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Description
DAHUA®|NVR4104-P-EI|3 YR WTY. 4 Channel WizSense H.265 AI Smart Box NVR, 4 x PoE, 80Mbps incoming bandwidth, 4K output *Special order. 3-5 days lead timeNVR4000 EI series is a new generation of network recorders that offers powerful decoding capabilities, and strong bandwidth. It features high precision face recognition, perimeter protection, and SMD (Smart Motion Detection), which can distinguish target vehicles and humans from other types in real time. When connected with select IPC cameras, Quick Pick technology becomes available, allowing human and vehicle targets to be easily searched for and
NVR4000-EI series is a new generation of network recorders that offers powerful decoding capabilities, and strong bandwidth. It features high-precision face recognition, perimeter protection, and SMD (Smart Motion Detection), which can distinguish target vehicles
and humans from other types in real time. When connected with select IPC cameras, Quick Pick technology becomes available, allowing human and vehicle targets to be easily searched for and picked out. This network recorder is not only cost-effective, but also compatible with a wide variety of third-party devices, making it a great choice for
small and medium-sized businesses.
Features
- Face Recognition - Dahua Face Recognition technology extracts the features of captured faces and compares them with that in face database.
- Perimeter Protection - Automatically filters out false alarms caused by animals, rustling leaves, bright lights, etc. Enables system to act secondary recognition
for the targets. Improves alarm accuracy. - SMD Plus - With intelligent algorithm, Dahua Smart Motion Detection technology
can categorize the targets that trigger motion detection and filter the motion detection alarm triggered by non-concerned targets to realize effective and accurate alarm. - AI Search - With AI search, operators can search for videos by the attributes of
targets and event types, which helps them to quickly and efficiently investigate where and when targets appeared and events took place. - Heat Map by Camera - Dahua heat map technology is used to display the crowd density and people appearance probability. Export and display the crowd status by
different colours. Generally, the crowd status is the statistics of people quantity in space and time dimensions. - ANPR by Camera - With deep learning algorithm, Dahua ANPR technology can recognize the number plate information of vehicles in the image with ANPR
cameras. Support blocklist/allowlist mode, searching target vehicles from recorded video. - Quick Pick - Dahua Quick Pick technology can enable users to quickly pick up the
human/vehicle targets that they are interested in, which facilitates retrieval from SMD events.
Specifications
- Smart H.265+/H.265/Smart H.264+/H.264/MJPEG decoding format
- Access, storage or forwarding at 80 Mbps
- Max. decoding capability: 16 × 1080p@30 fps. Supports adaptive decoding
- AI by NVR: 1-channel face detection and recognition; 2-channel perimeter protection; up to 10 face databases and 20,000 face images; 4-channel SMD Plus
- AI by Camera: Face detection and recognition, perimeter protection; SMD Plus; people counting; stereo analysis; heat map
- Connected to the third-party network cameras
- Security baseline 2.3
Shipping Notes
- Free Standard Shipping on $100+ Orders to the USA.
- Except Preorder products are shipped in 48 hours.
- Delivery to the USA:
- Standard Shipping : 3-10 business days
- If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
- We offer a 30-day return/exchange service after receiving.
- Final sale items are not eligible for returns or exchanges.
- To process your return/exchange, please contact us at [email protected]
- Please click here for more details>>> Return & Exchange Policy
4.8 ★★★★★
Based on 29 reviews
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Product Reviews
★★★★★ 4
Title: Really Good Book for Learning LLMs
Format: Paperback, Format: Paperback
I picked up this book after struggling with LLM implementation at work. Ken Huang explains things clearly without too much technical jargon.
The book covers everything from data preparation to building AI agents. I especially liked the chapters on RAG and prompting techniques - they helped me improve my current projects. The code examples actually work, which is nice.
Some parts are pretty advanced, so you need basic Python knowledge. I had to read a few chapters twice to fully get it. The fairness and bias detection section was eye-opening.
Good practical advice throughout. Not just theory - real solutions you can use. Worth the money if you're serious about LLM development.
Recommended for anyone building AI systems professionally.
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Reviewed in the United States on July 25, 2025
★★★★★ 5
a comprehensive guide at the intersection of generative AI and cybersecurity
Format: Kindle
This book blends deep theoretical foundations with practical frameworks and forward-looking strategies. From adversarial risk models to actionable guidance using OWASP Top 10 for LLMs and the NIST AI RMF, it offers both technical depth and operational clarity.
What makes it stand out is its balance of academic rigor and real-world CISO insights, providing a holistic perspective on securing GenAI systems. While it leans enterprise-focused, the content remains accessible to security engineers, risk managers, and policy leaders alike.
Generative AI Security is a timely and essential read for anyone working to deploy GenAI responsibly—building systems with both power and integrity in today’s fast-evolving threat landscape.
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Reviewed in the United States on July 2, 2025
★★★★★ 1
Light on substance and heavy on flaws
Format: Paperback
The book has a great list of topics, but fails to provide much substance any of them.
Most of the provided code is just comments that avoid the actual crux of the issues being discussed. (e.g. #implement the logic to validate XYZ - while the whole point of this chapter is teach how the heck we validate XYZ!)
Some parts are plain wrong, for example the part on Graph based RAG is fundamentally flawed as it assumes the text embedding and the graph embedding are in the same latent space. (This is one of many more examples).
Seems like the book was rushed, and the author has limited hands on experience (if any).
At least we know based on the amount of flaws that it was not written by an LLM
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Reviewed in the United States on December 31, 2025
★★★★★ 5
Excellent book to truly understand LLM design patterns
Format: Paperback
I just finished reviewing Ken Huang's pocket book on LLM Design Patterns, and WOW what an amazing resource! This book is excellent if you want to truly understand how to create and enhance intelligent AI language models, all that in your pocket!
Ken makes the difficult things seem surprisingly easy, and that's the real MAGIC.
- How to prepare your data for training by making it extremely clean.
Developing the brains: the practical aspects of training, optimizing, and maintaining your models.
- Learn amazing prompting techniques (such as Chain-of-Thought and Tree-of-Thoughts) to improve your AI's reasoning and problem-solving abilities.
Learn everything there is to know about RAGs so that your LLM can incorporate outside expertise.
- It also delves into creating "agentic" AI that is capable of action and planning (not only simple plan and execute but also enhanced techniques like ReWoo!)
Really, this feels like a useful toolkit, so Ken thank you for that resource
Thanks, Idan Habler
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Reviewed in the United States on June 9, 2025
★★★★★ 3
A Broad Overview, But Light on Modern Fine-Tuning
Format: Paperback
I'm currently really interested in fine-tuning LLMs and recently completed my first LoRA-based fine-tuning on a quantized model. I came to this book looking for more detail on fine-tuning. While it touches on the topic, I found the content didn’t quite align with the current state of the field in 2025. Techniques like LoRA, QLoRA, and PEFT weren’t really covered, and the material leaned more toward what I think are older or lower level approaches. That made it harder to connect with what I’m actually working on.
That said, when I shifted to other chapters — like the sections on prompt engineering techniques such as Chain of Thought (CoT) and Tree of Thought (ToT) — I found more value. These sections were clearer, and I picked up a few practical insights, like using few-shot examples that walk through the CoT reasoning process. That’s not something I’ve tried before, and I can see how it might help smaller models that struggle with any type of reasoning tasks.
Overall, the book feels more like a broad overview of all LLM concepts. For someone exploring many topics across the LLM ecosystem, it offers a wide-ranging introduction. But for readers like me who are actively trying to learn and apply techniques like fine-tuning and quantization, it may leave you wanting up-to-date guidance.
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Reviewed in the United States on August 10, 2025