backpropagation_from_scratch_posts.txt

# Social Media Posts: Backpropagation from Scratch

## SHORT-FORM POSTS

### Twitter/X (< 280 chars)

Built a neural network from scratch to learn spiral classification. The magic of backpropagation: propagate errors backward using the chain rule to update weights. Final accuracy: 100%!

#Python #MachineLearning #NeuralNetworks #DeepLearning

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### Bluesky (< 300 chars)

Implemented backpropagation from scratch in Python. The algorithm uses the chain rule to compute gradients: δ⁽ˡ⁾ = (W⁽ˡ⁺¹⁾)ᵀ δ⁽ˡ⁺¹⁾ ⊙ σ'(z⁽ˡ⁾). Trained on a spiral dataset and achieved perfect classification. Code includes full forward/backward pass implementation.

#NeuralNetworks #Python

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### Threads (< 500 chars)

Ever wondered how neural networks actually learn?

I built one from scratch to demystify backpropagation. Here's the core insight:

1. Forward pass: z = Wa + b, then apply activation
2. Compute output error: δ = prediction - truth
3. Backpropagate: multiply by weights, element-wise with derivative
4. Update: W = W - η∇L

Trained it on a spiral dataset (the hardest 2D classification problem). The network learned a beautiful nonlinear decision boundary. From 50% to 100% accuracy!

#LearnML #Python

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### Mastodon (< 500 chars)

Implemented backpropagation from scratch in NumPy. Architecture: 2→16→8→1 with tanh activation.

Key equations:
- Forward: z⁽ˡ⁾ = W⁽ˡ⁾a⁽ˡ⁻¹⁾ + b⁽ˡ⁾
- Error: δ⁽ˡ⁾ = (Wᵀδ) ⊙ σ'(z)
- Gradient: ∂L/∂W = (1/m)δaᵀ
- Update: W ← W - η∇L

Used binary cross-entropy loss: L = -(1/m)∑[y·log(ŷ) + (1-y)·log(1-ŷ)]

Spiral dataset achieved 100% accuracy. Visualization shows elegant decision boundary.

#Python #MachineLearning #NeuralNetworks

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## LONG-FORM POSTS

### Reddit (r/learnpython or r/MachineLearning)

**Title:** I built a neural network from scratch to understand backpropagation - here's how it works

**Body:**

I always found backpropagation confusing until I implemented it myself. Here's an ELI5 breakdown of what's actually happening:

**The Goal:** Find how much each weight contributes to the error so we can adjust it.

**The Problem:** In a multi-layer network, changing one weight affects everything downstream. How do we trace that influence?

**The Solution - Chain Rule:** Backpropagation breaks this into steps:

1. Compute output error (easy: prediction minus truth)
2. For each earlier layer, ask: "How much did you contribute to the next layer's error?"
3. Multiply: (next layer's weights)ᵀ × (next layer's error) × (your activation derivative)

**The Math (in plain terms):**

- Forward pass: z = Wa + b, then a = activation(z)
- Backward pass: δ⁽ˡ⁾ = (Wᵀδ⁽ˡ⁺¹⁾) ⊙ σ'(z⁽ˡ⁾)
- Gradients: ∂L/∂W = δ × aᵀ (outer product)
- Update: W = W - learning_rate × gradient

**What I learned:**

- Weight initialization matters (He for ReLU, Xavier for tanh/sigmoid)
- Learning rate too high = divergence, too low = slow training
- Deeper networks = smaller gradients in early layers (vanishing gradient problem)

The notebook trains on a spiral dataset - one of the hardest 2D classification problems because it requires nonlinear boundaries. The network goes from random guessing to 100% accuracy.

**View the full notebook with code:** https://cocalc.com/github/Ok-landscape/computational-pipeline/blob/main/notebooks/published/backpropagation_from_scratch.ipynb

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### Facebook (< 500 chars)

Ever wondered how AI learns? I built a neural network from scratch to find out!

The secret is backpropagation - an algorithm that figures out how to adjust each connection in the network to reduce errors. It's like a teacher grading a test and telling each student exactly what they got wrong and by how much.

My network learned to separate two intertwined spirals - something a simple line can't do. Watch it go from random guessing to perfect classification!

Check it out: https://cocalc.com/github/Ok-landscape/computational-pipeline/blob/main/notebooks/published/backpropagation_from_scratch.ipynb

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### LinkedIn (< 1000 chars)

**Understanding Neural Networks: A From-Scratch Implementation of Backpropagation**

To deepen my understanding of deep learning fundamentals, I implemented a complete neural network from scratch using only NumPy. This exercise revealed the elegant mathematics behind the seemingly "magical" learning process.

**Technical Implementation:**
- Architecture: Fully-connected feedforward network (2→16→8→1)
- Activation: tanh for hidden layers, sigmoid for output
- Loss: Binary cross-entropy
- Optimization: Gradient descent with analytical gradients

**Key Mathematical Components:**
- Forward propagation: z⁽ˡ⁾ = W⁽ˡ⁾a⁽ˡ⁻¹⁾ + b⁽ˡ⁾
- Error backpropagation via chain rule
- He/Xavier weight initialization for stable training

**Results:**
The network successfully learned to classify a nonlinear spiral dataset, demonstrating the power of multi-layer architectures for complex decision boundaries.

**Skills Demonstrated:** NumPy, mathematical modeling, algorithm implementation, data visualization, gradient-based optimization

Full implementation with visualizations: https://cocalc.com/github/Ok-landscape/computational-pipeline/blob/main/notebooks/published/backpropagation_from_scratch.ipynb

#MachineLearning #DeepLearning #Python #DataScience #AI

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### Instagram (< 500 chars)

Teaching a neural network to see patterns

Built this from scratch to understand how AI actually learns. The spiral dataset is deceptively hard - you can't separate these classes with a straight line.

The network learns through backpropagation:
→ Make a prediction
→ Measure the error
→ Trace blame backward through layers
→ Adjust weights to reduce error
→ Repeat 1000x

Result: A beautiful curved decision boundary that perfectly separates the spirals.

From 50% (random) to 100% accuracy.

#MachineLearning #Python #DataScience #NeuralNetworks #AI #Coding #DeepLearning #DataVisualization