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15
Tutorials Published
1000+
Active Learners
8-12
Min per Lesson

Published Tutorials

15 tutorials covering neural networks to the complete training loop. Start anywhere.

# Tutorial Title Time to Read
1 Understanding Neural Networks: The tech behind today's AI
Understanding Neural Networks: The tech behind today's AI Issue #34 • Nov 10
8-12 min
2 Inside a Neural Network: Neurons, Weights, and Biases Explained
Inside a Neural Network: Neurons, Weights, and Biases Explained Issue #35 • Nov 20
8-12 min
3 How Neural Networks Learn (Forward & Backward Propagation)
How Neural Networks Learn (Forward & Backward Propagation) Issue #36 • Nov 27
8-12 min
4 Activation Functions: Why Neural Networks Need Them
Activation Functions: Why Neural Networks Need Them Issue #37 • Dec 04
8-12 min
5 Loss Functions - How Neural Networks Measure Their Mistakes
Loss Functions - How Neural Networks Measure Their Mistakes Issue #38 • Dec 11
8-12 min
6 How Neural Networks Actually Learn (Gradient Descent)
How Neural Networks Actually Learn (Gradient Descent) Issue #39 • Dec 19
8-12 min
7 Training Neural Networks: The Complete Learning Loop
Training Neural Networks: The Complete Learning Loop Issue #40 • Dec 25
8-12 min
8 What Are Tensors? (And Why Modern AI Needs Them)
What Are Tensors? (And Why Modern AI Needs Them) Issue #41 • Jan 01
8-12 min
9 Convolutional Neural Networks: How AI Sees Images
Convolutional Neural Networks: How AI Sees Images Issue #43 • Jan 05
8-12 min
10 Recurrent Neural Networks: Processing Sequences and Time
Recurrent Neural Networks: Processing Sequences and Time Issue #44 • Jan 15
8-12 min
11 Attention Mechanisms: Teaching Neural Networks Where to Look
Attention Mechanisms: Teaching Neural Networks Where to Look Issue #45 • Jan 22
8-12 min
12 Transformers: The Architecture That Changed Everything
Transformers: The Architecture That Changed Everything Issue #46 • Jan 29
8-12 min
13 Embeddings & Vector Spaces: How AI Understands Meaning
Embeddings & Vector Spaces: How AI Understands Meaning Issue #47 • Feb 05
8-12 min
14 What is RAG? Building Production AI Without Training Models
What is RAG? Building Production AI Without Training Models Issue #48 • Feb 12
8-12 min
15 Three Ways to Customize LLMs: Prompting, RAG, and Fine-Tuning
Three Ways to Customize LLMs: Prompting, RAG, and Fine-Tuning Issue #49 • Feb 19
8-12 min

📬 New tutorial every Thursday • Next up: CNNs, Transformers, Attention Mechanisms, and RAG

Preview • Issue #1

Understanding Neural Networks: The Tech Behind Today's AI

If you've used ChatGPT, DALL-E, or any modern AI tool, you've interacted with neural networks. But what are they really?

At their core, neural networks are inspired by how our brains work—networks of interconnected neurons that fire together to process information. In AI, we've recreated this concept in code, building mathematical models that can learn patterns from data.

Here's the breakthrough: Instead of programming explicit rules (like "if email contains 'discount', mark as spam"), neural networks learn these rules themselves by looking at thousands of examples. Show a neural network 10,000 spam emails and 10,000 legitimate emails, and it figures out the patterns on its own.

This is why neural networks power everything from your email spam filter to ChatGPT's responses.

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