate the model on the test set only once
  • Build and read a confusion matrix
  • Understand per-class precision, recall and F1
  • Identify the most confused classes
  • Conclude and consider improvements

Final evaluation on the test set

The moment of truth: we evaluate the best saved model on the test set, which we never touched during tuning.

Chain rule and composite derivatives

NOTEObjective — Master the chain rule, the only mathematical tool you need to understand backpropagation. See how to differentiate a composite function and why this lets us propagate the gradient through layers.

Learning objectives

TIPBy the end of this module
  • State the chain rule
  • Differentiate a simple composite function
  • Understand the notion of composite function inside a network
  • Visualize the gradient flow from output to input
  • Link the chain rule to backpropagation

The chain rule, in one sentence

When one variable depends on another, which itself depends on a third, their rates of change multiply. That's it. If y depends on u, and u depends on x, then:

Backward

The gradient starts from the loss and flows upward. At each layer we multiply by the local derivative.

WARNINGCaution: To compute the backward pass, you must have kept the intermediate forward values (the z's and activations) in memory. That's why training consumes more memory than plain inference.
go-further

This article covers the most useful excerpts — the full Neural Networks Fundamentals course (11 chapters, 45 lessons, corrected exercises and final project) takes you all the way.

./access-the-full-course free course: Mastering Claude Code

FAQ

How long does it take to learn Neural Networks Fundamentals?
With a structured progression (11 chapters, 45 short practical lessons), you reach an operational level in a few weeks at 30–60 minutes per day. The key is to practice each concept immediately.
Are there any prerequisites?
Basic computer-science knowledge is enough. If you can use a terminal and read simple code, you're ready.
Where to start concretely?
Reproduce the commands in this article, then follow the full Neural Networks Fundamentals course: it chains the 45 lessons in order, with exercises and a final project.
./further-reading
→ Get started with Machine Learning for Beginners: your first concrete step today→ Machine Learning Simplified in practice: the code and commands that really matter→ Python Machine Learning: the 9 key steps to go from zero to operational

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