Search Results for "roadmap"
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7 projects for "roadmap" with 2 filters applied:
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Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
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Repair-CRMAll-In-One Solution with an Online Booking portal for automating scheduling & dispatching to ditch paperwork and improve the productivity of your technicians!
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AI Logo Generator
A free + OSS logo generator powered by Flux on Together AI
AI Logo Generator is an open-source AI logo generator that lets you create professional-looking logos in seconds from a simple text prompt. It uses the Flux Pro 1.1 model hosted on Together AI to generate logos, so the heavy lifting is done by a state-of-the-art image model while the app focuses on UX and workflow. The project is built with Next.js and TypeScript, and it uses shadcn/ui plus Tailwind CSS for a modern, responsive interface that feels like a polished SaaS product rather than a... -
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Learn AI Engineering
Learn AI and LLMs from scratch using free resources
Learn AI Engineering is a learning path for AI engineering that consolidates high-quality, free resources across the full stack: math, Python foundations, machine learning, deep learning, LLMs, agents, tooling, and deployment. Rather than a loose bookmark list, it organizes topics into a progression so learners can start from fundamentals and move toward practical, production-oriented skills. It mixes courses, articles, code labs, and videos, emphasizing materials that teach both concepts... -
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EmotiVoice
Multi-Voice and Prompt-Controlled TTS Engine
EmotiVoice is a multi-voice, prompt-controlled text-to-speech engine designed to generate highly expressive speech across thousands of voices. It supports both English and Chinese and ships with over 2,000 preset voices, making it suitable for everything from characters and virtual anchors to narration and dialogue. The core idea is prompt-based emotional and style control: you can ask the engine to speak “happy,” “sad,” “excited,” or with other high-level style prompts that shape prosody,... -
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daily-paper-computer-vision
Document papers compiled daily in computer vision/deep learning
...By aggregating paper titles and references in one place, it reduces the overhead of deciding what to read next and helps you spot trends early. It doubles as a personal knowledge log: you can star or fork it to build your own “to-read” roadmap. For students and engineers alike, it’s a practical way to stay research-literate without getting overwhelmed. -
D&B Hoovers is Your Sales AcceleratorSpeed up sales prospecting with the rich audience targeting capabilities of D&B Hoovers so you can spend more sales time closing.
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Self-learning-Computer-Science
Resources to learn computer science in your spare time
...It’s aimed at learners who find traditional course structures restrictive and want a flexible, self-paced path through CS, with a focus on building depth and breadth rather than shortcut exam skills. The repository provides a roadmap, references, teaching materials, and sometimes the author’s own project examples, offering both guidance and community support. Because the CS field is broad, the structure helps learners allocate study time, avoid duplication, and benefit from “best in class” resources instead of randomly browsing. -
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Deep Learning Papers Reading Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn
...The roadmap organizes papers into categories such as fundamentals, convolutional networks, sequence models, unsupervised learning, generative models, optimization, and application areas like computer vision or NLP. For each section, it suggests an order that lets readers gradually build intuition and then dive deeper into more advanced or recent topics. -
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Learn_Machine_Learning_in_3_Months
This is the code for "Learn Machine Learning in 3 Months"
This repository outlines an ambitious self-study curriculum for learning machine learning in roughly three months, emphasizing breadth, momentum, and hands-on practice. It sequences core topics—math foundations, classic ML, deep learning, and applied projects—so learners can pace themselves week by week. The plan mixes reading, lectures, coding assignments, and small build-it-yourself projects to reinforce understanding through repetition and implementation. Because ML is a wide field, the...
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