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.
https://cdn.filepost.dev/file/filepost/uploads/15/1578f21219...
But fyi for others this is one version behind and missing these updates:
V1.3 (May 12, 2026)
Many typos and phrasing fixes with the help of LLMs.
Section 3.8. Added a section about large-scale training- Understanding deep learning by Prince[1]. Has both theoretical and programming exercises.
- Deep learning - Foundations and concepts by Bishop[2].
- Deep learning architectures - mathematical approach - Calin[3].
- Mathematical theory of deep learning by Petersen et al[4]. Only handful of exercises each chapter.
- Dive into deep learning[5]. This one is more practical programming hands-on. The exercises is a mix between programming exercises and theoretical.
- EECS 182/282A | Deep Neural Networks 2025 (not a book but a course by Berkley)[6]. I only skimmed the assignments, but they seem very interesting. A mix of theoretical and practical.
I think Bishop is probably the best one for theoretical exercises, balancing between not too difficult but not too easy either. However, if you also want programming exercises, Prince has lots of those. Berkley also seems very interesting, modern and encompassing.
[1] https://udlbook.github.io/udlbook/
[2] https://www.bishopbook.com/
[3] https://link.springer.com/book/10.1007/978-3-030-36721-3
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.
8Gb of whitepapers/books...
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]
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).
It is perhaps addressed to a different crowd?
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: The apocalypse will be televised!
You know what’s worse than breaking up with your girlfriend? Being stuck with her prize-winning show cat. And you know what’s worse than that? An alien invasion, the destruction of all man-made structures on Earth, and the systematic exploitation of all the survivors for a sadistic intergalactic game show. That’s what. READ MORE
Join Coast Guard vet Carl and his ex-girlfriend’s cat, Princess Donut, as they try to survive the end of the world—or just get to the next level—in a video game–like, trap-filled fantasy dungeon. A dungeon that’s actually the set of a reality television show with countless viewers across the galaxy. Exploding goblins. Magical potions. Deadly, drug-dealing llamas. This ain’t your ordinary game show.
Welcome, Crawler. Welcome to the Dungeon. Survival is optional. Keeping the viewers entertained is not.
Published sept 21, 2020 RePublished Aug 27, 2024
> Changing coordinates. Given a vector x expressed in the standard basis, we change coordinates by applying
> This operation does not change the vector itself; it only changes how the vector is represented, converting it into eigenvector coordinates.I'm sure the author knows about the difference between the vector and a coordinate vector given a basis. I just don't like this wording. It's weird to say applying a function doesn't affect a change.