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In this video on "React JS Full Course 2023," We'll explain to you React js from basic to advance. We will cover topics like what are components, Nested components, Props in React js, and React methods, after that, we will create two projects using react js, the first one is a portfolio project and the next one is an Instagram clone. And by the end of this full course, we will be covering some of the interview questions on react js.

00:00:00 - React JS Full Course 2023
00:01:49 - What is React js?
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00:32:35 - Component lifecycle
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00:51:04 - React with Redux
00:58:52 - Portfolio Project using React js
02:26:00 - Instagram clone using React js
04:45:39 - React 18 new features
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05:08:55 - React vs Angular Js
05:25:14 - Interview Questions and answer

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About Caltech Coding Bootcamp
This Caltech Coding Bootcamp will help you master both front-end and back-end Java technologies, starting with the basics and progressing to the advanced aspects of Full Stack Web Development. In this Online Coding boot camp, you will learn Angular, Spring Boot, web services, JSPs, and MongoDB to help you launch your career as a full-stack developer.

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Learn how to build and deploy a SaaS using NextJS 13.4, DrizzleORM, OpenAI, Stripe, TypeScript, Tailwind, and Vercel. You will gain expertise in the following areas:
- Leveraging NextJS 13's cutting-edge App Router.
- Managing Payments seamlessly through Stripe.
- Harnessing the beauty of Shadcn and the power of Tailwind CSS.
- Unleashing the capabilities of OpenAI's API for Language Model usage.
- Interacting with databases with the efficiency of ORMs.

Resources
🔗 GitHub Repo: https://github.com/elliott-chong/chatpdf-yt
🔗 Vercel AI SDK: https://vercel.com/blog/introd....ucing-the-vercel-ai-
🔗 Timestamp 3:20:00 /api/chat/route.ts file https://pastebin.com/crLipeHK

Course developed by @elliottchong

⭐️ Contents ⭐️
⌨️ (0:00:00) Intro & Demo
⌨️ (0:09:53) Set Up NextJS
⌨️ (0:11:59) Set Up Shadcn
⌨️ (0:15:45) Set Up ClerkAuth
⌨️ (0:21:52) Home Page
⌨️ (0:33:22) DrizzleORM
⌨️ (0:50:15) File Upload Component
⌨️ (0:57:11) Set Up AWS S3
⌨️ (1:18:12) AI RAG Explanation
⌨️ (1:30:39) React Query Set Up
⌨️ (1:45:11) Set Up Pinecone DB
⌨️ (2:32:27) Chat Side Bar
⌨️ (2:47:42) PDF Viewer
⌨️ (2:50:33) Chat Component ( Vercel AI SDK )
⌨️ (3:08:02) Pinecone OpenAI Get Context
⌨️ (3:26:19) Persist Chat Logs to DB
⌨️ (3:34:16) Stripe Integration
⌨️ (4:14:59) Deploy and Outro

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Data analytics has been rapidly growing, with companies looking to generate insights and drive their business with the help of data. In this Data Analytics Full Course video, you will learn what data analytics is, why data analytics is necessary, the types of data analytics, and the various data analytics applications. You will then understand a case study and perform analysis of data using Python and R. In addition to that; we will see the top 10 data analysis tools and understand the difference between a data scientist and a data analyst. Finally, we’ll see the top interview questions that will help you crack a data analyst interview.

Dataset Link - https://drive.google.com/drive..../folders/1U8CF0Xx3Ni

00:02:09 Data Analytics for Beginners
00:03:01 What is Data Analytics
00:04:30 Steps involved in Data Analytics
00:06:25 Tools used in Data Analytics
00:10:00 Data analytics applications
00:16:35 companies using data analytics
00:17:15 case study
00:21:15 Who is a data analyst
00:21:53 Top 10 skills to become a Data analyst in 2022
00:28:54 Top 10 Analysis Tools
00:47:10 Data Analytics Using Phyton
02:34:14 R tutorial
03:02:54 Time Series
04:13:12 Pivot table in Excel
04:41:20 Excel Dashboarding
06:12:03 Covid 19
06:53:31 Spotify
07:34:18 World Happiness Report
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09:27:32 Data Analyst Interview Questions

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LangChain became immensely popular when it was launched in 2022, but how can it impact your development and application of AI models, Large Language Models (LLM) in particular. In this video Martin Keen shares an overview of the features and uses of LangChain.

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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐂𝐨𝐮𝐫𝐬𝐞 - 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 𝐭𝐨 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝: https://www.edureka.co/openai-....chatgpt-training-cou
This 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥 is intended as a Crash Course on 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 for Beginners. 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 has been growing in popularity exponentially. But, 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 is still not known to many people. In this video, I aim to show you the different ways in which you can use 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 for yourself.

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"Symmetry, as wide or narrow as you may define its meaning, is one idea by which man through the ages has tried to comprehend and create order, beauty, and perfection." and that was a quote from Hermann Weyl, a German mathematician who was born in the late 19th century.

The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Many high-dimensional learning tasks previously thought to be beyond reach -- such as computer vision, playing Go, or protein folding -- are in fact tractable given enough computational horsepower. Remarkably, the essence of deep learning is built from two simple algorithmic principles: first, the notion of representation or feature learning and second, learning by local gradient-descent type methods, typically implemented as backpropagation.

While learning generic functions in high dimensions is a cursed estimation problem, many tasks are not uniform and have strong repeating patterns as a result of the low-dimensionality and structure of the physical world.

Geometric Deep Learning unifies a broad class of ML problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way to construct new types of problem-specific inductive biases.

This week we spoke with Professor Michael Bronstein (head of graph ML at Twitter) and Dr.
Petar Veličković (Senior Research Scientist at DeepMind), and Dr. Taco Cohen and Prof. Joan Bruna about their new proto-book Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

Enjoy the show!

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
https://arxiv.org/abs/2104.13478

[00:00:00] Tim Intro
[00:01:55] Fabian Fuchs article
[00:04:05] High dimensional learning and curse
[00:05:33] Inductive priors
[00:07:55] The proto book
[00:09:37] The domains of geometric deep learning
[00:10:03] Symmetries
[00:12:03] The blueprint
[00:13:30] NNs don't deal with network structure (TedX)
[00:14:26] Penrose - standing edition
[00:15:29] Past decade revolution (ICLR)
[00:16:34] Talking about the blueprint
[00:17:11] Interpolated nature of DL / intelligence
[00:21:29] Going tack to Euclid
[00:22:42] Erlangen program
[00:24:56] “How is geometric deep learning going to have an impact”
[00:26:36] Introduce Michael and Petar
[00:28:35] Petar Intro
[00:32:52] Algorithmic reasoning
[00:36:16] Thinking fast and slow (Petar)
[00:38:12] Taco Intro
[00:46:52] Deep learning is the craze now (Petar)
[00:48:38] On convolutions (Taco)
[00:53:17] Joan Bruna's voyage into geometric deep learning
[00:56:51] What is your most passionately held belief about machine learning? (Bronstein)
[00:57:57] Is the function approximation theorem still useful? (Bruna)
[01:11:52] Could an NN learn a sorting algorithm efficiently (Bruna)
[01:17:08] Curse of dimensionality / manifold hypothesis (Bronstein)
[01:25:17] Will we ever understand approximation of deep neural networks (Bruna)
[01:29:01] Can NNs extrapolate outside of the training data? (Bruna)
[01:31:21] What areas of math are needed for geometric deep learning? (Bruna)
[01:32:18] Graphs are really useful for representing most natural data (Petar)
[01:35:09] What was your biggest aha moment early (Bronstein)
[01:39:04] What gets you most excited? (Bronstein)
[01:39:46] Main show kick off + Conservation laws
[01:49:10] Graphs are king
[01:52:44] Vector spaces vs discrete
[02:00:08] Does language have a geometry? Which domains can geometry not be applied? +Category theory
[02:04:21] Abstract categories in language from graph learning
[02:07:10] Reasoning and extrapolation in knowledge graphs
[02:15:36] Transformers are graph neural networks?
[02:21:31] Tim never liked positional embeddings
[02:24:13] Is the case for invariance overblown? Could they actually be harmful?
[02:31:24] Why is geometry a good prior?
[02:34:28] Augmentations vs architecture and on learning approximate invariance
[02:37:04] Data augmentation vs symmetries (Taco)
[02:40:37] Could symmetries be harmful (Taco)
[02:47:43] Discovering group structure (from Yannic)
[02:49:36] Are fractals a good analogy for physical reality?
[02:52:50] Is physical reality high dimensional or not?
[02:54:30] Heuristics which deal with permutation blowups in GNNs
[02:59:46] Practical blueprint of building a geometric network architecture
[03:01:50] Symmetry discovering procedures
[03:04:05] How could real world data scientists benefit from geometric DL?
[03:07:17] Most important problem to solve in message passing in GNNs
[03:09:09] Better RL sample efficiency as a result of geometric DL (XLVIN paper)
[03:14:02] Geometric DL helping latent graph learning
[03:17:07] On intelligence
[03:23:52] Convolutions on irregular objects (Taco)

GPT-3 has taken the world by storm, and for good reason: It lets you generate text with just a few prompts (i.e. 'few-shot learning') or even with no prompts and just the instruction (i.e. 'zero-shot learning'). But there are scenarios where the model does not deliver satisfying results out of the box. In these cases we need to train the GPT-3 model with our own dataset to improve the performance. In this talk he explored how this can be done and we saw a real-life application of this.

This speech by Heiko Hotz was held on November 18 at Data Science Conference Europe 2022 in person in Belgrade.

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Change the World Through Data

#datascience #ai #ml #artificialintelligence #bigdata #dataanalytics #gpt3

Ms. Coffee Bean explains how a huge collaboration of researchers managed to extract training data from large language models like GPT-2. Why is this even possible and what does this mean for even larger models like GPT-3?
Discussed paper:
" Extracting Training Data from Large Language Models" explained by Carlini et al. 2020.

▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
🔥 Optionally, pay us a coffee to boost our Coffee Bean production! ☕
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▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀

📺 GPT-3 – what you need to know: https://youtu.be/5fqxPOaaqi0

Outline:
* 00:00 Large Language Models
* 01:55 GPT-2
* 02:33 Why is it possible?
* 03:38 k-eidetic memorization
* 04:21 How does the attack work?
* 05:49 How bad is it?
* 08:12 What to do?

📄 Paper explained: Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., ... & Oprea, A. (2020). Extracting Training Data from Large Language Models. arXiv preprint arXiv:2012.07805. https://arxiv.org/pdf/2012.07805.pdf

-------------------------------------------------------------
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#AICoffeeBreak #MsCoffeeBean #GPT #MachineLearning #AI #research

Learn about 10 security vulnerabilities every JavaScript developer should know. First try to find the vulnerabilities in the different code examples. Then learn how to fix the issues.

✏️ Brandon from Semgrep developed this course.

🔗 Learn more about Semgrep: https://go.semgrep.dev/scan-in-1-minute-for-free
🏗 Semgrep provided a grant to make this course possible.



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How to build your own Tiny Language Model from scratch - Luca Gilli - PyCon Italia 2025

Elevator Pitch:
Join us to discover Tiny Language Models: ultra-compact, resource-efficient AI tools that deliver impressive results. In this session, you’ll learn how to create your own model, from data preparation to training. These models combine innovation with efficiency, making AI more accessible than ever.

Learn more: https://2025.pycon.it/event/ho....w-to-build-your-own-

#MachineLearning #DeepLearning #NaturalLanguageProcessing

Recorded live at the Agent Engineering Session Day from the AI Engineer Summit 2025 in New York. Learn more at https://ai.engineer and purchase tickets to our next event, the AI Engineer World's Fair, in SF June 3 - 5 here: https://ti.to/software-3/ai-en....gineer-worlds-fair-2

About Barry:

Barry is a member of technical staff on Anthropic's Applied AI team, focusing on developing agentic systems with enterprises and startups. Previously, he was a tech lead on the Monetization genAI team at Meta, where he claimed the inaugural 'AI Engineer' title. He holds degrees in Computer Science and Industrial Engineering from Northwestern