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Your AI second brain Docs • Web • App • Discord • ✍ Blog Khoj is a personal AI app to extend your capabilities. It smoothly scales up from an on-device personal AI to a cloud-scale enterprise AI. Chat with any local or online LLM (e.g llama3, qwen, gemma, mistral, gpt, claude, gemini). Get answers from the internet and your docs (including image, pdf, markdown, org-mode, word, notion files). Access it from your Browser, Obsidian, Emacs, Desktop, Phone or Whatsapp. Create agents with custom knowledge, persona, chat model and tools to take on any role. Automate away repetitive research. Get personal newsletters and smart notifications delivered to your inbox. Find relevant docs quickly and easily using our advanced semantic search. Generate images, talk out loud, play your messages. Khoj is open-source, self-hostable. Always. Run it privately on your computer or try it on our cloud app. See it in action Go to [Hidden Content] to see Khoj live. Full feature list You can see the full feature list here. Self-Host To get started with self-hosting Khoj, read the docs. GitHub: [Hidden Content]

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FinGPT: Open-Source Financial Large Language Models Let us not expect Wall Street to open-source LLMs or open APIs, due to FinTech institutes' internal regulations and policies. Blueprint of FinGPT [Hidden Content] What's New: [Model Release] Nov, 2023: We release FinGPT-Forecaster! Demo, Medium Blog & Model are available on Huggingface! [Paper Acceptance] Oct, 2023: "FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets" is accepted by Instruction Workshop @ NeurIPS 2023 [Paper Acceptance] Oct, 2023: "FinGPT: Democratizing Internet-scale Data for Financial Large Language Models" is accepted by Instruction Workshop @ NeurIPS 2023 [Model Release] Oct, 2023: We release the financial multi-task LLMs produced when evaluating base-LLMs on FinGPT-Benchmark [Paper Acceptance] Sep, 2023: "Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models" is accepted by ACM International Conference on AI in Finance (ICAIF-23) [Model Release] Aug, 2023: We release the financial sentiment analysis model [Paper Acceptance] Jul, 2023: "Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models" is accepted by FinLLM 2023@IJCAI 2023 [Paper Acceptance] Jul, 2023: "FinGPT: Open-Source Financial Large Language Models" is accepted by FinLLM 2023@IJCAI 2023 [Medium Blog] Jun 2023: FinGPT: Powering the Future of Finance with 20 Cutting-Edge Applications Why FinGPT? 1). Finance is highly dynamic. BloombergGPT trained an LLM using a mixture of finance data and general-purpose data, which took about 53 days, at a cost of around $3M). It is costly to retrain an LLM model like BloombergGPT every month or every week, thus lightweight adaptation is highly favorable. FinGPT can be fine-tuned swiftly to incorporate new data (the cost falls significantly, less than $300 per fine-tuning). 2). Democratizing Internet-scale financial data is critical, say allowing timely updates of the model (monthly or weekly updates) using an automatic data curation pipeline. BloombergGPT has privileged data access and APIs, while FinGPT presents a more accessible alternative. It prioritizes lightweight adaptation, leveraging the best available open-source LLMs. 3). The key technology is "RLHF (Reinforcement learning from human feedback)", which is missing in BloombergGPT. RLHF enables an LLM model to learn individual preferences (risk-aversion level, investing habits, personalized robo-advisor, etc.), which is the "secret" ingredient of ChatGPT and GPT4. Milestone of AI Robo-Advisor: FinGPT-Forecaster Try the latest released FinGPT-Forecaster demo at our HuggingFace Space The dataset for FinGPT-Forecaster: [Hidden Content] Enter the following inputs: ticker symbol (e.g. AAPL, MSFT, NVDA) the day from which you want the prediction to happen (yyyy-mm-dd) the number of past weeks where market news are retrieved whether to add the latest basic financials as additional information Click Submit！ And you'll be responded with a well-rounded analysis of the company and a prediction for next week's stock price movement! For detailed and more customized implementation, please refer to FinGPT-Forecaster FinGPT Demos: Current State-of-the-arts for Financial Sentiment Analysis FinGPT V3 (Updated on 10/12/2023) What's new: Best trainable and inferable FinGPT for sentiment analysis on a single RTX 3090, which is even better than GPT-4 and ChatGPT Finetuning. FinGPT v3 series are LLMs finetuned with the LoRA method on the News and Tweets sentiment analysis dataset which achieve the best scores on most of the financial sentiment analysis datasets with low cost. FinGPT v3.3 use llama2-13b as base model; FinGPT v3.2 uses llama2-7b as base model; FinGPT v3.1 uses chatglm2-6B as base model. Benchmark Results: Weighted F1 FPB FiQA-SA TFNS NWGI Devices Time Cost FinGPT v3.3 0.882 0.874 0.903 0.643 1 × RTX 3090 17.25 hours $17.25 FinGPT v3.2 0.850 0.860 0.894 0.636 1 × A100 5.5 hours $ 22.55 FinGPT v3.1 0.855 0.850 0.875 0.642 1 × A100 5.5 hours $ 22.55 FinGPT (8bit) 0.855 0.847 0.879 0.632 1 × RTX 3090 6.47 hours $ 6.47 FinGPT (QLoRA) 0.777 0.752 0.828 0.583 1 × RTX 3090 4.15 hours $ 4.15 OpenAI Fine-tune 0.878 0.887 0.883 - - - - GPT-4 0.833 0.630 0.808 - - - - FinBERT 0.880 0.596 0.733 0.538 4 × NVIDIA K80 GPU - - Llama2-7B 0.390 0.800 0.296 0.503 2048 × A100 21 days $ 4.23 million BloombergGPT 0.511 0.751 - - 512 × A100 53 days $ 2.67 million Cost per GPU hour. For A100 GPUs, the AWS p4d.24xlarge instance, equipped with 8 A100 GPUs is used as a benchmark to estimate the costs. Note that BloombergGPT also used p4d.24xlarge As of July 11, 2023, the hourly rate for this instance stands at $32.773. Consequently, the estimated cost per GPU hour comes to $32.77 divided by 8, resulting in approximately $4.10. With this value as the reference unit price (1 GPU hour). BloombergGPT estimated cost= 512 x 53 x 24 = 651,264 GPU hours x $4.10 = $2,670,182.40. For RTX 3090, we assume its cost per hour is approximately $1.0, which is actually much higher than available GPUs from platforms like vast.ai. Reproduce the results by running benchmarks, and the detailed tutorial is on the way. Finetune your own FinGPT v3 model with the LoRA method on only an RTX 3090 with this notebook in 8bit or this notebook in int4 (QLoRA) FinGPT V1 FinGPT by finetuning ChatGLM2 / Llama2 with LoRA with the market-labeled data for the Chinese Market Instruction Tuning Datasets and Models The datasets we used, and the multi-task financial LLM models are available at [Hidden Content] Our Code Datasets Train Rows Test Rows Description fingpt-sentiment-train 76.8K N/A Sentiment Analysis Training Instructions fingpt-finred 27.6k 5.11k Financial Relation Extraction Instructions fingpt-headline 82.2k 20.5k Financial Headline Analysis Instructions fingpt-ner 511 98 Financial Named-Entity Recognition Instructions fingpt-fiqa_qa 17.1k N/A Financial Q&A Instructions fingpt-fineval 1.06k 265 Chinese Multiple-Choice Questions Instructions Multi-task financial LLMs Models: demo_tasks = [ 'Financial Sentiment Analysis', 'Financial Relation Extraction', 'Financial Headline Classification', 'Financial Named Entity Recognition',] demo_inputs = [ "Glaxo's ViiV Healthcare Signs China Manufacturing Deal With Desano", "Apple Inc. Chief Executive Steve Jobs sought to soothe investor concerns about his health on Monday, saying his weight loss was caused by a hormone imbalance that is relatively simple to treat.", 'gold trades in red in early trade; eyes near-term range at rs 28,300-28,600', 'This LOAN AND SECURITY AGREEMENT dated January 27 , 1999 , between SILICON VALLEY BANK (" Bank "), a California - chartered bank with its principal place of business at 3003 Tasman Drive , Santa Clara , California 95054 with a loan production office located at 40 William St ., Ste .',] demo_instructions = [ 'What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}.', 'Given phrases that describe the relationship between two words/phrases as options, extract the word/phrase pair and the corresponding lexical relationship between them from the input text. The output format should be "relation1: word1, word2; relation2: word3, word4". Options: product/material produced, manufacturer, distributed by, industry, position held, original broadcaster, owned by, founded by, distribution format, headquarters location, stock exchange, currency, parent organization, chief executive officer, director/manager, owner of, operator, member of, employer, chairperson, platform, subsidiary, legal form, publisher, developer, brand, business division, location of formation, creator.', 'Does the news headline talk about price going up? Please choose an answer from {Yes/No}.', 'Please extract entities and their types from the input sentence, entity types should be chosen from {person/organization/location}.',] Models Description Function fingpt-mt_llama2-7b_lora Fine-tuned Llama2-7b model with LoRA Multi-Task fingpt-mt_falcon-7b_lora Fine-tuned falcon-7b model with LoRA Multi-Task fingpt-mt_bloom-7b1_lora Fine-tuned bloom-7b1 model with LoRA Multi-Task fingpt-mt_mpt-7b_lora Fine-tuned mpt-7b model with LoRA Multi-Task fingpt-mt_chatglm2-6b_lora Fine-tuned chatglm-6b model with LoRA Multi-Task fingpt-mt_qwen-7b_lora Fine-tuned qwen-7b model with LoRA Multi-Task fingpt-sentiment_llama2-13b_lora Fine-tuned llama2-13b model with LoRA Single-Task fingpt-forecaster_dow30_llama2-7b_lora Fine-tuned llama2-7b model with LoRA Single-Task Tutorials [Training] Beginner’s Guide to FinGPT: Training with LoRA and ChatGLM2–6B One Notebook, $10 GPU Understanding FinGPT: An Educational Blog Series FinGPT: Powering the Future of Finance with 20 Cutting-Edge Applications FinGPT I: Why We Built the First Open-Source Large Language Model for Finance FinGPT II: Cracking the Financial Sentiment Analysis Task Using Instruction Tuning of General-Purpose Large Language Models FinGPT Ecosystem FinGPT embraces a full-stack framework for FinLLMs with five layers: Data source layer: This layer assures comprehensive market coverage, addressing the temporal sensitivity of financial data through real-time information capture. Data engineering layer: Primed for real-time NLP data processing, this layer tackles the inherent challenges of high temporal sensitivity and low signal-to-noise ratio in financial data. LLMs layer: Focusing on a range of fine-tuning methodologies such as LoRA, this layer mitigates the highly dynamic nature of financial data, ensuring the model’s relevance and accuracy. Task layer: This layer is responsible for executing fundamental tasks. These tasks serve as the benchmarks for performance evaluations and cross-comparisons in the realm of FinLLMs Application layer: Showcasing practical applications and demos, this layer highlights the potential capability of FinGPT in the financial sector. FinGPT Framework: Open-Source Financial Large Language Models FinGPT-RAG: We present a retrieval-augmented large language model framework specifically designed for financial sentiment analysis, optimizing information depth and context through external knowledge retrieval, thereby ensuring nuanced predictions. FinGPT-FinNLP: FinNLP provides a playground for all people interested in LLMs and NLP in Finance. Here we provide full pipelines for LLM training and finetuning in the field of finance. The full architecture is shown in the following picture. Detail codes and introductions can be found here. Or you may refer to the wiki FinGPT-Benchmark: We introduce a novel Instruction Tuning paradigm optimized for open-source Large Language Models (LLMs) in finance, enhancing their adaptability to diverse financial datasets while also facilitating cost-effective, systematic benchmarking from task-specific, multi-task, and zero-shot instruction tuning tasks. Open-Source Base Model used in the LLMs layer of FinGPT Feel free to contribute more open-source base models tailored for various language-specific financial markets. Base Model Pretraining Tokens Context Length Model Advantages Model Size Experiment Results Applications Llama-2 2 Trillion 4096 Llama-2 excels on English-based market data llama-2-7b and Llama-2-13b llama-2 consistently shows superior fine-tuning results Financial Sentiment Analysis, Robo-Advisor Falcon 1,500B 2048 Maintains high-quality results while being more resource-efficient falcon-7b Good for English market data Financial Sentiment Analysis MPT 1T 2048 MPT models can be trained with high throughput efficiency and stable convergence mpt-7b Good for English market data Financial Sentiment Analysis Bloom 366B 2048 World’s largest open multilingual language model bloom-7b1 Good for English market data Financial Sentiment Analysis ChatGLM2 1.4T 32K Exceptional capability for Chinese language expression chatglm2-6b Shows prowess for Chinese market data Financial Sentiment Analysis, Financial Report Summary Qwen 2.2T 8k Fast response and high accuracy qwen-7b Effective for Chinese market data Financial Sentiment Analysis InternLM 1.8T 8k Can flexibly and independently construct workflows internlm-7b Effective for Chinese market data Financial Sentiment Analysis Benchmark Results for the above open-source Base Models in the financial sentiment analysis task using the same instruction template for SFT (LoRA): Weighted F1/Acc Llama2 Falcon MPT Bloom ChatGLM2 Qwen InternLM FPB 0.863/0.863 0.846/0.849 0.872/0.872 0.810/0.810 0.850/0.849 0.854/0.854 0.709/0.714 FiQA-SA 0.871/0.855 0.840/0.811 0.863/0.844 0.771/0.753 0.864/0.862 0.867/0.851 0.679/0.687 TFNS 0.896/0.895 0.893/0.893 0.907/0.907 0.840/0.840 0.859/0.858 0.883/0.882 0.729/0.731 NWGI 0.649/0.651 0.636/0.638 0.640/0.641 0.573/0.574 0.619/0.629 0.638/0.643 0.498/0.503 News Columbia Perspectives on ChatGPT [MIT Technology Review] ChatGPT is about to revolutionize the economy. We need to decide what that looks like [BloombergGPT] BloombergGPT: A Large Language Model for Finance [Finextra] ChatGPT and Bing AI to sit as panellists at fintech conference ChatGPT at AI4Finance [YouTube video] I Built a Trading Bot with ChatGPT, combining ChatGPT and FinRL. Hey, ChatGPT! Explain FinRL code to me! Introductory Sparks of artificial general intelligence: Early experiments with GPT-4 [GPT-4] GPT-4 Technical Report [InstructGPT] Training language models to follow instructions with human feedback NeurIPS 2022. The Journey of Open AI GPT models. GPT models explained. Open AI's GPT-1, GPT-2, GPT-3. [GPT-3] Language models are few-shot learners NeurIPS 2020. [GPT-2] Language Models are Unsupervised Multitask Learners [GPT-1] Improving Language Understanding by Generative Pre-Training [Transformer] Attention is All you Need NeurIPS 2017. (Financial) Big Data [BloombergGPT] BloombergGPT: A Large Language Model for Finance WHAT’S IN MY AI? A Comprehensive Analysis of Datasets Used to Train GPT-1, GPT-2, GPT-3, GPT-NeoX-20B, Megatron-11B, MT-NLG, and Gopher FinRL-Meta Repo and paper FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning. Advances in Neural Information Processing Systems, 2022. [AI4Finance] FinNLP Democratizing Internet-scale financial data. Interesting Demos GPT-3 Creative Fiction Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors. ChatGPT for FinTech ChatGPT Trading Bot [YouTube video] ChatGPT Trading strategy 20097% returns [YouTube video] ChatGPT Coding - Make A Profitable Trading Strategy In Five Minutes! [YouTube video] Easy Automated Live Trading using ChatGPT (+9660.3% hands free) [YouTube video] ChatGPT Trading Strategy 893% Returns [YouTube video] ChatGPT 10 Million Trading Strategy [YouTube video] ChatGPT: Your Crypto Assistant [YouTube video] Generate Insane Trading Returns with ChatGPT and TradingView Citing FinGPT News Columbia Perspectives on ChatGPT [MIT Technology Review] ChatGPT is about to revolutionize the economy. We need to decide what that looks like [BloombergGPT] BloombergGPT: A Large Language Model for Finance [Finextra] ChatGPT and Bing AI to sit as panellists at fintech conference ChatGPT at AI4Finance [YouTube video] I Built a Trading Bot with ChatGPT, combining ChatGPT and FinRL. Hey, ChatGPT! Explain FinRL code to me! Introductory Sparks of artificial general intelligence: Early experiments with GPT-4 [GPT-4] GPT-4 Technical Report [InstructGPT] Training language models to follow instructions with human feedback NeurIPS 2022. The Journey of Open AI GPT models. GPT models explained. Open AI's GPT-1, GPT-2, GPT-3. [GPT-3] Language models are few-shot learners NeurIPS 2020. [GPT-2] Language Models are Unsupervised Multitask Learners [GPT-1] Improving Language Understanding by Generative Pre-Training [Transformer] Attention is All you Need NeurIPS 2017. (Financial) Big Data [BloombergGPT] BloombergGPT: A Large Language Model for Finance WHAT’S IN MY AI? A Comprehensive Analysis of Datasets Used to Train GPT-1, GPT-2, GPT-3, GPT-NeoX-20B, Megatron-11B, MT-NLG, and Gopher FinRL-Meta Repo and paper FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning. Advances in Neural Information Processing Systems, 2022. [AI4Finance] FinNLP Democratizing Internet-scale financial data. Interesting Demos GPT-3 Creative Fiction Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors. ChatGPT for FinTech ChatGPT Trading Bot [YouTube video] ChatGPT Trading strategy 20097% returns [YouTube video] ChatGPT Coding - Make A Profitable Trading Strategy In Five Minutes! [YouTube video] Easy Automated Live Trading using ChatGPT (+9660.3% hands free) [YouTube video] ChatGPT Trading Strategy 893% Returns [YouTube video] ChatGPT 10 Million Trading Strategy [YouTube video] ChatGPT: Your Crypto Assistant [YouTube video] Generate Insane Trading Returns with ChatGPT and TradingView Citing FinGPT @article{yang2023fingpt, title={FinGPT: Open-Source Financial Large Language Models}, author={Yang, Hongyang and Liu, Xiao-Yang and Wang, Christina Dan}, journal={FinLLM Symposium at IJCAI 2023}, year={2023} } @article{zhang2023instructfingpt, title={Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models}, author={Boyu Zhang and Hongyang Yang and Xiao-Yang Liu}, journal={FinLLM Symposium at IJCAI 2023}, year={2023} } @article{zhang2023fingptrag, title={Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models}, author={Zhang, Boyu and Yang, Hongyang and Zhou, tianyu and Babar, Ali and Liu, Xiao-Yang}, journal = {ACM International Conference on AI in Finance (ICAIF)}, year={2023} } @article{wang2023fingptbenchmark, title={FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets}, author={Wang, Neng and Yang, Hongyang and Wang, Christina Dan}, journal={NeurIPS Workshop on Instruction Tuning and Instruction Following}, year={2023} } @article{2023finnlp, title={Data-centric FinGPT: Democratizing Internet-scale Data for Financial Large Language Models}, author={Liu, Xiao-Yang and Wang, Guoxuan and Yang, Hongyang and Zha, Daochen}, journal={NeurIPS Workshop on Instruction Tuning and Instruction Following}, year={2023} } LICENSE MIT License Disclaimer: We are sharing codes for academic purposes under the MIT education license. Nothing herein is financial advice, and NOT a recommendation to trade real money. Please use common sense and always first consult a professional before trading or investing. GitHub: [Hidden Content]

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New AI Initiative Meta is working on developing its own AI search engine designed to provide conversational answers about current events, aiming to integrate this functionality with its Meta AI chatbot. Reducing Reliance This move is intended to lessen Meta’s dependence on external search engines like Google and Microsoft Bing, which currently supply information on news, sports, and stocks for the Meta AI platform. Strategic Backup The new search engine will serve as a backup solution for Meta, ensuring they are not reliant on external partners if those companies choose to withdraw their services. Competitive Landscape As Meta competes with companies like OpenAI in the AI space, developing its own search capabilities is crucial for maintaining a competitive edge in providing information and enhancing user experience. Future Implications This initiative could reshape how users interact with information on Meta’s platforms, potentially leading to more personalized and contextually relevant search results. Read more at: The Information | Reuters

This week Google-backed Anthropic announced its upgraded AI model Claude 3.5 Sonnet could "perform tasks like navigating web browsers, filling forms, and manipulating data." Now Google plans something similar for Chrome, reports 9to5Linux.com:According to The Information, Google is "developing artificial intelligence that takes over a person's web browser to complete tasks such as gathering research, purchasing a product or booking a flight." "Project Jarvis" — in a nod to J.A.R.V.I.S. in Iron Man — would operate in Google Chrome and is a consumer-facing (rather than enterprise) feature to "automate everyday, web-based tasks." The article doesn't specify whether this would be for mobile or desktop... Given a command/action, Jarvis works by taking "frequent screenshots of what's on their computer screen, and interpreting the shots before taking actions like clicking on a button or typing into a text field." The Information reports that Google "plans to preview the product, also known as a computer-using agent, as early as December alongside the release of its next flagship Gemini large language model, which would help power the product, two of the people said." Source: 9to5google

Official Announcement OpenAI has stated it will not release the AI model code-named Orion this year, despite previous reports suggesting otherwise. Clarification from Spokesperson A spokesperson clarified to TechCrunch that there are no plans for Orion’s launch this year, emphasizing a focus on other technologies instead. Conflicting Reports Recent reports indicated that Orion might be OpenAI’s next major model, with expectations for a December launch and previews for trusted partners like Microsoft. Microsoft’s Anticipation Microsoft, a key collaborator with OpenAI, was expected to gain early access to Orion as soon as November, raising questions about the product timeline. Future Developments While Orion is on hold, OpenAI reassures users of upcoming releases of other innovative technologies. Read more at: TechCrunch

Introduction of SynthID Google has made its SynthID watermarking technology open source, allowing developers and businesses to embed imperceptible watermarks in AI-generated content. Purpose of the Toolkit This toolkit enables the AI industry to mark content as artificially generated, providing a means to detect deepfakes and other misleading AI content before it spreads. Watermarking Techniques SynthID is used across various media formats, including audio, video, and images, employing different techniques for each to ensure reliable detection. Methodology Explained A recent Nature paper details how SynthID embeds watermarks into text outputs from the Gemini AI model using a sampling algorithm that influences token selection during text generation. Limitations to Consider Despite its potential, experts note several limitations that may hinder the widespread adoption of AI watermarking as a standard practice in the industry. Read more at: Ars Technica

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Artificial Intelligence for Beginners - A Curriculum AI For Beginners - Sketchnote by @girlie_mac Explore the world of Artificial Intelligence (AI) with our 12-week, 24-lesson curriculum! It includes practical lessons, quizzes, and labs. The curriculum is beginner-friendly and covers tools like TensorFlow and PyTorch, as well as ethics in AI What you will learn Mindmap of the Course In this curriculum, you will learn: Different approaches to Artificial Intelligence, including the "good old" symbolic approach with Knowledge Representation and reasoning (GOFAI). Neural Networks and Deep Learning, which are at the core of modern AI. We will illustrate the concepts behind these important topics using code in two of the most popular frameworks - TensorFlow and PyTorch. Neural Architectures for working with images and text. We will cover recent models but may be a bit lacking in the state-of-the-art. Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems. What we will not cover in this curriculum: Business cases of using AI in Business. Consider taking Introduction to AI for business users learning path on Microsoft Learn, or AI Business School, developed in cooperation with INSEAD. Classic Machine Learning, which is well described in our Machine Learning for Beginners Curriculum. Practical AI applications built using Cognitive Services. For this, we recommend that you start with modules Microsoft Learn for vision, natural language processing, Generative AI with Azure OpenAI Service and others. Specific ML Cloud Frameworks, such as Azure Machine Learning, Microsoft Fabric, or Azure Databricks. Consider using Build and operate machine learning solutions with Azure Machine Learning and Build and Operate Machine Learning Solutions with Azure Databricks learning paths. Conversational AI and Chat Bots. There is a separate Create conversational AI solutions learning path, and you can also refer to this blog post for more detail. Deep Mathematics behind deep learning. For this, we would recommend Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville, which is also available online at [Hidden Content]. For a gentle introduction to AI in the Cloud topics you may consider taking the Get started with artificial intelligence on Azure Learning Path. Content Lesson Link PyTorch/Keras/TensorFlow Lab 0 Course Setup Setup Your Development Environment I Introduction to AI 01 Introduction and History of AI - - II Symbolic AI 02 Knowledge Representation and Expert Systems Expert Systems / Ontology /Concept Graph III Introduction to Neural Networks 03 Perceptron Notebook Lab 04 Multi-Layered Perceptron and Creating our own Framework Notebook Lab 05 Intro to Frameworks (PyTorch/TensorFlow) and Overfitting PyTorch / Keras / TensorFlow Lab IV Computer Vision PyTorch / TensorFlow Explore Computer Vision on Microsoft Azure 06 Intro to Computer Vision. OpenCV Notebook Lab 07 Convolutional Neural Networks & CNN Architectures PyTorch /TensorFlow Lab 08 Pre-trained Networks and Transfer Learning and Training Tricks PyTorch / TensorFlow Lab 09 Autoencoders and VAEs PyTorch / TensorFlow 10 Generative Adversarial Networks & Artistic Style Transfer PyTorch / TensorFlow 11 Object Detection TensorFlow Lab 12 Semantic Segmentation. U-Net PyTorch / TensorFlow V Natural Language Processing PyTorch /TensorFlow Explore Natural Language Processing on Microsoft Azure 13 Text Representation. Bow/TF-IDF PyTorch / TensorFlow 14 Semantic word embeddings. Word2Vec and GloVe PyTorch / TensorFlow 15 Language Modeling. Training your own embeddings PyTorch / TensorFlow Lab 16 Recurrent Neural Networks PyTorch / TensorFlow 17 Generative Recurrent Networks PyTorch / TensorFlow Lab 18 Transformers. BERT. PyTorch /TensorFlow 19 Named Entity Recognition TensorFlow Lab 20 Large Language Models, Prompt Programming and Few-Shot Tasks PyTorch VI Other AI Techniques 21 Genetic Algorithms Notebook 22 Deep Reinforcement Learning PyTorch /TensorFlow Lab 23 Multi-Agent Systems VII AI Ethics 24 AI Ethics and Responsible AI Microsoft Learn: Responsible AI Principles IX Extras 25 Multi-Modal Networks, CLIP and VQGAN Notebook Each lesson contains Pre-reading material Executable Jupyter Notebooks, which are often specific to the framework (PyTorch or TensorFlow). The executable notebook also contains a lot of theoretical material, so to understand the topic you need to go through at least one version of the notebook (either PyTorch or TensorFlow). Labs available for some topics, which give you an opportunity to try applying the material you have learned to a specific problem. Some sections contain links to MS Learn modules that cover related topics. Getting Started We have created a setup lesson to help you with setting up your development environment. - For Educators, we have created a curricula setup lesson for you too! How to Run the code in a VSCode or a Codepace Don't forget to star () this repo to find it easier later. Meet other Learners Join our official AI Discord server to meet and network with other learners taking this course and get support. Quizzes Help Wanted Do you have suggestions or found spelling or code errors? Raise an issue or create a pull request. Special Thanks Primary Author: Dmitry Soshnikov, PhD Editor: Jen Looper, PhD Sketchnote illustrator: Tomomi Imura Quiz Creator: Lateefah Bello, MLSA Core Contributors: Evgenii Pishchik Other Curricula Our team produces other curricula! Check out: Data Science for Beginners Version 2.0 Generative AI for Beginners NEW Cybersecurity for Beginners Web Dev for Beginners IoT for Beginners Machine Learning for Beginners XR Development for Beginners Mastering GitHub Copilot for AI Paired Programming GitHub Repo ENJOY & HAPPY LEARNING!

Doxing Tools & Resources - Dox Anyone On Social Media Doxing | A Collection Of Most Useful Social Media Dox Resources Facebook Facebook Recover Lookup - [Hidden Content] Used to check if given mail or phone number is associated with any facebook account or not CrowdTangle Link Checker - [Hidden Content] show the specific Facebook posts, Instagram posts, tweets, and subreddits that mention this link. It works for articles, as well as YouTube videos, Facebook videos and more. Social Searcher - [Hidden Content] allows you to monitor all public social mentions in social networks and web. Lookup-id.com - [Hidden Content] helps you to find the Facebook ID of anyone’s profile or a Group Who posted this - [Hidden Content] Facebook keyword search for people who work in the public interest. It allows you to search keywords on specific dates. Facebook Search - [Hidden Content] Allows you to search on facebook for posts,people,photos,etc using some filters Hashatit - [Hidden Content] A simple social media search engine for Hashtags Facebook People Search - [Hidden Content] Search on facebook by victim’s name Facebook Page Analytics tool - [Hidden Content] Find out how your page performances are compared to the average performances of the 42,578 pages added to the Barometer. Instagram Igram - [Hidden Content] Download Photos, Videos, IGTV & more from a public instagram account. IFTTT - [Hidden Content] allow a user to program a response to events in the world. Pickuki - [Hidden Content] Edit and browse Instagram content without logging in. Gramhir - [Hidden Content] Analyze and explore Instagram in a new and better way Instaloader - [Hidden Content] Download pictures (or videos) along with their captions and other metadata from Instagram. LinkedIn RecruitEm - [Hidden Content] allows to search social media profiles. It helps recruiters to create a google boolean string that searches all public profiles. RocketReach - [Hidden Content] allows you to programatically search & lookup contact info over 700 million professionals, and 35 million companies. Twitter Social Bearing - [Hidden Content] Free Twitter analytics & search for tweets, timelines & twitter maps. Find, filter and sort tweets or people by engagement, influence, location, sentiment and more Trendsmap - [Hidden Content] Analyse any topic globally or by region in detail. Create unique map based visualisations showing tweet activity across a country, a region, or the world. Follower.me - [Hidden Content] a Twitter analytics application that gives you rich insights about any public Twitter profile. TweetBeaver - [Hidden Content] can gather data on any non-private account and returns most searches as a csv for easier filtering and analysis. TweeterID - [Hidden Content] allows you to easily look up any username (@Handle) on Twitter and find out what their corresponding ID is. TweetDeck - [Hidden Content] offers a more convenient Twitter experience by letting you view multiple timelines in one easy interface. Spoonbill - [Hidden Content] allows you see profile changes from the people you follow on Twitter or other social networks. FollowerWonk - [Hidden Content] Helps you to find twitter accounts using bio and also provides many other useful features. Twitter Advance Search - [Hidden Content] Allows you to search on twitter using filters for better search results. Twipho - [Hidden Content] lets you discover what’s happening on Twitter right now, but via the medium of images rather than text-based tweets. TweeetMap - [Hidden Content] allows you to analyze twitter accounts that you do not own, and gain accurate insight on influencer interactions and personality TinfoLeak - [Hidden Content] Basic information about a Twitter user (name, picture, location, followers, etc.) ENJOY & HAPPY LEARNING! Appreciate the share & feedback! don’t be cheap!

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Become a Prompt Engineer: Go From Zero to Scripting AI Workflows | Learn With Hasan Master the Most In-Demand Skill of the Future! Access to APIs: For those who want to follow along closely, access to the OpenAI API or any other Language Model (LLM) API is recommended. In short, your passion for learning is the key ingredient. We’re here to guide you through the rest! Course Description This course will take you from zero to hero, with no prerequisites needed. Start by mastering the basics of prompt engineering, progress to Python scripting, and then learn to construct AI workflows with power prompts. In today’s fast-paced world, proficiency in prompt engineering is not just a handy skill – it’s a passport to the future. It’s among the most in-demand skills now and in the foreseeable future. But the benefits don’t stop there. This course will equip you to become 10x more productive. Plus, it opens the door to fantastic career opportunities, whether that’s landing a high-quality job, freelancing, or even starting your own online business. It’s more than a skill; it’s a game-changer! I’ve seen firsthand the power of this skill. It revolutionized my business workflows, saving me countless hours and enabling me to start several successful online businesses. And remember, you’re not on this journey alone. I’ll be there with you almost every day. So are you ready to embrace the future?. Language English | Skills Beginner Intermediate | Last updated 10/2023 Download Link ENJOY & HAPPY LEARNING!

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