https://www.analyticsvidhya.com/blog/2016/08/deep-learning-path/?utm_content=bufferd56c5&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer
#Deep_Learning, a prominent topic in #Artificial_Intelligence domain, has been in the spotlight for quite some time now. It is especially known for its breakthroughs in fields like Computer Vision and Game playing (Alpha GO), surpassing human ability. Since the last survey, there has been a drastic increase in the trends. (click here to check out the survey)
Here is what Google trends shows us:
https://django-ai.readthedocs.io/en/latest/readme.html
#Artificial_Intelligence for #Django
django-ai is a collection of apps for integrating statistical models into your Django project so you can implement #Machine_Learning conveniently.
🙋🏻♂️ Знакомьтесь, это Бруно Оливейра, VP of Engineering в Noema, дочерней компании DSR Corporation. Noema занимается созданием решений с использованием технологий AI и Computer Vision.
👨🏻💻 Именно интерес к CV привел Бруно в DSR.
💬 — Не так просто найти компанию, которая специализируется на создании CV/AI продуктов для использования в реальной жизни. Это именно то, чем мне нравится заниматься, — рассказывает Бруно.
#dsr_team#doingsoftwareright#noema#computer_vision#artificial_intelligence
#csharp#ai#artificial_intelligence#llm#openai#sdk
Semantic Kernel is a tool that helps developers build and manage AI systems easily. It supports multiple programming languages like C#, Python, and Java, making it versatile for different projects. This tool allows you to connect your AI models to various services and databases, which helps in automating tasks and making decisions based on user inputs. It's especially useful for businesses because it's reliable, secure, and can handle complex workflows. By using Semantic Kernel, developers can create intelligent AI agents that can interact with users and perform tasks efficiently.
https://github.com/microsoft/semantic-kernel
#python#artificial_intelligence#cybersecurity#generative_ai#llm#pentesting
Cybersecurity AI (CAI) is an open-source, lightweight framework that helps you build AI agents to find and fix security vulnerabilities efficiently. It supports many AI models and tools, works on multiple operating systems, and allows human control during tasks. CAI automates complex security testing steps like scanning, exploiting, and validating bugs, making bug bounty hunting easier and faster. It also logs detailed traces for better analysis and supports teamwork among AI agents. Using CAI can boost your cybersecurity skills, save time, and improve your ability to protect systems from attacks by combining AI power with your expertise.
https://github.com/aliasrobotics/cai
#python#agents#artificial_intelligence#cybersecurity#generative_ai#llm#penetration_testing
Strix is a free, open-source tool that uses AI agents to automatically find and fix security problems in your apps by acting like real hackers—running your code, hunting for vulnerabilities, and proving they’re real by actually exploiting them, not just guessing[1][2]. It works fast, gives clear reports, and can even suggest fixes or create pull requests to help you secure your code quickly. You can run it on your own computer, in your development pipeline, or use a cloud version for easier setup. The main benefit is that you get thorough, real-world security testing without the slow pace and high cost of manual checks, helping you catch and fix issues before they become serious problems.
https://github.com/usestrix/strix
🚀 AINFT Transitions to B.AI Brand Focused on Agent Finance
The official Twitter account of AINFT will transition to B.AI starting today. According to ChainCatcher, the B.AI brand aims to advance Agent Finance, which involves AI Agents autonomously managing funds, executing trades, and optimizing returns, thereby granting artificial intelligence true financial autonomy and accelerating the realization of Artificial General Intelligence (AGI). To ensure a smooth transition for the community, the brand will implement phased upgrades to avoid the impact of a one-time switch. During this process, AINFT will continue to operate as a core sub-brand within the B.AI ecosystem. All content, technological iterations, and community activities related to AINFT will be migrated to the new platform @AINFTcom.
#B_AI#Agent_Finance#AI_Agents#Artificial_Intelligence#AGI#Technology_Transition#AINFT#Financial_Autonomy#Blockchain#Crypto
http://codeinpython.com/tutorials/deep-learning-tensorflow-keras-pytorch/?nonamp=1
Deep Learning #Tensorflow vs #Keras vs #PyTorch
#Deep_learning is the application of artificial #neural_networks (ANNs) to learn tasks. These tasks contain more than one hidden layer. Deep learning is part of a broader family of #machine_learning. Machine learning itself is a part of #Artificial_Intelligence(#AI).
#rust#artificial_intelligence#big_data#data_engineering#distributed_computing#machine_learning#multimodal#python#rust
Daft is a powerful, easy-to-use data engine that lets you process large-scale data using Python or SQL with high speed and efficiency. It supports complex data types like images and tensors, works well interactively for quick data exploration, and can scale to huge cloud clusters using Ray. Daft integrates smoothly with cloud storage and data catalogs, making it ideal for data engineering, analytics, and machine learning workflows. By using Daft, you can handle big, multimodal datasets faster and more flexibly, improving your ability to analyze and prepare data for AI models without complex setup or slowdowns.
https://github.com/Eventual-Inc/Daft
#jupyter_notebook#artificial_intelligence#book#large_language_models#llm#llms#oreilly#oreilly_books
You can learn how to use Large Language Models (LLMs) effectively through the book *Hands-On Large Language Models* by Jay Alammar and Maarten Grootendorst. This book uses nearly 300 custom illustrations to explain key concepts and practical tools for working with LLMs, including tokenization, transformers, prompt engineering, fine-tuning, and advanced text generation. It also provides runnable code examples in Google Colab, making it easy to practice and apply what you learn. This resource helps you understand and build your own LLM applications confidently, saving you time and effort in mastering complex AI technology. It’s highly recommended for anyone wanting hands-on experience with LLMs.
https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
#elixir#agent#ai#artificial_intelligence#elixir#event_driven_architecture#functional_programming#orchestration#workflow
Jido is a pure functional framework for Elixir to build autonomous multi-agent workflows. Agents are immutable data with a simple `cmd/2` function that transforms state purely and outputs directives for effects like signals or spawning, handled by OTP runtime. It formalizes patterns like standard signals, reusable actions, and hierarchies over raw GenServer, adding AI tools, strategies (ReAct, FSM), and supervision. You benefit by creating scalable, testable, fault-tolerant agent systems easily for production AI apps, saving reinvented code.
https://github.com/agentjido/jido
http://www.datapine.com/blog/technology-buzzwords/
12 IT & Technology Buzzwords You Won’t Be Able To Avoid In 2017
#Virtual_Assistants
#Artificial_Intelligence (#AI)
#Augmented_Reality / #Virtual_Reality
#Deep_Learning / #Advanced_Machine_Learning
#Blockchain
Everything On-Demand (The Uber Effect)
Digital Twin
Smart Factory / Industry 4.0
Actionable Analytics / Self-service analytics
Internet of Things / Device Mash / Ambient UX
React JS / React Native
Quantum Computing