Grokking Artificial Intelligence Algorithms is live · pricing from $19.99/mo · last verified 8 Nov 2026 · viability monitor
Grokking Artificial Intelligence Algorithms
Grokking AI Algorithms, Second Edition teaches AI algorithms by building them in Python — search to transformers.
A teaching book rather than a reference, and a strong one for developers who learn by typing. The March 2026 edition earns its place mostly through the new LLM pipeline and image diffusion chapters, which tie classic algorithms to how generative AI actually works. Buy it for the reasoning under the hood; pass if you need MLOps, deployment, or research-grade math.
Verdict generatedVerified 6d ago · liveness 54/100 · cite: rightaichoice.com/tools/grokking-artificial-intelligence-algorithms
- Software developers transitioning into AI who want to understand algorithms, not just call libraries
- Developers who learn best by typing and running code rather than reading proofs
- Data scientists who can build models but want stronger algorithmic foundations
- Self-taught programmers wanting a structured path through classic and modern AI algorithms
- Advanced researchers looking for novel algorithms or original research contributions
- Engineers wanting a production ML framework, deployment, or MLOps guide
- Readers who only want LLM prompt engineering without classic algorithm background
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Skip Grokking AI Algorithms if you need deployment, MLOps, or serving guidance, an interactive hosted coding environment, or graded exercises and certificates — this is a self-directed Python book you run on your own machine.
The eBook is nonrefundable once delivered, so a change of mind after checkout cannot be reversed the way a print order can.
For one book, buying outright is the cheaper path: the eBook is $33.59 (30% off the $47.99 list) and print-plus-eBook is $35.99 (40% off $59.99). The Lite subscription at $19.99/mo only beats that if you read roughly two or more Manning titles inside a month, and Pro at $24.99/mo adds audiobooks plus one free eBook per month to keep. Teams needing shared catalog access go to a 5, 10, or 20-seat plan priced on request rather than per-reader.
In short
Grokking Artificial Intelligence Algorithms — Grokking AI Algorithms, Second Edition teaches AI algorithms by building them in Python — search to transformers. Best for Software developers transitioning into AI who want to understand algorithms, not just call libraries, Developers who learn best by typing and running code rather than reading proofs, Data scientists who can build models but want stronger algorithmic foundations. Plans from $19.99/mo.
What is Grokking Artificial Intelligence Algorithms?
Grokking Artificial Intelligence Algorithms — Grokking AI Algorithms, Second Edition teaches AI algorithms by building them in Python — search to transformers. Best for Software developers transitioning into AI who want to understand algorithms, not just call libraries, Developers who learn best by typing and running code rather than reading proofs, Data scientists who can build models but want stronger algorithmic foundations. Plans from $19.99/mo.
Rishal Hurbans' second edition is a 592-page Manning book that teaches AI by implementation. You move from search and optimization through planning, intelligent agents, evolutionary algorithms, neural networks, and reinforcement learning, then into the chapters that define this edition: constructing an LLM pipeline from scratch and building an image diffusion model from scratch. The March 2026 edition (ISBN 9781633434813) adds those generative chapters over the first edition. Each chapter takes a problem type, maps a real-world task onto it, walks the pseudocode, then hands you runnable Python. Picking the right algorithm is treated as a skill in itself, so you finish with a way to reason about which approach fits a problem — not a catalog of recipes. The supporting material is generous for one purchase: official GitHub source code, downloadable chapter briefs, an interactive book forum on Manning's liveBook platform, an audio edition, and a free previous-edition eBook when you register a print copy. Sample briefs such as 'What is an Algorithm?' and 'How Does AI Create Images and Videos?' let you audition the style before buying. Against broad reference texts like Russell and Norvig's Artificial Intelligence: A Modern Approach, this trades breadth and formal rigor for diagrams and code you actually run. The transformer and diffusion chapters are what separate it from older algorithm primers — a working bridge from classic AI to generative AI.
Key features
- Visual explanations with diagrams and analogies for each algorithm
- Step-by-step Python code examples you can run and modify
- Covers search, optimization, planning, and learning algorithms
- Build intelligent agents that solve puzzles
- Solve problems with evolutionary and genetic algorithms
- Make predictions with neural networks
- Understand reinforcement learning through practical examples
- Build a transformer/LLM pipeline from scratch
- Build an image diffusion model from scratch
- Guidance on picking the right algorithm for each AI problem
- Pseudocode walkthroughs before the implementation
- Thought-provoking exercises and real-world case studies
- Official GitHub repository with all source code
- Audio edition via online + audio purchase or Pro subscription
- Free previous-edition eBook included with print purchase
Behind the verdict
Where this book earns its money is the connective tissue. Most AI primers either drown you in proofs or skip straight to library calls. Hurbans walks the middle line: here's the problem type, here's the pseudocode, here's the runnable Python, here's why you'd choose this algorithm over that one. In practice, the 'picking the right algorithm' thread is the part you'll keep returning to after the exercises are done. Pick this when you're a working developer who can read code faster than notation. The GitHub source repo and chapter briefs make it easy to jump to a specific algorithm instead of reading cover to cover. The second edition's transformer and diffusion chapters are the reason to get this version rather than a used first edition — they connect the classic material to what people are actually building with now. Pass on it if you want a production ML handbook. There's no deployment, serving, or MLOps content here, and no novel research. Readers who only want prompt engineering will find the classic algorithm grounding slower than they'd like; that grounding is the point, but it isn't for everyone. The closest alternative is Russell and Norvig's Artificial Intelligence: A Modern Approach for breadth and formal rigor, though that book is a reference and a semester course, not something you code alongside. If you want a code-first tour with generative AI coverage at the end, this fits better. One caveat worth naming: it's a book, so you get exercises and case studies but no hosted environment or graded feedback unless you use Manning's liveBook forum. Budget for that.
Limitations
- This is a Manning book, not a software tool, so it has no hosted platform, API, or interactive coding environment — the code examples are meant to run locally in Python.
- The second edition, published March 2026, adds chapters on building a transformer/LLM pipeline from scratch and an image diffusion model from scratch.
- Coverage is broad, spanning search, optimization, planning, learning, evolutionary algorithms, neural networks, and reinforcement learning, which means no single topic goes as deep as a dedicated text. eBook purchases are nonrefundable because they are delivered immediately.
as of 2026-10-07
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Is Grokking Artificial Intelligence Algorithms still active?
Grokking Artificial Intelligence Algorithms is live · pricing from $19.99/mo · last verified 8 Nov 2026 · viability monitor.
Viability score
How well maintained and how widely used is Grokking Artificial Intelligence Algorithms? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: November 2026
How we score →What do users say about Grokking Artificial Intelligence Algorithms?
What people actually say about Grokking Artificial Intelligence Algorithms — is it worth it?
We scanned public community sources for Grokking Artificial Intelligence Algorithms on Nov 8, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 3 of the posts we fetched could be positively tied to Grokking Artificial Intelligence Algorithms. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
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Every listed fact about Grokking Artificial Intelligence Algorithms — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 8 Nov 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified Grokking Artificial Intelligence Algorithms 14 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
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How much does Grokking Artificial Intelligence Algorithms cost in 2026?
Grokking Artificial Intelligence Algorithms: Plans from $19.99/mo. Pricing last verified 8 Nov 2026.
Plans compared
Every published Grokking Artificial Intelligence Algorithms tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| eBook (pdf, ePub, online) | $47.99Vendor: “$47.99 (currently $31.19, 35% off)” | — | Solo developer or student who wants the full 592-page second edition immediately and will run the code locally in Python.Adds: Starting tier: gives you the complete second edition in pdf, ePub, and online formats plus access to the official GitHub source code. |
| Print (includes eBook) | $59.99Vendor: “$59.99 (currently $38.99, 35% off)” | — | Readers who want a physical 592-page reference on the desk and still want the digital copy for searching and copy-pasting code.Adds: Adds the black-and-white printed book on top of the eBook edition, which is included at no extra cost. |
| Online + audio (read and listen) | $49.99Vendor: “$49.99 (currently $32.49, 35% off)” | — | — |
| Lite subscription | $19.99/mo | monthly | Developers reading several Manning titles a month who do not need audiobooks or video content.Adds: Starting subscription tier at $19.99/mo: unlocks the whole Manning catalog including MEAPs, but not audiobooks, liveVideo, or liveProject content. |
| Pro subscription | $24.99/mo | monthly | Readers who want audiobooks and video courses across the catalog and buy Manning titles often enough to use the discount.Adds: Adds audiobooks, liveVideos, and liveProjects on top of the Lite catalog, plus one free eBook per month to keep and an exclusive 50% discount on purchases. |
| Team | Custom (5, 10, or 20 seats) | quote | — |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Grokking Artificial Intelligence Algorithms's pricing actually pencils out — and where peers do it cheaper.
For one book, buying outright is the cheaper path: the eBook is $33.59 (30% off the $47.99 list) and print-plus-eBook is $35.99 (40% off $59.99). The Lite subscription at $19.99/mo only beats that if you read roughly two or more Manning titles inside a month, and Pro at $24.99/mo adds audiobooks plus one free eBook per month to keep. Teams needing shared catalog access go to a 5, 10, or 20-seat plan priced on request rather than per-reader.
Has Grokking Artificial Intelligence Algorithms' pricing changed?
No plan price for Grokking Artificial Intelligence Algorithms has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Grokking Artificial Intelligence Algorithms?
Pick Grokking Artificial Intelligence Algorithms if you are Software developers transitioning into AI who want to understand algorithms, not just call libraries, Developers who learn best by typing and running code rather than reading proofs, Data scientists who can build models but want stronger algorithmic foundations, Self-taught programmers wanting a structured path through classic and modern AI algorithms.
Look elsewhere if you are Advanced researchers looking for novel algorithms or original research contributions, Engineers wanting a production ML framework, deployment, or MLOps guide, Readers who only want LLM prompt engineering without classic algorithm background, Learners who need graded exercises, certificates, or an interactive hosted platform.
Skip it if Skip Grokking AI Algorithms if you need deployment, MLOps, or serving guidance, an interactive hosted coding environment, or graded exercises and certificates — this is a self-directed Python book you run on your own machine.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Grokking Artificial Intelligence Algorithms actually fits — and what changes day-one when you adopt it.
You work through the search, optimization, and planning chapters in the evenings, cloning the GitHub source repository and modifying each example to your own problem before moving on.
Outcome: You can explain and implement A*-style pathfinding and genetic optimization yourself, and you stop copying AI code you do not understand.
You already train models but read the neural network and reinforcement learning chapters plus the new transformer/LLM pipeline chapter, running each implementation locally to inspect intermediate values.
Outcome: You can reason about why a model behaves the way it does and debug architecture choices instead of treating the framework as a black box.
You use the chapter briefs and pseudocode walkthroughs as a review path, sketching each algorithm from memory and then checking against the book's Python.
Outcome: You can whiteboard and justify algorithm selection under interview pressure, including how a transformer is assembled.
Use cases
- Learn how search algorithms like A* work by building a pathfinding simulation.
- Implement a genetic algorithm that optimizes a travel itinerary step by step.
- Understand a neural network's internals by coding one from scratch in Python.
- Build a reinforcement learning agent that learns to play a simple game.
- Construct a small transformer/LLM pipeline to see how modern language models are assembled.
- Build an image diffusion model from scratch to understand how AI creates images.
- Prepare for machine learning interviews by reviewing algorithm fundamentals with clear explanations.
Setup time & first value
How long it actually takes to get something useful out of Grokking Artificial Intelligence Algorithms — broken out by persona, not the marketing-page minute.
Fastest for the eBook: buy at $33.59 and start reading within minutes, with the GitHub source-code repository available immediately for about 10 minutes of clone-and-run setup on any machine with Python installed. Print buyers wait for shipping. A Lite or Pro subscriber at $19.99/mo or $24.99/mo gets access right after signup, roughly the time it takes to create an account and open liveBook.
Switching to or from Grokking Artificial Intelligence Algorithms
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From the first edition: Manning offers upgrade discounts and the second edition includes the free previous-edition eBook, so you keep the older text while moving to the March 2026 chapters on transformers and diffusion.
- →From a video-first course: pair the course's pacing with this book's pseudocode-then-code structure and run the GitHub examples to replace passive watching with implementation.
- →From free tutorials and blog posts: use the book's chapter briefs as a syllabus and its case studies to fill the gaps scattered tutorials leave between algorithm families.
- →From Russell and Norvig's Artificial Intelligence: A Modern Approach: use this as the practical companion that swaps proofs for diagrams and runnable Python on the same core topics.
- ↗To a production ML engineering guide: move on once you can implement the algorithms, because this book deliberately stops before deployment, serving, and MLOps.
- ↗To a deep learning framework course: graduate to PyTorch- or JAX-focused material when you want to train at scale rather than build each layer by hand.
- ↗To an applied LLM or prompt engineering resource: go here if your goal shifts from understanding the transformer internals to shipping LLM features quickly.
- ↗To original research papers: read the primary literature once the book's foundations stop being the bottleneck for what you want to build.
Integrations
Resources & guides
- Resourcegithub.com
Grokking Artificial Intelligence Algorithms · Grokking Artificial Intelligence Algorithms
Helpful link from github.com
- Resourcemanning.com
Grokking Ai Algorithms Second Edition · Grokking Artificial Intelligence Algorithms
Helpful link from manning.com
- Resourcemanning.com
Why Buy From Manning · Grokking Artificial Intelligence Algorithms
Helpful link from manning.com
Tutorials & Learning
YouTube returned 6 videos for “Grokking Artificial Intelligence Algorithms”, and we withheld 5: 5 did not mention Grokking Artificial Intelligence Algorithms. Showing the 1 we can prove is about Grokking Artificial Intelligence Algorithms.
Official links
What are the best Grokking Artificial Intelligence Algorithms alternatives?
The closest alternatives to Grokking Artificial Intelligence Algorithms we list are SapienAI, openscience and Hands On Large Language Models. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| SapienAI | Freemium | Pricing not published | — |
| openscience | Freemium | Pricing not published | — |
| Hands On Large Language Models | Paid | Pricing not published | — |
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Common stack mates teams adopt alongside Grokking Artificial Intelligence Algorithms, with the specific reason each pairing earns its keep.
SapienAI
SapienAI is an open, self-hostable agentic research workspace that unites AI agents with collaborative LaTeX, Typst and Markdown writing.
openscience
Open-source AI co-scientist that runs scientific research workflows: literature search, Python code, and experiments in one workspace.
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Grokking Artificial Intelligence Algorithms vs its closest rival
Grokking Artificial Intelligence Algorithms's closest head-to-head is with Surge Ai.
Our Grokking Artificial Intelligence Algorithms vs Surge Ai verdict written Jul 3, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
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