| ▲ | The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer(arxiv.org) |
| 223 points by ibobev 4 hours ago | 35 comments |
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| ▲ | StilesCrisis 2 hours ago | parent | next [-] |
| The very first formula in the text, explicitly described as "as simple as possible," names its variable ê1 with no explanation given. I think this paper needs another pass. |
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| ▲ | apricot an hour ago | parent | next [-] | | What do you mean "no explanation given"? It says: "In 2 dimensions, the standard (or identity) basis vectors are ê1 = [ 1 0 ] and ê2 = [0 1]." Seems pretty clear to me he's defining ê1 and ê2, or rather telling us that he's using this very common way of naming the basis vectors. | |
| ▲ | mkl an hour ago | parent | prev | next [-] | | It's just giving a vector a name. It also names ê₂. These particular names are very standard and familiar to most people who've met vectors before. You can look at those vectors and see that the position of the 1 is the subscript, without explanation. The ^ could do with an explanation that it means unit vector, but if you've met vectors before you likely know that (maybe that's elsewhere in the book, but a quick search didn't find it). | |
| ▲ | nhatcher an hour ago | parent | prev [-] | | That's absolutely standard notation for a unit basis vector. I haven't read the prerequisites carefully, so if it addressed to the general public maybe? It is perhaps addressed to a different crowd? |
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| ▲ | hasteg 38 minutes ago | parent | prev | next [-] |
| Another book which I would highly recommend for learning the math (or at least getting a very good idea of how it works) behind machine learning/transformers is "Why Machines Learn: The Elegant Math Behind Modern AI" by Anil Ananthaswamy. I read it this past year and I found it was extremely good at pacing and building up intuition to the "how and why" of most important math concepts and discoveries in ML that have lead to where we are today. For those who don't already have a fairly deep understanding of linear algebra, statistics, and multivariate calculus, this book will be a far more gentle introduction yet still has enough rigor for you to come away with something. |
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| ▲ | p1esk 3 hours ago | parent | prev | next [-] |
| Wow, this book is surprisingly readable! I like how he starts from basics of linear algebra. |
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| ▲ | nblgbg 3 hours ago | parent | prev | next [-] |
| Are there any deep learning books with some problems to solve ? |
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| ▲ | prideout 4 hours ago | parent | prev | next [-] |
| My favorite little book is François Fleuret's: The Little Book of Deep Learning
https://fleuret.org/francois/lbdl.html |
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| ▲ | arcanemachiner 4 hours ago | parent | next [-] | | I love how that site offers a phone-formatted PDF as an option. How thoughtful! | | |
| ▲ | qifzer 2 hours ago | parent | next [-] | | You may enjoy the reflow feature on Koreader. It fits a pdfs content into a screenwidth single column layout. That text is also zoomable. | |
| ▲ | dominotw 2 hours ago | parent | prev [-] | | i got duo to increase my reading. | | |
| ▲ | deskamess an hour ago | parent | next [-] | | Is it working out well for tech pdf's and books? Once I saw the form factor I realized it would be better than an iPad (less heavy, better battery) and that is the use case I am looking at. Might wait till v2. | | |
| ▲ | y1n0 an hour ago | parent [-] | | From what I understand the iPad mini weighs the same with a slightly larger screen. Has their matte display option. I have big pockets so for me it’s probably pocketable. But I still would have a phone. So I’ve got a duo on order with a year lease so I’m not stuck if I don’t think it’s great. I have to confess to considering a mini with cellular instead of a phone altogether though. |
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| ▲ | y1n0 an hour ago | parent | prev [-] | | My old crappy eyes need big screens for this. I have one on order. I’m ambivilating over the screen crack issue. |
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| ▲ | brcmthrowaway an hour ago | parent | prev | next [-] | | My full stack JS jockey buddy studied this in the nascent days of LLMs to prep for an interview and is now generationally wealthy. Sliding doors moment for sure... | |
| ▲ | joe_the_user 2 hours ago | parent | prev | next [-] | | I'd note that the Fleuret books gives the foundation for basic deep learning whereas the OP seems to give the necessary foundations for Transformers and related machinery. These later addition what is now "AI" are in total at least as complicated as the original deep learning concepts. Not that just learning basic neural networks but the two book don't appear to cover the same ground. | | |
| ▲ | wh306 an hour ago | parent [-] | | Only had a quick skim, but looks like the content of this book has very little to do with transformers. Might be more relevant for diffusion models (image/video generation) though. |
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| ▲ | CamperBob2 3 hours ago | parent | prev [-] | | It's a nice treatment of the subject, but I wish he would post a conventionally-formatted .PDF that puts every page on a full sheet of standard printer paper. | | |
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| ▲ | 3abiton 2 hours ago | parent | prev | next [-] |
| Someone should add it to the hacker news curated paper list! Along side the video and book list. |
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| ▲ | amplifydev 3 hours ago | parent | prev | next [-] |
| Interesting! Is there any one who have recommended books on the attention architecture specifically? I have been interested in that for a long time but never gotten around to actually studying it. |
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| ▲ | minimaxa 3 hours ago | parent | prev | next [-] |
| Appreciated, I drop all of these into a RAG folder for when I need them later on in projects. 8Gb of whitepapers/books... |
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| ▲ | cheesewhistle 2 hours ago | parent | next [-] | | I'm v curious how you're incorporating this into your workflow. Is it that when you want to implement something you can call out to these books as rigorous reference material for your agents? I'd never thought of doing such a thing and am curious how you're doing it and how it's worked. | | |
| ▲ | minimaxa 17 minutes ago | parent [-] | | You can source it here in the library tool, it connects to a cloud/local LLM for RAG: https://vektormemory.com/ Extracted whitepapers, should keep you busy: VL-JEPA: Joint Embedding Predictive Architecture for Vision-language [2512.10942v1]
A Multi-Memory Segment System for Generating [2508.15294v3]
Adversarial Cues in Decision Models Used [2610.11436v1]
AGENTIC BACKDOORS VIA PRETRAINING POISONING [2610.37741v1]
Agentic Memory Learning Unified Long-Term [2601.01885v2]
Agentic Semi-Structured Memory [2604.14362v1]
Agents That Know Too Much [2606.26627v1]
AI Agents Enable Adaptive [2606.03811v1]
AI Agents That Matter [2407.01502v1]
AI Assistants Cloud Versus Local LLM [2604.18566v2]
AMA-Bench Evaluating Long-Horizon Memory [2602.22769v2]
A-Mem Agentic Memory for LLM Agents [2502.12110v11]
APEX-MEM Agentic Semi-Structured Memory [2604.14362v1]
ASK ONLY WHEN NEEDED [2604.20572v2]
Attention Is All You Need [1706.03762v7]
Attention Is Not What You Need [2512.19428v1]
Benchmarking Multi-Agent LLM [2603.22651v1]
Beyond Nodes vs. Edges [2604.14685v1]
Beyond Static Summarization [2601.04463v1]
Calibrated Decision Models for Autonomous [2609.28940v1]
Character-and-Scene Episodic Memory for Agents [2602.06051v3]
CLASP Training-Free LLM-Assisted Source Code Watermarking [2510.11251v2]
Cognitive Architectures for Language Agents [2309.02427v3]
COMPUTATIONAL COPYRIGHT [2312.06646v5]
Constitutional AI [2212.08073v1]
Creative Problem Solving in Artificially Intelligent Agents [2204.10358v1]
CyberGym [2506.02548v3]
Deep image search [2602.10809v1]
Developing Surrogates [2602.21588v1]
Diffusion-Based Neural Network Weights Generation [2402.18153v2]
Dynamic LLM-Powered Agent Network [2310.02170v2]
Dynamic Logit-Level Gating [2606.04378v1]
EternalMath [2601.01400v1]
EverMemOS [2601.02163v2]
EvoMemBench Benchmarking Agent Memory [2605.18421v1]
ExploitBench [2605.14153v1]
FADEMEM BIOLOGICALLY-INSPIRED [2601.18642v2]
Finance-Grounded Optimization [2509.04541v2]
Formal Algorithms for Transformers [2207.09238v1]
Geogalactica [2401.00434v2]
HAGE - Harnessing Agentic Memory [2605.09942v1]
HaluMem [2511.03506v1]
Harnessing the Universal Geometry of Embeddings [2505.12540v4]
HIPPOCAMPUS [2602.13594v1]
hipporag [2405.14831v3]
Hybrid Reasoning Agents [2610.18861v1]
Hyper agents [2603.19461v1]
HyperMem [2604.08256v2]
Interactive Simulacra of Human Behavior [2304.03442v2]
Is Complex Agentic Memory Always Needed [2610.30755v1]
Language Modeling Is Compression [2309.10668v2]
Learn from your own latents [2605.27734v1]
Learning on the Job [2607.22157v1]
Learning to Memorize at Test Time [2501.00663v1]
Learning to Rewrite Tool Descriptions [2602.20426v2]
LLM Agents Already Know When to Call Tools [2605.09252v2]
LLM-Enabled Frequency-Aware [2602.19522v1]
LONGMEMEVAL [2410.10813v2]
MAGMA A Multi-Graph based [2601.03236v2]
MemGround [2604.14158v1]
Memory in the Age of AI Agents [2512.13564v2]
Memory Tax [2610.34073v1]
Memp - Exploring Agent Procedural Memory [2508.06433v4]
Mitigating Provenance-Role [2605.25869v1]
MOSS Self-Evolution through Source-Level Rewriting [2605.22794v2]
Multi-task DAG Learning [2306.02831v1]
Natural Language conductor [2512.04388v5]
NeuroGen - Neural Network Parameter Generation [2505.12470v2]
Next-Latent Prediction Transformers Learn Compact World Models [2511.05963v4]
Novel Memory Forgetting Techniques [2604.02280v1]
OMNI-LEAK Orchestrator Multi-Agen [2602.13477v2]
On Understanding Extremely Long-Term Video with Adaptive [2411.12593v3]
Opal Private Memory [2604.02522v1]
Parameter Prediction for Unseen Deep Architectures [2110.13100v1]
Prompt Optimization [2602.18449v1]
PromptMark [2606.20835v1]
RAG-MCP Mitigating Prompt Bloat [2505.03275v1]
RAIN-MERGING [2602.22538v1]
Recursive Self-Improvement through Multi-Agent [2610.12176v1]
Reflection-Based Memory [2506.02158v1]
Refusal in Language Models Is Mediated by a Single Direction [2406.11717v3]
REMEM REASONING WITH EPISODIC MEMORY [2602.13530v3]
Remember When It Matters [2607.08716v1]
Remembering More, Risking More [2605.17830v1]
Sakana Fugu Technical Report [2606.21228v1]
SalesRLAgent [2503.23303v1]
Scaling Long-Form Story Generation [2609.35759v1]
Search-R1- Training LLM [2503.09516v5]
Secure Semantic Communications [2602.22134v2]
Selective Memory for Artificial Intelligence [2603.15994v1]
Skill.state [2608.26263v2]
Smarter Together [2511.08301v1]
SmartSearch [2603.15599v1]
SOLVING A MILLION-STEP LLM TASK [2511.09030v1]
Storage Is Not Memory [2605.04897v1]
StructMem Structured Memory [2604.21748v1]
Structured Belief State and the First Precision [2605.11325v2]
SuperLocalMemory V3.3 [2604.04514v1]
SWE-MeM Learning Adaptive Memory [2606.28434v1]
Synthius-Mem Brain-Inspired Hallucination [2604.11563v1]
The AI Scientist-v2 [2504.08066v1]
The Bitter Lesson of Tool Calling [2608.06370v1]
The Customer Is the Builder [2610.30307v1]
The Gradient of Generative AI Release [2302.04844v1]
The Little Book of Generative AI Foundations [2605.29713v2]
The Malicious Use [1802.07228v2]
The Memory Wars [2508.05867v1]
Thinking Fast and Slow in AI [2110.01834v1]
Time algo [2610.00577v1]
Towards trustworthy agentic AI [2605.23989v1]
Transformers learn in-context by gradient descent [2212.07677v2]
TReMu Towards Neuro-Symbolic Temporal Reasoning [2502.01630v2]
TRINITY AN EVOLVED LLM COORDINATOR [2512.04695v3]
VERBALIZED SAMPLING [2510.01171v3]
Verifiable Agent-to-Agent [2602.02395v1]
Verified Multi-Agent Orchestration [2603.11445v2]
WEBCOACH - A MODEL [2511.12997v1]
XGrammar-2 Dynamic and Efficient Structured Generation [2601.04426v4]
Your Agent’s Memories Are Not Its Own [2607.05029v1]
Zep A Temporal Knowledge Graph Architecture [2501.13956v1] |
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| ▲ | plicense 2 hours ago | parent | prev | next [-] | | Can you share what you have so far in that folder? | | |
| ▲ | gerdesj 2 hours ago | parent [-] | | They spell it out quite clearly: "8Gb of whitepapers/books..." |
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| ▲ | brcmthrowaway an hour ago | parent | prev [-] | | Holy.. Care to share the list of titles in a gist? |
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| ▲ | Daklone an hour ago | parent | prev [-] |
| We are recommending books for HackerBooks.org I see. Good, this is why I have come here also. Let me tell you about one of the greatest book series of our generation: Dungeon Crawler Carl, By Matt Dinniman https://mattdinniman.com/books/ From the Dust jacket:
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RePublished Aug 27, 2024 |
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| ▲ | northes an hour ago | parent [-] | | The street marketing around this book is frankly baffling, do you suppose this is relevant? |
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