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· dineshblog · a working notebook

Notes on AI, ML, data & the engineering behind them.

Every session is written for the reader who's smart and curious but short on time. Zero background assumed, diagram first, then the code. A six-month plan across 130 sessions and 16 modules — plus 31+ long-form essays.

Start at Session 1See the full plan

· start here · M00

Dev Environment — Linux/WSL, Terminal, VS Code

Baseline setup so you never hit an environment wall.

Session 001read

· jump to LLMs · M09

The 3 Pillars — Metrics, Logs, Traces

The senses of a running system.

Session 077read

The 130 sessions

grouped by module →

A calendar walk — one session per day, alternating across the 13 modules so you never sit inside a single topic for a week. Prefer to binge one module at a time? See the module DAG.

  1. Day 1M00Dev Environment — Linux/WSL, Terminal, VS Code
  2. Day 2M01Python Variables & Types — Mental Model of Memory
  3. Day 3M02Big-O Notation — Reasoning About Scale
  4. Day 4M03Arrays & Strings — Indexing, Slicing, Two-Pointer
  5. Day 5M04The Relational Model — Tables, Keys, Normalisation
  6. Day 6M05Data Modelling — Dimensional, Data Vault, OBT
  7. Day 7M06HTTP Fundamentals — Verbs, Status Codes, Headers, Caching
  8. Day 8M07OS Basics — Processes, Threads, Memory, FDs
  9. Day 9M08CAP & PACELC — the Actual Trade-Offs
  10. Day 10M09The 3 Pillars — Metrics, Logs, Traces
  11. Day 11M10AuthN vs AuthZ, Sessions & Password Storage
  12. Day 12M11The ML Mental Model — Features, Labels, Train/Val/Test
  13. Day 13M12Perceptron & Activation Functions
  14. Day 14M00Git & GitHub — Commits, Branches, PRs
  15. Day 15M01Control Flow — if/else, loops, comprehensions
  16. Day 16M02Discrete Math — Sets, Logic, Combinatorics, Graphs
  17. Day 17M03Hashmaps & Sets — Hash Functions, Collisions
  18. Day 18M04SQL Basics — SELECT, WHERE, ORDER BY, LIMIT
  19. Day 19M05Batch vs Streaming — Mental Model & Use Cases
  20. Day 20M06REST API Design — Resources, Versioning, Idempotency
  21. Day 21M07Networking I — TCP/IP, DNS, Sockets
  22. Day 22M08Replication — Leader/Follower, Multi-Leader, Leaderless
  23. Day 23M09Prometheus, Grafana, OpenTelemetry — Hands-on
  24. Day 24M10TLS 1.3, PKI & Cert Lifecycle
  25. Day 25M11Linear Regression from Scratch (numpy)
  26. Day 26M12Multi-Layer Perceptron — Forward Pass
  27. Day 27M00The Command Line — bash, pipes, grep, jq
  28. Day 28M01Functions — arguments, scope, closures
  29. Day 29M02Linear Algebra I — Vectors, Dot Product, Geometry
  30. Day 30M03Linked Lists — Singly, Doubly, When They Win
  31. Day 31M04Joins — INNER, LEFT, RIGHT, FULL, Anti-Join
  32. Day 32M05Spark — RDD, DataFrame, Jobs/Stages/Shuffles
  33. Day 33M06GraphQL — Schema, Resolvers, N+1, When to Pick It
  34. Day 34M07Networking II — Load Balancers L4 vs L7, Reverse Proxies
  35. Day 35M08Consistency Models — Linearizable, Sequential, Eventual
  36. Day 36M09SLIs, SLOs & Error Budgets — the SRE Math
  37. Day 37M10OWASP Top 10, Secrets Mgmt & Threat Modelling
  38. Day 38M11Logistic Regression — Sigmoid, Cross-Entropy, from Scratch
  39. Day 39M12Backpropagation — Derived by Hand on a 2-Layer Net
  40. Day 40M00Reading Docs & Effective Googling — the Meta-Skill
  41. Day 41M01Data Structures — list, tuple, dict, set (when to use what)
  42. Day 42M02Linear Algebra II — Matrices, Transforms, Eigenvalues
  43. Day 43M03Stacks & Queues — LIFO/FIFO in Practice
  44. Day 44M04Aggregations — GROUP BY, HAVING, Subqueries
  45. Day 45M05Kafka — Topics, Partitions, Consumer Groups
  46. Day 46M06gRPC & Protobuf — When RPC Wins
  47. Day 47M07Caching — Cache-Aside, Write-Through, TTLs, Invalidation
  48. Day 48M08Consensus — Paxos & Raft Intuition
  49. Day 49M09Incident Response — Runbooks, Postmortems, On-Call
  50. Day 50M11Regularization — L1, L2, Elastic Net
  51. Day 51M12Optimizers — SGD, Momentum, Adam, RMSprop
  52. Day 52M01Classes & Objects — the OOP Mental Model
  53. Day 53M02Calculus I — Derivatives & Chain Rule
  54. Day 54M03Recursion — Call Stack, Base Case, Worked Examples
  55. Day 55M04Window Functions — the Game-Changer
  56. Day 56M05Stream Processing — Watermarks, Windows, Exactly-Once
  57. Day 57M06AuthN & AuthZ — OAuth 2.0, OIDC, JWT
  58. Day 58M07CDN — Edge, Cache Hierarchies, Cache-Control
  59. Day 59M08Sharding & Partitioning Strategies
  60. Day 60M11Bias–Variance Trade-off & Learning Curves
  61. Day 61M12PyTorch Fundamentals — Tensors, Autograd, nn.Module
  62. Day 62M01Inheritance, Composition & Polymorphism
  63. Day 63M02Calculus II — Gradients & Gradient Descent from Scratch
  64. Day 64M03Trees & BSTs — Traversal (BFS/DFS)
  65. Day 65M04CTEs & Recursive Queries
  66. Day 66M05Orchestration — Airflow, DAGs, Retries, Backfills
  67. Day 67M07Docker — Images, Layers, Dockerfile, Networking
  68. Day 68M08Message Queues — SQS, RabbitMQ, Kafka as Queue
  69. Day 69M11Decision Trees — Gini, Entropy, Splits
  70. Day 70M12Regularization in DL — Dropout, BatchNorm, Weight Decay
  71. Day 71M01Errors, Exceptions & Debugging with pdb
  72. Day 72M02Probability — Random Variables, Distributions, Expectation
  73. Day 73M03Heaps & Priority Queues
  74. Day 74M04Indexes — B-Tree Intuition, When to Add
  75. Day 75M05dbt — Models, Tests, Docs, Warehouse-Native ELT
  76. Day 76M07Kubernetes I — Pods, Deployments, Services
  77. Day 77M08Multi-Region — Active-Passive, Active-Active, Failover
  78. Day 78M11Random Forest & Bagging
  79. Day 79M12CNNs — Convolution, Pooling, ImageNet Architectures
  80. Day 80M01Modules, Packages, Virtualenvs, pip & uv
  81. Day 81M02Statistics — CLT, Hypothesis Testing, Confidence Intervals
  82. Day 82M03Graphs — Representation, BFS, DFS, Shortest Path
  83. Day 83M04Transactions & ACID — Isolation Levels, MVCC
  84. Day 84M05Lakehouse — Delta / Iceberg / Hudi, ACID on Files
  85. Day 85M07Kubernetes II — ConfigMaps, Secrets, HPA, Network Policies
  86. Day 86M11Gradient Boosting — XGBoost, LightGBM
  87. Day 87M12RNNs & LSTMs — Sequences & the Vanishing Gradient
  88. Day 88M01Testing with pytest — TDD Workflow
  89. Day 89M03Sorting — Merge, Quick, and When to Trust the Built-in
  90. Day 90M04Query Planning — EXPLAIN, Execution Plans, Tuning
  91. Day 91M05Data Quality — Freshness, Volume, Schema, Distribution
  92. Day 92M07Azure Cloud — Identity, Storage, Networking, App Service
  93. Day 93M11Evaluation Metrics — P/R/F1/ROC/PR/AUC
  94. Day 94M12Embeddings — word2vec, GloVe, Contrastive Learning
  95. Day 95M01Type Hints, mypy, dataclasses & pydantic
  96. Day 96M03Binary Search — the Pattern Behind 100 Problems
  97. Day 97M04NoSQL Landscape — KV, Document, Column, Graph
  98. Day 98M05Governance & Cost — Lineage, PII, Attribution
  99. Day 99M07Infrastructure as Code — Terraform / Bicep Basics
  100. Day 100M11Feature Engineering — Encoding, Scaling, Missing
  101. Day 101M12Transfer Learning & Fine-Tuning Classical DL
  102. Day 102M03Dynamic Programming — Memoisation & Tabulation
  103. Day 103M11Imbalanced Data — SMOTE, Class Weights, Thresholds
  104. Day 104M03Greedy & Backtracking — When to Use Each
  105. Day 105M11Model Selection — CV, Hyperparameter Tuning, Optuna

Essays & deep-dives

31 entries

Standalone articles — no set schedule, no prerequisites. Long-form deep-dives, chapter series, and one-off notes.

  1. Weekly revisions — the 6-month plan· 26 chapters

    One revision per week walking through the previous week’s five sessions with recall prompts.

  2. Microsoft Web Stack — novice to fluent· 22 chapters

    22-chapter self-study plan from developer tooling through resilience, security, and shipping.

  3. How Transformers actually attend

    Beyond the textbook diagrams — what a single attention head is really computing, how multi-head splits the world, and why scaling laws keep rewarding bigger context.

  4. Designing a recommendation system from scratch

    Retrieval vs ranking, candidate generation, freshness vs relevance — the tradeoffs every real recommender lives by.

  5. Kafka 101 for ML engineers

    Topics, partitions, consumer groups — the parts of Kafka that actually matter when you put ML features behind it.

  6. Overall Engineering Clarity — Data, Distributed Systems and AI (Deep Dive)

    A long-form, primary study companion. Internals, flows, decision trees, code, and Q&A with reasoning across Spark, lakehouse, graphs, search, LLMs, RAG/agents, distributed HLD, governance, modeling, SQL, JVM, Python, K8s and CI/CD.

  7. Data Infrastructure for AI & Experimentation at Scale

    A comprehensive deep-dive into the data backbone powering ML, personalization, experimentation, and GenAI on modern streaming platforms

  8. Automating audio generation

    From text , generate audio files and publishing them to webapp

  9. Deploying scripts as an API in Azure

    Deploying local python scripts and converting them as an API.

  10. Learning how to build an recommendation system from initial signals

    From a few initial adopters of a product, how we can target new set of users who are more likely can use the product

  11. Building windows app and publishing to app store.

    Exploring the fundamentals of building an .exe file from scratch, including C++ compilation, object files, linking, DLLs, and more.

  12. Stanford CS229: Machine Learning Course

    CS229 provides a broad introduction to statistical machine learning (at an intermediate / advanced level) and covers supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical ); and reinforcement learning among other topics

  13. Best LLM Prompt for understanding any concept in-depth

    Use this prompt for gathering information all at one place

  14. Introduction to TensorFlow on Google Cloud

    Diving deep into a Google Skill boost

  15. Diving deep into Tiktok recommendation engine

    Going deeper into the video rec repo Monolith and paper produced by Bytedance

  16. Basics of Azure AI Foundry

    A self-sufficient guide to Azure AI Foundry — what it is, how the hub/project/deployment model works, how to ship a grounded agent end-to-end with SDK + Bicep, and the security/eval/cost levers you cannot skip.

  17. Reading on MEDICAL ERROR DETECTION AND CORRECTION IN CLINICAL NOTES

    This blog explores a paper on detecting and correcting medical errors in clinical notes using Large Language Models (LLMs)

  18. Apache Airflow - open source orchestration engine

    Architecture of Apache Airflow, how DAGs help design complex flows and dependencies, and how we can leverage Apache airflow to train a ML Model and monitor.

  19. Taking the Azure Fabric Ignite Edition Challenges to Complete

    Microsoft Learn Challenge conducting a challenge to get good in few of the challenges which are super useful to complete to gain knowledge on Microsoft Fabric.

  20. Exploring different services in GCP

    Exploration and documentation of different services offered in GCP

  21. Starting a company in India

    Documenting the process of starting a company in india

  22. Exploring Azure Data Explorer and Best Practices

    A self-sufficient deep-dive on Azure Data Explorer (ADX/Kusto) — architecture, the KQL language from zero to advanced, ingestion patterns, performance/cost levers, and operational best practices.

  23. Google 5 Day Gen AI course with interactive hands-on practice

    Google and Kaggle provided good summary course on Gen AI , the blog contains details and highlights of the course.

  24. RAG architecture basics and workings

    Retrieval-Augmented Generation from first principles — embeddings, vector databases, chunking, retrieval, prompt construction, evaluation, and common failure modes — enough depth to build one yourself.

  25. AI Voice chatting to help with Customer support use-cases

    Using current speech augmented LLMs (SpeechLLMs) with realtime voice modality to understand user issues and to provide support and solutions.

  26. Audio to Video Generation Using Replit AI and Deploy as an Azure Webapp

    Tool to convert an uploaded audio mixed with an image and generate a video format with image and uploaded audio in the video format.

  27. Deploying Web Applications in Azure with Docker

    A self-sufficient, production-minded walkthrough — from Docker internals to a hardened deploy on Azure App Service / Container Apps.

  28. Orchestrating ML Pipelines with Azure Data Factory

    Leveraging Azure Data Factory for Scalable and Efficient Machine Learning Workflows

  29. Developing a Astrology webapp version 1.

    Initial version 1 of Astro app hosted at astroyuga.com

  30. Improving the UI of this blogging app - V2

    Changing the UI layout and improving the experience by modernizing the UI with custom styling

  31. Leveraging CURSOR and Azure Services for Rapid Web Deployment

    Accelerating Development and Deployment Cycles with AI Tools

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