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But what is a neural network? | Deep learning chapter 1

What are the neurons, why are there layers, and what is the math underlying it?

Typo correction: At 14 minutes 45 seconds, the last index on the bias vector is n, when it’s supposed to in fact be a k. Thanks for the sharp eyes that caught that!

There are two neat things about this book. First, it’s available for free, so consider joining me in making a donation to Nielsen if you get something out of it. And second, it’s centered around walking through some code and data which you can download yourself, and which covers the same example that I introduce in this video. Yay for active learning!

https://github.com/mnielsen/neural-networks-and-deep-learning

IISc-IBM AI Workshop | 04th Dec 2024 | Session 1 (9:30 am – 11:00 am)

Dec 3, 2024
One-day workshop on topics in Generative AI

IISc-IBM AI Day is being jointly organized by the Centre for Networked Intelligence (with support from Cisco CSR) and IBM-IISc Hybrid Cloud Lab, in collaboration with IBM India Research Lab.

The goal of this workshop would be to apprise the audience of Generative AI, a set of toolkits from IBM for GenAI, and various LLMs from IBM for diverse applications.

🗓️ Date
December 04, 2024 (9am-4:30pm)

đź“Ť Venue
In-person: Room MP-20, ECE Department, Indian Institute of Science Campus, Bengaluru (đź“Ś Map location)
Online: link will be shared with registered participants.

đź‘Ş Target audience
The workshop is targeted at engineering/science students (pursuing graduate/undergraduate level educational program in India), keen on learning the application of Gen-AI in diverse domains and building systems for Gen-AI.

Yann LeCun: Towards Machines That Can Understand, Reason, & Plan

https://www.youtube.com/watch?v=_JfEScYyVCE

Panel Discussion: Open Questions in Theory of Learning

In a society that is confronting the new age of AI in which LLMs begin to display aspects of human intelligence, understanding the fundamental theory of deep learning and applying it to real systems is a compelling and urgent need.

This panel will introduce some new simple foundational results in the theory of supervised learning. It will also discuss open problems in the theory of learning, including problems specific to neuroscience.

Moderator: Tomaso Poggio – Professor of Brain and Cognitive Sciences, MIT
Panelists:
Ila Fiete – Professor of Brain and Cognitive Sciences, MIT
Haim Sompilinski – Professor of Molecular and Cellular Biology and of Physics, Harvard University
Eran Malach – Research fellow, Kempner Institute at Harvard University
Philip Isola – Associate Professor, EECS at MIT

Jeff Dean (Google): Exciting Trends in Machine Learning

Abstract: In this talk I’ll highlight several exciting trends in the field of AI and machine learning. Through a combination of improved algorithms and major efficiency improvements in ML-specialized hardware, we are now able to build much more capable, general purpose machine learning systems than ever before. As one example of this, I’ll give an overview of the Gemini family of multimodal models and their capabilities. These new models and approaches have dramatic implications for applying ML to many problems in the world, and I’ll highlight some of these applications in science, engineering, and health. This talk will present work done by many people at Google.

Bio: Jeff Dean joined Google in 1999 where he now serves as Google’s Chief Scientist, focusing on AI advances for Google DeepMind and Google Research. His areas of focus include machine learning and AI, and applications of AI to problems that help billions of people in societally beneficial ways. His work has been integral to many generations of Google’s search engine, its initial ad serving system, distributed computing infrastructure such as BigTable and MapReduce, the Tensorflow open-source machine learning system, as well as many libraries and developer tools.

Jeff received a Ph.D. in Computer Science from the University of Washington and a B.S. in Computer Science & Economics from the University of Minnesota. He is a member of the National Academy of Engineering and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM) and of the American Association for the Advancement of Sciences (AAAS), and a winner of the 2012 ACM Prize in Computing and the 2021 IEEE John von Neumann medal.

Overview Artificial Intelligence Course | Stanford CS221: Learn AI (Autumn 2019)

Topics: Overview of course, Optimization
Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University
http://onlinehub.stanford.edu/

Associate Professor Percy Liang
Associate Professor of Computer Science and Statistics (courtesy)

Assistant Professor Dorsa Sadigh
Assistant Professor in the Computer Science Department & Electrical Engineering Department

To follow along with the course schedule and syllabus, visit:
https://stanford-cs221.github.io/autumn2019/#schedule

artificialintelligencecourse

0:00 Introduction
3:30 Why AI?
15:10 AI as Agents
18:20 AI Tools
20:39 Biases
23:28 Summary
34:08 PacMan
43:11 Perquisites, Homework, Exams

Artificial Intelligence Tutorial | Artificial Intelligence Full Course | AI Tutorial

This video on the Artificial Intelligence tutorial will make you learn in detail about the different concepts involved in AI. You will understand the basics of AI and get an idea about Machine Learning and Deep Learning with hands-on demo in this Artificial Intelligence full course. You will look at how to become an AI Engineer and see some vital machine learning and deep learning interview questions. Now, let’s dive in and learn artificial intelligence in detail.

Below topics are explained in this Artificial Intelligence tutorial:

  1. Introduction to Artificial Intelligence (0:33)
  2. What is Artificial Intelligence (06:20)
  3. Brief history of Artificial Intelligence (07:16)
  4. Types of Artificial Intelligence (10:25)
  5. Artificial of Artificial Intelligence (13:23)
  6. Future of Artificial Intelligence (14:56)
  7. Machine Learning vs Deep Learning vs Artificial Intelligence (16:15)
  8. Human vs Artificial Intelligence (19:36)
  9. What is Machine Learning and Deep Learning? (21:33)
  10. Real-life examples (31:31)
  11. Types of Artificial Intelligence (33:49)
  12. Machine Learning tutorial (42:38)
  13. Why Machine Learning (43:12)
  14. What is Machine Learning (47:19)
  15. Types of Machine Learning (54:01)
  16. Supervised Learning (54:13)
  17. Reinforcement Learning (56:35)
  18. Supervised vs Unsupervised (57:54)
  19. Machine Learning Algorithms (59:12)
  20. Linear regression (1:01:00)
  21. Decision trees (1:08:12)
  22. Support Vector Machine (1:16:31)
  23. Clustering (1:44:56)
  24. K-means clustering (1:45:45)
  25. Logistic Regression (2:15:19)
  26. Applications of Machine Learning (2:39:40)
  27. What is Deep Learning? (2:44:36)
  28. What is a Neural Network? (2:46:46)
  29. Machine Learning Interview Questions & Answers (0:20:50)

Practical LLM Security: Takeaways From a Year in the Trenches

Oct 9, 2024
As LLMs are being integrated into more and more applications, security standards for these integrations have lagged behind. Most security research either focuses 1) on social harms, biases exhibited by LLMs, and other content moderation tasks, or 2) zooms in on the LLM itself and ignores the applications that are built around them. Investigating traditional security properties such as confidentiality, integrity, or availability for the entire integrated application has received less attention, yet in practice, we find that this is where the majority of non-transferable risk lies with LLM applications.

NVIDIA has implemented dozens of LLM powered applications, and the NVIDIA AI Red Team has helped secure all of them. We will present our practical findings around LLM security: what kinds of attacks are most common and most impactful, how to assess LLM integrations most effectively from a security perspective, and how we both think about mitigation and design integrations to be more secure from first principles.

Full Abstract & Presentation Materials:

https://www.blackhat.com/us-24/briefings/schedule/index.html#practical-llm-security-takeaways-from-a-year-in-the-trenches-39468

Reliable, fully local RAG agents with LLaMA3.2-3b

LLaMA3.2 has released a new set of compact models designed for on-device use cases, such as locally running assistants. Here, we show how LangGraph can enable these types of local assistant by building a multi-step RAG agent – this combines ideas from 3 advanced RAG papers (Adaptive RAG, Corrective RAG, and Self-RAG) into a single control flow using LangGraph. But we show LangGraph makes it possible to run a complex agent locally.

Code:

Llama3.2:

https://www.youtube.com/watch?v=bq1Plo2RhYI

LangChain vs LangGraph: A Tale of Two Frameworks

Get ready for a showdown between LangChain and LangGraph, two powerful frameworks for building applications with large language models (LLMs.) Master Inventor Martin Keen compares the two, taking a look at their unique features, use cases, and how they can help you create innovative, context-aware solutions.

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