Dinesh’sLearning Lab
AN INDEPENDENT LEARNING LAB BY DINESH MALUCHURU

Don’t just know it.
Understand it.

From the first principle to the working system.
A field guide for people who learn by taking things apart.

Find your learning path Explore the library
THEORY ↔ PRACTICE398 published lessons21 learning pathsAlways a work in progress

01 / The learning map

Follow a thread.
See the connections.

Many subjects. Meaningful depth.
Connected paths to come back to.

01

Deep Learning and LLMs: an inspectable learning laboratory

67 lessons

A broad deep-learning and LLM progression with a verified NumPy spine, advanced framework and systems lessons, source evidence and explicit execution boundaries.

Arrays, shapes, and the first linear modelVectors, Matrices, and the Geometry of Data
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02

Algorithm practice · From recognition to a defensible solution

66 lessons

Move from Python idioms and window invariants to trees, graphs and dynamic-programming families. Compare variants by their state, transition and proof—not by memorizing one template.

The LeetCode Game — How to Read a ProblemBig-O in Practice — Counting, Not Vibes
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03

Distributed systems and reliability

24 lessons

Reason about services when time, networks and machines fail. Progress through consistency, replication, partitioning, coordination and recovery with explicit failure models.

HTTP Fundamentals — Verbs, Status Codes, Headers, CachingREST API Design — Resources, Versioning, Idempotency
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04

Data Platform & Orchestration

23 lessons

Trace data from relational queries and storage through batch processing, streams, orchestration and lakehouse maintenance. Focus on grain, state, late data and repeatable recovery.

The Relational Model — Tables, Keys, NormalisationSQL Basics — SELECT, WHERE, ORDER BY, LIMIT
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05

From Novice to Fluent on the Modern Microsoft Web Stack — 22-Chapter Self-Study Plan

22 lessons

Build an end-to-end mental model of the Microsoft web stack: C# and .NET, HTTP and ASP.NET, TypeScript and React, then identity, telemetry, resilience and delivery.

MS Stack Ch 1 — Developer toolingMS Stack Ch 2 — Web fundamentals
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06

Cloud infrastructure and delivery

10 lessons

Connect containers, Kubernetes, cloud services and infrastructure-as-code to operational decisions. Compare resource identity, networking, persistent state and rollout boundaries rather than copying commands blindly.

Docker — Images, Layers, Dockerfile, NetworkingKubernetes I — Pods, Deployments, Services
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See all paths and your progress

On the workbench / Interactive 01

Make a mistake.
Then descend.

A model learns by changing its parameters. Move the weight, choose a learning rate, and watch one gradient step change the error.

L(w) = (w − 2)²
w′ = w − η · 2(w − 2)

A scalar quadratic teaching model, not a neural network. Its exact minimum is w = 2. No data leaves your browser.

LOSS LANDSCAPE01 / OPTIMIZATION
Quadratic loss and gradient descent trajectoryLoss equals weight minus two squared. Minimum at weight two. Current weight -1.500, loss 12.250. The current point is shown on the curve.0510152025-3-1025LOSSWEIGHT wminimum
Weight-1.500
Loss12.250
Gradient-7.000

Next update: -1.500 − 0.20 × (-7.000) = -0.100. The step moves toward the minimum.

Keyboard: Tab to a slider, then use arrow keys. Tab to the step button and press Enter. 0 steps taken.

A note on the method

Less collecting.
More connecting.

The goal isn’t to finish the internet. It’s to build a mental model you can use when the tutorial ends.

Pick one lesson. Work through the example. Change an assumption. Keep the ideas that survive.

Begin with the first lesson