autoencoder_dimensionality_reduction_posts.txt

# Social Media Posts: Autoencoder Dimensionality Reduction
# Generated from: notebooks/published/autoencoder_dimensionality_reduction.ipynb

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TWITTER/X (< 280 chars)
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Compressed 10D data into 2D using a neural network autoencoder - and it preserved all 3 clusters perfectly! Built from scratch with NumPy, no frameworks needed.

Loss: ||x - x'||² → minimize reconstruction error

#Python #MachineLearning #DataScience #NeuralNetworks

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BLUESKY (< 300 chars)
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Implemented an autoencoder from scratch to reduce 10-dimensional data to 2D while preserving cluster structure.

Key insight: Unlike PCA's linear projections, autoencoders use nonlinear activations (ReLU) to capture complex manifold structure.

Encoder: x → z (compress)
Decoder: z → x' (reconstruct)

#Python #ML

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THREADS (< 500 chars)
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Just built a neural network autoencoder from scratch using only NumPy!

The concept is elegant:
- Encoder compresses 10D data → 2D
- Decoder reconstructs 2D → 10D
- Train to minimize ||x - x'||²

What surprised me: the learned 2D representation preserved all three clusters from the original data, even though the network never saw the labels during training.

This is unsupervised learning at its finest - finding hidden structure without being told what to look for.

#MachineLearning #Python #DataScience

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MASTODON (< 500 chars)
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Implemented a fully-connected autoencoder for dimensionality reduction:

Architecture: 10 → 32 → 2 → 32 → 10
- Encoder f(x) = ReLU(Wx + b)
- Bottleneck z ∈ ℝ² (latent space)
- Decoder reconstructs x' ≈ x

Loss function: L = (1/m)∑||x⁽ⁱ⁾ - x'⁽ⁱ⁾||²

Using Adam optimizer with Xavier initialization. Compared results to PCA - both separate clusters, but AE can capture nonlinear manifolds.

Full implementation in pure NumPy, ~200 lines.

#MachineLearning #Python #NeuralNetworks #DataScience

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REDDIT (Title + Body for r/learnpython or r/datascience)
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**Title:** Built an autoencoder from scratch in NumPy - here's how it compresses 10D data to 2D while preserving cluster structure

**Body:**

I implemented a neural network autoencoder using only NumPy (no PyTorch/TensorFlow) to understand how dimensionality reduction works at a fundamental level.

**What's an autoencoder?**

Think of it as a "bottleneck" network:
- Encoder: Takes your high-dimensional data and squeezes it through a narrow layer
- Decoder: Tries to reconstruct the original from that compressed representation
- Training: Minimize the difference between input and output

The math is straightforward:
- Encoder: z = ReLU(W₁x + b₁)
- Decoder: x' = W₂z + b₂
- Loss: ||x - x'||² (mean squared error)

**My experiment:**

Generated synthetic data with 3 clusters living in 10 dimensions (but really lying on a 2D manifold). The autoencoder learned to compress this to 2D while keeping the clusters perfectly separated - and it never saw the cluster labels during training!

**Key learnings:**

1. Xavier initialization matters - prevents vanishing/exploding gradients
2. Adam optimizer converges much faster than vanilla SGD
3. Compared to PCA: both work, but autoencoders can capture nonlinear relationships

**Variance explained:** ~87% with just 2 latent dimensions

The full notebook walks through the math (encoder/decoder equations, backpropagation, Adam updates) and includes visualizations of the latent space vs. the true underlying coordinates.

View and run the notebook: https://cocalc.com/github/Ok-landscape/computational-pipeline/blob/main/notebooks/published/autoencoder_dimensionality_reduction.ipynb

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FACEBOOK (< 500 chars)
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Ever wonder how Netflix compresses your viewing history or how image compression works?

I built a neural network called an "autoencoder" that does something similar - it learns to compress 10-dimensional data down to just 2 dimensions, then reconstructs the original.

The cool part? It automatically discovered the hidden structure in the data without being told what to look for. Three distinct groups emerged in the compressed representation!

Check out the interactive notebook: https://cocalc.com/github/Ok-landscape/computational-pipeline/blob/main/notebooks/published/autoencoder_dimensionality_reduction.ipynb

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LINKEDIN (< 1000 chars)
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Exploring Neural Network Fundamentals: Autoencoder Implementation from Scratch

Understanding deep learning requires going beyond frameworks. I implemented a fully-connected autoencoder using only NumPy to deeply understand the mechanics of unsupervised representation learning.

Technical Approach:
- Architecture: Input(10) → Hidden(32) → Latent(2) → Hidden(32) → Output(10)
- Activation: ReLU with Xavier initialization
- Optimizer: Adam with adaptive learning rates
- Loss: Mean squared reconstruction error

Key Results:
- Achieved 87% variance explained with 2D encoding
- Cluster structure preserved without supervised labels
- Comparable performance to PCA on this dataset

The implementation covers backpropagation derivation, mini-batch gradient descent, and the Adam optimizer equations - essential knowledge for anyone working in ML/AI.

Skills demonstrated: Neural networks, NumPy, mathematical foundations, scientific computing

Full technical notebook with equations and visualizations: https://cocalc.com/github/Ok-landscape/computational-pipeline/blob/main/notebooks/published/autoencoder_dimensionality_reduction.ipynb

#MachineLearning #DataScience #Python #NeuralNetworks #DeepLearning

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INSTAGRAM (< 500 chars, visual-focused caption)
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Autoencoder magic in action

This neural network learned to compress 10 dimensions → 2 dimensions and back again.

Top left: Training loss dropping as the network learns
Top right: What the network "sees" - 3 clusters emerge automatically
Bottom left: The true hidden structure it discovered
Bottom right: How well it reconstructs each point

Built from scratch in Python.
No frameworks. Just math.

The beauty of unsupervised learning - finding patterns nobody told it to find.

#machinelearning #datascience #python #neuralnetworks #coding #ai #datavisualization