Essential Embedded AI Resources and Learning Notes

A fan asked me: “What are the opportunities for embedded developers in the next 5-10 years?”

The essence of opportunities is the imbalance in talent supply and demand over a period of time. In simple terms, it is due to sudden changes in the industry, where keen capital quickly enters, leading to a massive expansion of the industry in a short time, requiring a large number of developers.

Currently, embedded development is increasingly leaning towards intelligence, which we refer to as smart hardware (hardware + software).

Taking Baidu’s robot as an example, the core of the robot is its brain, which is the “data and algorithms”, but for the robot’s brain to enable the robot’s body to move like a human, to speak, and to walk freely, it must rely on embedded technology.

Although artificial intelligence has been booming in recent years, its real stage for business landing is in the IoT edge AI embedded field, which has a vast number of application scenarios.

Therefore, I personally believe that with the promotion of the Internet of Things and artificial intelligence, embedded systems will welcome more development opportunities in the next 5-10 years. On the one hand, embedded development will encounter more application scenarios; on the other hand, the technical system of embedded development will gradually enrich, thus expanding the technical boundaries of IoT development.

Currently, many AI frameworks have gradually supported edge AI, such as Google’s TensorFlow Lite and TensorFlow Lite Micro, as well as Huawei’s MindSpore Lite. Chip manufacturers ST and NXP have also launched some tools and demos aimed at edge AI.

In this context, I have three suggestions for technical personnel engaged in embedded development to enhance their workplace value:

• Further enrich your knowledge structure, focusing on artificial intelligence technology;

• Pay attention to the accumulation of industry experience; embedded development has many connections with various industry fields (in the future, embedded development will gradually cover traditional industries);

• Focus on relevant technologies in the industrial Internet field.

I have recently organized a set of essential learning materials for AI beginners, strongly recommending everyone to study them. The author, Wang Xiaotian, has 8 years of practical experience in the field of artificial intelligence, currently working as a senior technical expert in AI algorithms at one of the BAT companies, and graduated with dual master’s degrees (Computer Science and Applied Mathematics) from one of the top three universities in France.

He has published more than ten papers in the fields of artificial intelligence and chips, possessing a solid academic background and rich project and business landing experience.

During his work, he is mainly responsible for CV and NLP-related algorithm work in the artificial intelligence business line, promoting core algorithm research and optimization for human-machine hybrid intelligence, semantic segmentation, machine translation, iris recognition, and other modules. He has in-depth research on image classification, object detection, target tracking, autonomous driving, computer architecture, and more.

He possesses both theoretical and practical experience and understands the pain points of beginners.To be honest, it is rare to find someone with such qualifications.

(The content of the materials is too much, only part is extracted)

Due to work needs, I am also studying this tutorial. Although I have been in this industry for many years, I still find it very helpful when reviewing this tutorial, I believe that whether you are a beginner in AI or already have some work experience, this learning material is worth studying seriously.

All the above relevant content has been packaged and summarized into a Baidu Cloud link. A thoughtful touch is that for those brothers who do not have a Baidu Cloud membership, they can download at a speed of 2MB+/S, and I have specially prepared a download tool for everyone.

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Next, I will detail how to study this material.

First, to get started with AI, mastering a deep learning framework is one of the essential survival skills.

Therefore, the tutorial will start with learning deep learning frameworks, guiding you from zero to training networks, enabling you to independently build and design convolutional neural networks (including mainstream classification and detection networks), and perform training and inference of neural networks (involving multiple mainstream frameworks such as PyTorch, TensorFlow, Caffe, MxNet), allowing you to master various deep learning open-source frameworks through practical experience.

Here is an excerpt from the framework learning section for you to feel.

In terms of computer vision technology, it will systematically explain convolutional neural networks, object detection, OpenCV, etc., gradually deepening from detection model teaching until reaching an enhancement of core capabilities in CV algorithms.

There are many related AI beginner resources online, but many technical contents are too few, not systematic, or written incompletely and incomprehensibly, with repetitive content occupying the majority (here I weakly complain about the diversification of Baidu’s search results).

Voiceover: One copy of homogeneous tutorials is enough; be careful to filter them and do not waste unnecessary time.

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Due to WeChat restrictions, a single account can add up to 100 people a day. If there are too many, it will be restricted. Hurry up and scan the code to receive it, first come first served.