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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A machine learning primer built from first principles. For engineers who want to reason about ML systems the way they reason about software systems.
| Date | Stars |
|---|---|
| 2026-07-24 | 1185 |
| 2026-07-25 | 1186 |
| 2026-07-28 | 1186 |
| 2026-07-30 | 1186 |
| 2026-07-31 | 1186 |
| 2026-08-06 | 1185 |
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# There Is No Spoon  A machine learning primer built from first principles. Written for engineers who want to reason about ML systems the way they reason about software systems. <br> ## 🎯 Who This Is For You're a strong engineer. You can draw a software system on a whiteboard from your own hard-earned mental model. You understand tradeoffs — maintenance vs elegance, performance vs complexity. You have a gut for software design. **You don't have that gut for machine learning yet.** You know the tools exist but you can't feel when to reach for which. This primer builds that intuition. <br> ## 💡 What Makes This Different This isn't a textbook or a tutorial. It's a **mental model** — the abstractions you need to reason about ML systems the way you already reason about software systems. Every concept is anchored in **physical and engineering analogies**: - Neurons as polarizing filters - Depth as paper folding - Gradient flow as pipeline valves - The chain rule as a gear train - Projections as shadows These analogies aren't decorative — they're the primary explanation, with math as the supporting detail. The focus is **when to reach for which tool and why** — not just what each tool does, but the design decision it represents and the tradeoffs it implies. <br> ## 📐 What It Covers The primer is organized in three parts: <br> 🧱 **Part 1 — Fundamentals** The neuron, composition (depth and width as paper folding), learning as optimization (derivatives, chain rule, backprop), generalization, and representation (features as directions, superposition). 🏗️ **Part 2 — Architectures** The combination rule family (dense, convolution, recurrence, attention, graph ops, SSMs), the transformer in depth (self-attention, FFN as volumetric lookup, residual connections), encoding, learning rules beyond backprop, training frameworks (supervised, self-supervised, RL, GANs, diffusion), and matching topology to problem. 🚦 **Part 3 — Gates as Control Systems** Gate primitives (scalar, vector, matrix), soft logic composition, branching and routing, recursion within a forward pass, and the geometric math toolbox (projection, masking, rotation, interpolation). <br> ## 📖 Read It The primer is a single markdown file with inline visualizations: ### **[ml-primer.md](ml-primer.md)** <br> Jump to a specific topic: | | Topic | What it covers | |---|-------|---------------| | ⚡ | [The Neuron](ml-primer.md#-the-neuron) | Start here — dot product, bias, nonlinearity | | 📄 | [Composition](ml-primer.md#-composition-depth-width-and-paper-folding) | What depth buys you — the paper folding model | | 📉 | [Learning](ml-primer.md#-learning-as-optimization) | Derivatives, chain rule, backprop, loss landscape | | 🎯 | [Generalization](ml-primer.md#-generalization) | Why overparameterized networks work at all | | 🧠 | [Representation](ml-primer.md#-representation-what-networks-actually-store) | Features as directions, superposition | | 🔀 | [Combination Rules](ml-primer.md#-the-combination-rule-family) | Convolution vs attention vs recurrence vs graph vs SSM | | 🤖 | [The Transformer](ml-primer.md#-the-transformer) | Self-attention, FFN, residual connections | | 🏋️ | [Frameworks](ml-primer.md#%EF%B8%8F-frameworks) | Supervised, self-supervised, RL, GANs, diffusion | | 🗺️ | [Topology](ml-primer.md#%EF%B8%8F-topology-for-the-problem) | Matching architecture to problem — worked examples | | 🧩 | [Design Patterns](ml-primer.md#-design-patterns) | Common problems → which tool to reach for | | 🚦 | [Gates](ml-primer.md#-gates-as-control-systems) | The practitioner's gating and control toolkit | | 🔧 | [Diagnostics](ml-primer.md#-appendix-diagnosing-and-fixing-training-problems) | Loss curve symptoms, sanity checks, LR tuning | <br> The syllabus shows the full topic map: **[SYLLABUS.md](SYLLABUS.md)** <br> ## 🧭 How to Use This This primer was built through conversation — one concept at a time,
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matched fp:a3fab61b6467106f, topic:tutorial, readme:tutorial
matched fp:a3fab61b6467106f, topic:deep-learning