"By integrating Monte Carlo Tree Search (MCTS) with LLMs, LATS unifies reasoning, acting, and planning, creating a more deliberate and adaptive problem-solving framework." Ozgur Guler outlines the benefits of using Language Agent Tree Search (LATS) to tackle complex LLM decision-making tasks.
Towards Data Science
Internet Veröffentlichung
Toronto, Ontario 634,508 followers
Your home for data science. A Medium publication sharing concepts, ideas and codes.
Über uns
Using Medium, we provide a platform for thousands of people to exchange ideas and to expand our understanding of data science. Our audience is mixed, consisting of readers entirely new to the subject and expert professionals who want to share their inventions and discoveries. The TDS team currently includes 2 full-time editors, as well as 15+ Volunteer Editorial Associates. We’re thrilled to share the work of hundreds of independent authors and contributors.
- Website
-
http://towardsdatascience.com
External link for Towards Data Science
- Industrie
- Internet Veröffentlichung
- Größe des Unternehmens
- 2-10 Mitarbeiter
- Hauptsitz
- Toronto, Ontario
- Typ
- In Privatbesitz
- Spezialitäten
- Data Science, Machine Learning, Artificial Intelligence, and Community building
Standorte
-
Primäre
2300 Yonge Street
Suite 1600
Toronto, Ontario M4P 1E4, CA
Employees at Towards Data Science
Aktualisierungen
-
What does the future hold for human-computer interaction in the age of AI and advanced speech recognition and synthesis? Luciano Abriata examines the current state of things and unpacks the challenges that stand between today's tools and Star Trek-like magic.
How AI Could Soon Take Human-Computer Interaction to New Levels
towardsdatascience.com
-
"It’s usually the more abstract/soft-skill-related part of the job that most people don’t know how to navigate — things like how to break down an abstract business problem into smaller, clearly defined analyses that can eventually lead to concrete business impact." Tessa Xie summarizes her learnings from her days as a consultant to help data scientists excel at their jobs.
Analytics Frameworks Every Data Scientist Should Know
towardsdatascience.com
-
New to speculative decoding? Het Trivedi shows how this approach can help speed up LLM inference in a new, practical guide.
Boosting LLM Inference Speed Using Speculative Decoding
towardsdatascience.com
-
To gain a hands-on understanding of different reinforcement learning algorithms and how they perform, follow along Anand Majmudar's fascinating experiment, where simulated humanoid robots try to battle it out against the others based on their respective policies.
The Tournament of Reinforcement Learning: DDPG, SAC, PPO, I2A, Decision Transformer
towardsdatascience.com
-
For a comprehensive overview of RAG systems, why they don't always work as expected, and how to approach them effectively, head right over to Ahmed BESBES's latest article.
What Nobody Tells You About RAGs
towardsdatascience.com
-
"In this post I will show you how the Floyd-Warshall algorithm can be employed to compute bond distances between atoms in a molecule, or said in a different way, the minimal number of bonds separating every possible pair of atoms." Don't miss Luciano Abriata's new tutorial.
The Floyd-Warshall Algorithm From Graph Theory, Applied to Parsing Molecular Structures
towardsdatascience.com
-
If you've been hearing the recent buzz around Mamba and state-space models and wanted to explore the topic in greater depth, don't miss Sascha Kirch's patiently explained series — part 2 dives deep into the inner workings of state space models.
Structured State Space Models Visually Explained
towardsdatascience.com
-
If you're curious to learn about linear programming and its problem-solving potential, head right over to Jarom Hulet's latest guide, which shows how this technique can be used on a classic stock-cutting problem.
Linear Programming: The Stock Cutting Problem
-
Don't miss Raúl Vizcarra Chirinos's latest deep dive, which covers "some challenges that emerge when discussing privacy in terms of AI, including a brief overview of Machine Unlearning and the SISA training approach (Sharded, Isolated, Sliced, and Aggregated training)."
Learning to Unlearn: Why Data Scientists and AI Practitioners Should Understand Machine Unlearning
towardsdatascience.com