Search Results for "self learning"
Sort By:
66 projects for "self learning" with 2 filters applied:
-
Veeam Data Platform v13.1 - Get Your Free TrialTry Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
-
1
AgentEvolver
Towards Efficient Self-Evolving Agent System
AgentEvolver is an open-source research framework for building self-evolving AI agents powered by large language models. The system focuses on improving the efficiency and scalability of training autonomous agents by allowing them to generate tasks, explore environments, and refine strategies without heavy reliance on manually curated datasets. Its architecture combines reinforcement learning with LLM-driven reasoning mechanisms to guide exploration and learning. -
2
deep-q-learning
Minimal Deep Q Learning (DQN & DDQN) implementations in Keras
...For learners and researchers interested in reinforcement learning, this repo offers a concrete, runnable example bridging theory and practice: you can execute the code, play with hyperparameters, observe convergence behavior, and see how deep Q-learning learns policies over time in standard environments. Because it’s self-contained and Python-based, it's well-suited for experimentation, modifications, or extension — for instance adapting to custom Gym environments, tweaking network architecture, or combining with other RL techniques. -
3
Deep Learning Interviews book
Hundreds of fully solved job interview questions
The interviews.ai repository hosts the open materials for the book Deep Learning Interviews, a comprehensive collection of technical questions and fully solved problems covering many aspects of artificial intelligence. The project was created to help students, researchers, and engineers prepare for machine learning and deep learning interviews by providing structured explanations of key concepts. The repository organizes problems across topics such as neural networks, optimization,... -
4
DINOv3
Reference PyTorch implementation and models for DINOv3
DINOv3 is the third-generation iteration of Meta’s self-supervised visual representation learning framework, building upon the ideas from DINO and DINOv2. It continues the paradigm of learning strong image representations without labels using teacher–student distillation, but introduces a simplified and more scalable training recipe that performs well across datasets and architectures. -
Paessler: Easy to Use With Enterprise Power. Free TrialYou shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
-
5
Start Machine Learning in 2026
A complete guide to start and improve in machine learning
Start Machine Learning in 2026 repository is an open educational guide designed to help beginners enter the field of machine learning and artificial intelligence with little or no prior technical background. The project organizes a large collection of learning resources, including online courses, books, tutorials, research articles, and video lectures that explain fundamental AI concepts. -
6
Dash Data Agent
Self-learning data agent that grounds its answers in layers of content
Dash is a self-learning data agent built by the Agno AI community that generates grounded answers to English queries over structured data by synthesizing SQL and reasoning based on six layers of context, improving automatically with each run. It sidesteps common limitations of simple text-to-SQL agents by incorporating multiple context layers — including schema structure, human annotations, known query patterns, institutional knowledge from docs, machine-discovered error patterns, and live runtime context — to generate SQL queries that are both technically correct and semantically meaningful. ... -
7
JEPA
PyTorch code and models for V-JEPA self-supervised learning from video
JEPA (Joint-Embedding Predictive Architecture) captures the idea of predicting missing high-level representations rather than reconstructing pixels, aiming for robust, scalable self-supervised learning. A context encoder ingests visible regions and predicts target embeddings for masked regions produced by a separate target encoder, avoiding low-level reconstruction losses that can overfit to texture. This makes learning focus on semantics and structure, yielding features that transfer well with simple linear probes and minimal fine-tuning. ... -
8
Super comprehensive deep learning notes
Super Comprehensive Deep Learning Notes
Super comprehensive deep learning notes is a massive and well-structured collection of deep learning notebooks that serve as a comprehensive study resource for anyone wanting to learn or reinforce concepts in computer vision, natural language processing, deep learning architectures, and even large-model agents. The repository contains hundreds of Jupyter notebooks that are richly annotated and organized by topic, progressing from basic Python and PyTorch fundamentals to advanced neural network designs like ResNet, transformers, and object detection algorithms. ... -
9
vJEPA-2
PyTorch code and models for VJEPA2 self-supervised learning from video
VJEPA2 is a next-generation self-supervised learning framework for video that extends the “predict in representation space” idea from i-JEPA to the temporal domain. Instead of reconstructing pixels, it predicts the missing high-level embeddings of masked space-time regions using a context encoder and a slowly updated target encoder. This objective encourages the model to learn semantics, motion, and long-range structure without the shortcuts that pixel-level losses can invite. ... -
Save Up to 91% on Cloud Compute With Spot VMsRun batch jobs at 60-91% off with Spot VMs. Long-running workloads get automatic discounts with sustained use.
-
10
handson-ml3
Fundamentals of Machine Learning and Deep Learning
handson-ml3 contains the Jupyter notebooks and code for the third edition of the book Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow. It guides readers through modern machine learning and deep learning workflows using Python, with examples spanning data preparation, supervised and unsupervised learning, deep neural networks, RL, and production-ready model deployment. The third edition updates the content for TensorFlow 2 and Keras, introduces new chapters (for example on... -
11
SwanLab
An open-source, modern-design AI training tracking and visualization
...SwanLab supports both cloud and self-hosted deployments, allowing organizations to run the system privately or integrate it into shared development environments. The platform integrates with a wide range of machine learning frameworks including PyTorch, Transformers, Keras, and other widely used training ecosystems. -
12
Reflexion
Reflexion: Language Agents with Verbal Reinforcement Learning
Reflexion is a research-oriented AI framework that focuses on improving the reasoning and problem-solving capabilities of language model agents through iterative self-reflection and feedback loops. Instead of relying solely on a single-pass response, Reflexion enables agents to evaluate their own outputs, identify errors, and refine their reasoning over multiple iterations, leading to more accurate and reliable results. The framework introduces a mechanism where agents maintain a memory of past attempts and use that memory to guide future decisions, effectively simulating a learning process without requiring traditional model retraining. ... -
13
LibrePhotos
A self-hosted open source photo management service
LibrePhotos is an open-source self-hosted photo management platform designed to organize, browse, and analyze personal media libraries while preserving user privacy. The system allows individuals to store and manage their photos and videos locally rather than relying on commercial cloud services. It provides features similar to services like Google Photos but runs on a private server controlled by the user. The application includes AI-powered tools that automatically analyze images to detect... -
14
DINOv2
PyTorch code and models for the DINOv2 self-supervised learning
DINOv2 is a self-supervised vision learning framework that produces strong, general-purpose image representations without using human labels. It builds on the DINO idea of student–teacher distillation and adapts it to modern Vision Transformer backbones with a carefully tuned recipe for data augmentation, optimization, and multi-crop training. The core promise is that a single pretrained backbone can transfer well to many downstream tasks—from linear probing on classification to retrieval, detection, and segmentation—often requiring little or no fine-tuning. ... -
15
Academic Research Skills for Claude Code
Academic Research Skills for Claude Code
Academic Research Skills is a structured learning repository aimed at improving users’ ability to conduct rigorous academic research, particularly in technical and scientific domains. It compiles methodologies, frameworks, and best practices for literature review, critical analysis, and research writing. The project is designed as a self-guided resource, helping learners understand how to evaluate sources, synthesize information, and develop strong arguments. -
16
Apache Hamilton
Helps data scientists define testable self-documenting dataflows
Apache Hamilton is an open-source Python framework designed to simplify the creation and management of dataflows used in analytics, machine learning pipelines, and data engineering workflows. The framework enables developers to define data transformations as simple Python functions, where each function represents a node in a dataflow graph and its parameters define dependencies on other nodes. Hamilton automatically analyzes these functions and constructs a directed acyclic graph... -
17
MuZero General
A commented and documented implementation of MuZero
muzero-general is an open-source implementation of the MuZero reinforcement learning algorithm introduced by DeepMind. MuZero is a model-based reinforcement learning method that combines neural networks with Monte Carlo Tree Search to learn decision-making policies without requiring explicit knowledge of the environment’s dynamics. The repository provides a well-documented and commented implementation designed primarily for educational purposes. It allows researchers and developers to train... -
18
OpenSpace
OpenSpace: Make Your Agents: Smarter, Low-Cost, Self-Evolving
OpenSpace is a self-evolving agent framework designed to improve the performance, efficiency, and collaboration of AI agents through continuous learning and shared knowledge. It introduces a system where agents develop reusable “skills” based on real task execution, allowing them to improve over time without retraining underlying models. The platform emphasizes collective intelligence, enabling multiple agents to share learned behaviors and benefit from each other’s experiences. ... -
19
Hindsight
Hindsight: Agent Memory That Learns
Hindsight is an advanced, open-source memory system for AI agents designed to enable long-term learning, reasoning, and consistency across interactions by treating memory as a first-class component of intelligence rather than a simple retrieval layer. It addresses one of the core limitations of modern AI agents, which is their inability to retain and meaningfully use past experiences over time, by introducing a structured, biomimetic memory architecture inspired by how human memory works.... -
20
DATA SCIENCE ROADMAP
Data Science Roadmap from A to Z
DATA SCIENCE ROADMAP is an educational repository designed to guide learners through the process of becoming proficient in data science and machine learning. The project presents a structured roadmap that outlines the knowledge and skills required for different stages of a data science career. Topics typically include programming with Python, statistics, mathematics, machine learning algorithms, data visualization, and big data technologies. The roadmap also includes links to courses,... -
21
Reef
Continual learning infra for self-improving agents
Reef is open-source infrastructure for building AI agents that continually improve from interaction and feedback. It connects live inference, feedback collection, learning, evaluation, and versioned deployment in one lifecycle. The system can improve either underlying model weights or the surrounding agent harness, including prompts, skills, and rules. Its core loop follows four stages: serve requests, observe feedback, grow candidate improvements, and commit accepted updates.... -
22
Acontext
Context data platform for building observable, self-learning AI agents
...The platform observes agent tasks and user feedback in real time, offering robust observability into workflows and helping teams understand how agents perform over time. Acontext also supports agent self-learning by distilling structured skills and experiences from previously completed tasks, which can later be reused or searched to improve future performance. It includes tools to interact with session data, background agents that monitor progress, and a dashboard that visualizes success rates, artifacts, and learned skills. By combining persistent storage, observability, and learning capabilities, Acontext aims to make AI agents more scalable, reliable, and capable. -
23
AudioMuse-AI
AudioMuse-AI is an Open Source Dockerized environment
...By analyzing the underlying audio content rather than relying on external metadata services, the system can organize large personal music libraries and generate curated playlists for different moods or listening contexts. AudioMuse-AI integrates with several popular self-hosted music servers including Jellyfin, Navidrome, and Emby, allowing users to extend existing media servers with advanced AI-powered recommendation capabilities. The system uses machine learning and audio analysis tools such as Librosa and ONNX models to extract features directly from audio tracks. -
24
EvoAgentX
Self-evolving AI agent framework for automated workflows
...This allows agents to adapt dynamically instead of relying on fixed logic. It is designed for researchers and developers who want to automate complex agent systems and improve performance through continuous learning cycles, reducing manual orchestration and enabling more efficient development. -
25
Pro Workflow
Claude Code learns from your corrections: self-correcting memory
Pro Workflow is a productivity framework for Claude Code that introduces self-improving workflows through memory, context engineering, and structured agent orchestration. The system learns from user corrections over time, storing feedback and refining its behavior across sessions to improve accuracy and efficiency. It supports advanced development setups such as parallel worktrees, enabling multiple tasks to be handled simultaneously without interference. The framework includes a collection...
