Artificial Intelligence

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The AI research winery in India is the pillar of the laboratories. AI is seen to be the core of this transformation in all the systems, starting with healthcare and going through agriculture, education, and urban planning, and the Indian research labs are the engines of this rapid transformation. Literally, everything you need to know about the elite AI research lab centers in India are one step crucial to your living. These centers pave the way not only for cutting-edge research but also for the smartest contribution to the AI revolution in India. For students in their final years of graduate studies and young professionals looking to pursue a Ph.D. in AI or launch an AI startup, understanding the top AI research labs in India is crucial. Read more......

Here are 8 concise tips to help you ace a technical AI engineering interview: 𝟭. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝗟𝗟𝗠 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 - Cover the high-level workings of models like GPT-3, including transformers, pre-training, fine-tuning, etc. 𝟮. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 - Talk through techniques like demonstrations, examples, and plain language prompts to optimize model performance. 𝟯. 𝗦𝗵𝗮𝗿𝗲 𝗟𝗟𝗠 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 - Walk through hands-on experiences leveraging models like GPT-4, Langchain, or Vector Databases. 𝟰. 𝗦𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱 𝗼𝗻 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 - Mention latest papers and innovations in few-shot learning, prompt tuning, chain of thought prompting, etc. 𝟱. 𝗗𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗺𝗼𝗱𝗲𝗹 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 - Compare transformer networks like GPT-3 vs Codex. Explain self-attention, encodings, model depth, etc. 𝟲. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 - Explain supervised fine-tuning, parameter efficient fine tuning, few-shot learning, and other methods to specialize pre-trained models for specific tasks. 𝟳. 𝗗𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 - From tokenization to embeddings to deployment, showcase your ability to operationalize models at scale. 𝟴. 𝗔𝘀𝗸 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 - Inquire about model safety, bias, transparency, generalization, etc. to show strategic thinking.

How to Develop an AI Powered Mobile App Are you ready to dive into the world of artificial intelligence and mobile app development? In the ever-changing tech landscape of India, the development of an AI-powered mobile app is becoming a necessity for both wannabe developers as well as the experienced ones. In this guide, we’ll focus on the steps to build an app with AI, setting out the challenges and prospects faced in the market. (AI App) Access Full Guide to create an AI app

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Andrew Ng just released two new AI Python courses for beginners! The course teaches how to write code using AI. If you're thinking about learning to code, now is the perfect time to do so. https://deeplearning.ai/short-courses/ai-python-for-beginners/

Will LLMs always hallucinate? As large language models (LLMs) become more powerful and pervasive, it's crucial that we understand their limitations. A new paper argues that hallucinations - where the model generates false or nonsensical information - are not just occasional mistakes, but an inherent property of these systems. While the idea of hallucinations as features isn't new, the researchers' explanation is. They draw on computational theory and Gödel's incompleteness theorems to show that hallucinations are baked into the very structure of LLMs. In essence, they argue that the process of training and using these models involves undecidable problems - meaning there will always be some inputs that cause the model to go off the rails. This would have big implications. It suggests that no amount of architectural tweaks, data cleaning, or fact-checking can fully eliminate hallucinations. So what does this mean in practice? For one, it highlights the importance of using LLMs carefully, with an understanding of their limitations. It also suggests that research into making models more robust and understanding their failure modes is crucial. No matter how impressive the results, LLMs are not oracles - they're tools with inherent flaws and biases LLM & Generative AI Resources: https://t.me/generativeai_gpt

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Python Learning Plan in 2024 |-- Week 1: Introduction to Python | |-- Python Basics | | |-- What is Python? | | |-- Installing Python | | |-- Introduction to IDEs (Jupyter, VS Code) | |-- Setting up Python Environment | | |-- Anaconda Setup | | |-- Virtual Environments | | |-- Basic Syntax and Data Types | |-- First Python Program | | |-- Writing and Running Python Scripts | | |-- Basic Input/Output | | |-- Simple Calculations | |-- Week 2: Core Python Concepts | |-- Control Structures | | |-- Conditional Statements (if, elif, else) | | |-- Loops (for, while) | | |-- Comprehensions | |-- Functions | | |-- Defining Functions | | |-- Function Arguments and Return Values | | |-- Lambda Functions | |-- Modules and Packages | | |-- Importing Modules | | |-- Standard Library Overview | | |-- Creating and Using Packages | |-- Week 3: Advanced Python Concepts | |-- Data Structures | | |-- Lists, Tuples, and Sets | | |-- Dictionaries | | |-- Collections Module | |-- File Handling | | |-- Reading and Writing Files | | |-- Working with CSV and JSON | | |-- Context Managers | |-- Error Handling | | |-- Exceptions | | |-- Try, Except, Finally | | |-- Custom Exceptions | |-- Week 4: Object-Oriented Programming | |-- OOP Basics | | |-- Classes and Objects | | |-- Attributes and Methods | | |-- Inheritance | |-- Advanced OOP | | |-- Polymorphism | | |-- Encapsulation | | |-- Magic Methods and Operator Overloading | |-- Design Patterns | | |-- Singleton | | |-- Factory | | |-- Observer | |-- Week 5: Python for Data Analysis | |-- NumPy | | |-- Arrays and Vectorization | | |-- Indexing and Slicing | | |-- Mathematical Operations | |-- Pandas | | |-- DataFrames and Series | | |-- Data Cleaning and Manipulation | | |-- Merging and Joining Data | |-- Matplotlib and Seaborn | | |-- Basic Plotting | | |-- Advanced Visualizations | | |-- Customizing Plots | |-- Week 6-8: Specialized Python Libraries | |-- Web Development | | |-- Flask Basics | | |-- Django Basics | |-- Data Science and Machine Learning | | |-- Scikit-Learn | | |-- TensorFlow and Keras | |-- Automation and Scripting | | |-- Automating Tasks with Python | | |-- Web Scraping with BeautifulSoup and Scrapy | |-- APIs and RESTful Services | | |-- Working with REST APIs | | |-- Building APIs with Flask/Django | |-- Week 9-11: Real-world Applications and Projects | |-- Capstone Project | | |-- Project Planning | | |-- Data Collection and Preparation | | |-- Building and Optimizing Models | | |-- Creating and Publishing Reports | |-- Case Studies | | |-- Business Use Cases | | |-- Industry-specific Solutions | |-- Integration with Other Tools | | |-- Python and SQL | | |-- Python and Excel | | |-- Python and Power BI | |-- Week 12: Post-Project Learning | |-- Python for Automation | | |-- Automating Daily Tasks | | |-- Scripting with Python | |-- Advanced Python Topics | | |-- Asyncio and Concurrency | | |-- Advanced Data Structures | |-- Continuing Education | | |-- Advanced Python Techniques | | |-- Community and Forums | | |-- Keeping Up with Updates | |-- Resources and Community | |-- Online Courses (Coursera, edX, Udemy) | |-- Books (Automate the Boring Stuff, Python Crash Course) | |-- Python Blogs and Podcasts | |-- GitHub Repositories | |-- Python Communities (Reddit, Stack Overflow) Here you can find essential Python Interview Resources👇 https://topmate.io/analyst/907371 Like this post for more resources like this 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

⚡️ OpenAI released a new OpenAI o1 model - it is 5-6 (!) times better than GPT-4o This is the secret project the developers have been working on for so long. The new model shows itself 5 times better in math problems and 6 times better in writing code! This insane boost in quality is due to the fact that the model THINKS before giving you the answer. Access starts being granted TODAY.

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BEST AI RESEARCH PAPER SUMMARIZERS

Paperguide - Provides tools for extracting key insights, managing references, and annotating documents within PDFs to enhance study and research. Tenorshare AI PDF Tool - Quickly analyzes and condenses papers using AI, and features an interactive chat interface powered by ChatGPT. Elicit - Improves how users find and summarize academic papers using intelligent search and natural language processing to generate concise summaries. QuillBot - Leverages AI for its Summarizer tool to analyze documents and generate extractive summaries, customizing length and format. Semantic Scholar - An AI-powered academic search engine that generates one-sentence paper summaries and identifies influential citations. IBM Watson Discovery - Harnesses cognitive computing to understand context within texts and enable precise searches across large document libraries for summarization.

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