This book provides a comprehensive introduction to the foundational concepts of machine learning (ML) and deep learning (DL). It bridges the gap between theoretical mathematics and practical application, focusing on Python as the primary programming language for implementing key algorithms and data structures. The book covers a wide range of topics, including basic and advanced Python programming, fundamental mathematical operations, matrix operations, linear algebra, and optimization techniques crucial for training ML and DL models.

Questo libro fornisce un'introduzione completa ai concetti fondamentali di machine learning (ML) e deep learning (DL). Colma il divario tra matematica teorica e applicazione pratica, concentrandosi su Python come linguaggio di programmazione principale per l'implementazione di algoritmi e strutture dati chiave.

https://arxiv.org/abs/2410.19849

@informatica

#python #datastructures #deeplearning #machinelearning

Deep Learning and Machine Learning -- Python Data Structures and Mathematics Fundamental: From Theory to Practice

This book provides a comprehensive introduction to the foundational concepts of machine learning (ML) and deep learning (DL). It bridges the gap between theoretical mathematics and practical application, focusing on Python as the primary programming language for implementing key algorithms and data structures. The book covers a wide range of topics, including basic and advanced Python programming, fundamental mathematical operations, matrix operations, linear algebra, and optimization techniques crucial for training ML and DL models. Advanced subjects like neural networks, optimization algorithms, and frequency domain methods are also explored, along with real-world applications of large language models (LLMs) and artificial intelligence (AI) in big data management. Designed for both beginners and advanced learners, the book emphasizes the critical role of mathematical principles in developing scalable AI solutions. Practical examples and Python code are provided throughout, ensuring readers gain hands-on experience in applying theoretical knowledge to solve complex problems in ML, DL, and big data analytics.

arXiv.org
Flocking Quadtrees

Learn how to make a flocking simulation using Quadtrees and Clojurescript. It's a revisit of last weeks post about Functional Quadtrees.

Latest drop from our group 🥳 Paper by Daniel Jünger, Kevin Kristensen, Yunsong Wang, Xiangyao Yu, and Bertil Schmidt

#Bloomfilters are a fundamental #DataStructure for approximate membership queries, with applications ranging from #databases to #genomics.

https://arxiv.org/abs/2512.15595

#GPU #CUDA #bigdata #bioinformatics #datastructures

Optimizing Bloom Filters for Modern GPU Architectures

Bloom filters are a fundamental data structure for approximate membership queries, with applications ranging from data analytics to databases and genomics. Several variants have been proposed to accommodate parallel architectures. GPUs, with massive thread-level parallelism and high-bandwidth memory, are a natural fit for accelerating these Bloom filter variants potentially to billions of operations per second. Although CPU-optimized implementations have been well studied, GPU designs remain underexplored. We close this gap by exploring the design space on GPUs along three dimensions: vectorization, thread cooperation, and compute latency. Our evaluation shows that the combination of these optimization points strongly affects throughput, with the largest gains achieved when the filter fits within the GPU's cache domain. We examine how the hardware responds to different parameter configurations and relate these observations to measured performance trends. Crucially, our optimized design overcomes the conventional trade-off between speed and precision, delivering the throughput typically restricted to high-error variants while maintaining the superior accuracy of high-precision configurations. At iso error rate, the proposed method outperforms the state-of-the-art by $11.35\times$ ($15.4\times$) for bulk filter lookup (construction), respectively, achieving above $92\%$ of the practical speed-of-light across a wide range of configurations on a B200 GPU. We propose a modular CUDA/C++ implementation, which will be openly available soon.

arXiv.org
Building a Fast, Memory-Efficient Hash Table in Java (by borrowing the best ideas)

One day, I ran into SwissTable—the kind of design that makes you squint, grin, and immediately regret every naive linear-probing table you’ve ever shipped. This post is the story of how I tried to bring that same “why is this so fast?” feeling into Java. It’s part deep dive, part engineering diary, and part cautionary tale about performance work. 1) The SwissTable project, explained the way it feels when you first understand it SwissTable is an open-addressing hash table design that came out of Google’s work and was famously presented as a new C++ hash table approach (and later shipped in Abseil).

Bluue Whale

I had my #DataStructures students replicate the famous "animals" #BASIC program in #CPP as a way of learning about binary decision trees. Here's a conversation I had with one of their programs (after they trained it):

Does it live in the water? yes
Does it have legs? yes
Is it freaky as hell? yes
Are you thinking of an axolotl? yes
Yay! I guessed it!
Want to play again? no
Goodbye.

Cách giải “Best Time to Buy and Sell Stock” trên LeetCode chỉ với 1 vòng duyệt O(N): giữ giá mua tối thiểu, tính lợi nhuận hiện tại, cập nhật lợi nhuận tối đa. Đoạn code Java ngắn gọn, hiệu quả, phù hợp cho phỏng vấn. #LeetCode #Coding #Algorithm #DataStructures #Java #Thuật_Toán #Lập_trình #InterviewPrep

https://dev.to/debeshpg90/121-best-time-to-buy-and-sell-stock-leetcode-top-interview-150-coding-questions-16m3

121. Best Time to Buy and Sell Stock | LeetCode | Top Interview 150 | Coding Questions

Problem Link https://leetcode.com/problems/best-time-to-buy-and-sell-stock/ ...

DEV Community

🚀 DOMINANDO DICIONÁRIOS PYTHON: O SEGREDO O(1) PARA DSA! 🐍

Cansado de buscas lentas em listas? Descubra como dict vira hash table turbinada: hashing interno, colisões resolvidas, O(1) em inserção/busca/deleção + exemplos reais pra grafos, caches e LeetCode!

Desde Python 3.7: ordem garantida! 💥

👉 Leia o guia técnico completo:
https://bolha.blog/riverfount/dominando-dicionarios-em-python-o-segredo-o-1-para-dsa-eficiente

#Python #DSA #DataStructures #HashTable #Programação #DevOps

Dominando Dicionários em Python: O Segredo O(1) para DSA Eficiente!

Procurando por dicionários Python DSA, hash tables em Python, complexidade Big O dict Python ou estruturas de dados Python avançadas? Nes...

Riverfount
🤔🎨 Ah, yes, yet another attempt to make data structures "fun" by shoehorning them into #Clojure. Who wouldn't want to spend their weekend navigating a "functional" #quadtree demo that ironically leaves mobile users clicking aimlessly? 📱🔍
https://lbjgruppen.com/en/posts/functional-quadtree-clojure #dataStructures #functionalProgramming #UXdesign #HackerNews #HackerNews #ngated
Functional Quadtrees

A Quadtree is a tree data structure, which is useful for giving more focus/detail to certain regions of your data, while saving resources elsewhere. I could only find a couple tutorials/guides and both were imperative, so I figured it'd be fun to do a functional version in Clojure which runs in the browser.

Functional Quadtrees

A Quadtree is a tree data structure, which is useful for giving more focus/detail to certain regions of your data, while saving resources elsewhere. I could only find a couple tutorials/guides and both were imperative, so I figured it'd be fun to do a functional version in Clojure which runs in the browser.