--- title: "DeepLearningExamples vs ai-engineering-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-deeplearningexamples-vs-rohitg00-ai-engineering-from-scratch" tools: ["nvidia-deeplearningexamples", "rohitg00-ai-engineering-from-scratch"] --- # DeepLearningExamples vs ai-engineering-from-scratch *GraphCanon updated Sep 20, 2026* ## Verdict Pick DeepLearningExamples if deepLearningExamples offers state-of-the-art deep learning scripts optimized for NVIDIA GPUs, with a focus on reproducibility and performance. It supports a wide range of applications from computer vision to speech and N; pick ai-engineering-from-scratch if ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python. [DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples) reports 15k GitHub stars, 3.4k forks, and 321 open issues, last pushed Aug 12, 2024. [ai-engineering-from-scratch](https://aiengineeringfromscratch.com) has 55k stars, 9.6k forks, and 114 open issues, last pushed Sep 7, 2026. Figures are from public GitHub metadata via [DeepLearningExamples's repository](https://github.com/NVIDIA/DeepLearningExamples) and [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch). | | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Tagline | State-of-the-Art Deep Learning scripts for easy training and deployment with reproducible accuracy and performance on enterprise-grade infrastructure | Learn, build, and deploy AI engineering skills from scratch. | | Stars | 14,847 | 54,935 | | Forks | 3,406 | 9,649 | | Open issues | 321 | 114 | | Language | Jupyter Notebook | Python | | Adopt for | DeepLearningExamples offers state-of-the-art deep learning scripts optimized for NVIDIA GPUs, with a focus on reproducibility and performance. It supports a wide range of applications from computer vision to speech and N | ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and | | Persona | - | - | | Runtime | - | - | | License | The license information for DeepLearningExamples is unknown. | MIT | | Categories | Computer Vision, Inference & Serving, Model Training | AI Agents, Computer Vision, Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 766d | 10d | | Open issues (now) | 321 | 114 | | Stars delta | +3 (30d) | +8.1k (30d) | | Open issues delta | 0 (30d) | +7 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/nvidia-deeplearningexamples/trust.md) | [trust report](/tools/rohitg00-ai-engineering-from-scratch/trust.md) | ## Decision facts: DeepLearningExamples - **Requirements:** Requires NVIDIA GPUs for optimal performance.; Uses NVIDIA's CUDA-X software stack, including libraries like cuDNN, NCCL, and cuBLAS. - **Adopt for:** DeepLearningExamples offers state-of-the-art deep learning scripts optimized for NVIDIA GPUs, with a focus on reproducibility and performance. It supports a wide range of applications from computer vision to speech and N - **License detail:** The license information for DeepLearningExamples is unknown. ## Decision facts: ai-engineering-from-scratch - **Adopt for:** ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and ## Choose when ### Choose DeepLearningExamples if… - DeepLearningExamples is primarily Jupyter Notebook; ai-engineering-from-scratch is Python. - Requirements: Requires NVIDIA GPUs for optimal performance.; Uses NVIDIA's CUDA-X software stack, including libraries like cuDNN, NCCL, and cuBLAS.. - Tags unique to DeepLearningExamples: drug-discovery, forecasting, large-language-models, mxnet. - Also covers Inference & Serving. - When you need deep learning scripts optimized for NVIDIA GPUs, including Volta, Turing, and Ampere architectures, for tasks like computer vision, NLP, and speech recognition. ### Choose ai-engineering-from-scratch if… - ai-engineering-from-scratch is primarily Python; DeepLearningExamples is Jupyter Notebook. - Tags unique to ai-engineering-from-scratch: agents, ai-engineering, from-scratch, generative-ai. - Also covers AI Agents, Developer Tools. - when you need a structured course that covers a wide range of AI engineering topics from scratch, including foundational math, deep learning, and reinforcement learning. ## When NOT to use DeepLearningExamples - If your infrastructure does not include NVIDIA GPUs, as the scripts are specifically optimized for NVIDIA hardware. - If you are looking for a tool that supports a wider range of hardware or non-NVIDIA GPU environments. - When you need a solution that is not tied to specific frameworks like PyTorch, TensorFlow, or PaddlePaddle, as DeepLearningExamples focuses on these frameworks. ## When NOT to use ai-engineering-from-scratch - if you are looking for a tool that focuses solely on theoretical knowledge without practical application, as this course emphasizes hands-on learning. - when you do not have access to Node.js or Python, as these are required for running the course and its interactive components. - if you prefer a more traditional learning approach without the use of terminal-based learning tools, as the course is designed for interactive terminal sessions. - when you are working with a development environment that does not support skill-capable hosts, as the course is optimized for such environments. ## Common questions ### What is the difference between DeepLearningExamples and ai-engineering-from-scratch? DeepLearningExamples: State-of-the-Art Deep Learning scripts for easy training and deployment with reproducible accuracy and performance on enterprise-grade infrastructure. ai-engineering-from-scratch: Learn, build, and deploy AI engineering skills from scratch.. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepLearningExamples over ai-engineering-from-scratch? Choose DeepLearningExamples over ai-engineering-from-scratch when DeepLearningExamples is primarily Jupyter Notebook; ai-engineering-from-scratch is Python; Requirements: Requires NVIDIA GPUs for optimal performance.; Uses NVIDIA's CUDA-X software stack, including libraries like cuDNN, NCCL, and cuBLAS.; Tags unique to DeepLearningExamples: drug-discovery, forecasting, large-language-models, mxnet; Also covers Inference & Serving; When you need deep learning scripts optimized for NVIDIA GPUs, including Volta, Turing, and Ampere architectures, for tasks like computer vision, NLP, and speech recognition. ### When should I choose ai-engineering-from-scratch over DeepLearningExamples? Choose ai-engineering-from-scratch over DeepLearningExamples when ai-engineering-from-scratch is primarily Python; DeepLearningExamples is Jupyter Notebook; Tags unique to ai-engineering-from-scratch: agents, ai-engineering, from-scratch, generative-ai; Also covers AI Agents, Developer Tools; when you need a structured course that covers a wide range of AI engineering topics from scratch, including foundational math, deep learning, and reinforcement learning. ### When should I avoid DeepLearningExamples? If your infrastructure does not include NVIDIA GPUs, as the scripts are specifically optimized for NVIDIA hardware. If you are looking for a tool that supports a wider range of hardware or non-NVIDIA GPU environments. When you need a solution that is not tied to specific frameworks like PyTorch, TensorFlow, or PaddlePaddle, as DeepLearningExamples focuses on these frameworks. ### When should I avoid ai-engineering-from-scratch? if you are looking for a tool that focuses solely on theoretical knowledge without practical application, as this course emphasizes hands-on learning. when you do not have access to Node.js or Python, as these are required for running the course and its interactive components. if you prefer a more traditional learning approach without the use of terminal-based learning tools, as the course is designed for interactive terminal sessions. when you are working with a development environment that does not support skill-capable hosts, as the course is optimized for such environments. ### Is DeepLearningExamples or ai-engineering-from-scratch more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (54,935 vs 14,847). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepLearningExamples and ai-engineering-from-scratch open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to DeepLearningExamples or ai-engineering-from-scratch? GraphCanon lists graph-backed alternatives at [DeepLearningExamples alternatives](/tools/nvidia-deeplearningexamples/alternatives) and [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) ([DeepLearningExamples markdown twin](/tools/nvidia-deeplearningexamples/alternatives.md), [ai-engineering-from-scratch markdown twin](/tools/rohitg00-ai-engineering-from-scratch/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/nvidia-deeplearningexamples-vs-rohitg00-ai-engineering-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepLearningExamples or ai-engineering-from-scratch? DeepLearningExamples: Dormant. ai-engineering-from-scratch: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for DeepLearningExamples and ai-engineering-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepLearningExamples trust report](/tools/nvidia-deeplearningexamples/trust); [ai-engineering-from-scratch trust report](/tools/rohitg00-ai-engineering-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nvidia-deeplearningexamples`](/api/graphcanon/graph?tool=nvidia-deeplearningexamples) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_