Under the new exhaust: Embedded AI learning training project (with 20G code information / learning video)

Some fans ask me: "What is the wind port of embedded developers?"

Painted outer sound: The nature of the wind is, in fact, it is an imbalance in talent supply and demand for a while. To put it bluntly, it is due to the industrial mutation, keen capital quickly enters, leading to a lot of development in a short period of time, require a lot of developers.

The current embedded development is increasingly inclined to intelligence, that is, what we said is intelligent hardware (hardware + software).

Taking the Baidu robot as an example, the core of the robot is the brain, that is, "data and algorithm", but the robot’s large brain machine body body can be active as human beings, can say that it will be said, walking, then you must rely on embedded technology.

Although the artificial intelligence has been in the year, it is a large stage of the real business that is in the field of the IoT side AI embedded, which has a very huge application scenario.

Therefore, I personally think that under the promotion of Internet of Things and artificial intelligence, embedded will usher in more development opportunities in the next 5-10 years, on the one hand, embedded development will usher in more application scenarios, another The technical system in terms of embedded development will also gradually abundantly, thus expanding the technical boundaries of the Internet of Things.

There have been many AI frameworks that have gradually supported end-side AI, such as Google’s Tensorflow Lite and Tensorflow Lite Micro, and Huawei’s Mindspore Lite. Chip vendors ST and NXP have also introduced some of the tools and DEMOs that are partially-oriented.

I have been engaged in embedded development work, and I have always paid attention to the development of embedded AI. I believe that with the arrival of the 5G era, AI has huge potential in various industries.

The technical personnel under every air is always the most difficult job, since the mobile Internet, excellent developers have been doubled.

At the moment, I personally optimistic about the development potential of the embedded AI industry in the future, and don’t have to be too anxiety that has developed to the bottleneck. First of all, it is first to consolidate your strength, let yourself caught when you come. Live it.

Then, in such a era, I have recommended 3 o’clock to improve their workplace value:

  • Further enriching its own knowledge structure, you must focus on artificial intelligence technology;

  • Pay attention to the accumulation of industry experience, there are very many links in embedded development and industry (future embedded development gradually covered with traditional industries);

  • Pay attention to related technologies in industrial Internet.

Recently, a set of AI entry must-have learning materials, strongly recommending everyone to learn, the author Wang Xiaotian, has a practical experience in the 8 years of human intelligence, and is currently at one of Bat, the AI ??algorithm senior technical expert, France TO3 colleges and universities (Computer Science Graduated from Milotics.

He issued more than 10 papers in artificial intelligence and chip field, with deep academic backgrounds and extensive experience in projects and business.

During the work period, it is mainly responsible for the work of artificial intelligence line CV and NLP related algorithm, promoting human machine mixing intelligence, semantic segmentation, machine translation, iris identification and other modules of core algorithm research and optimization. There is an in-depth study of image classification, object detection, target tracking, automatic driving, computer architecture, etc.

He has theory and actual experience, knowing that beginners learn pain points. To be honest, people such as such qualifications are very difficult.

Due to work needs, I am also studying in this tutorial. Although I have been engaged in this industry for many years, when I look at this tutorial, I can still check the shortage, harvest, I believe that it is Ai entry, or already already have already A certain work experience, this learning information is worth learning.

All all relevant content have been packaged, and the summary has a link to Baidu cloud. The little intimate is that some brothers don’t buy Baidu Cloud members, and can download 2MB + / s speed. Specially prepared the download tool for everyone.

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Next, I will introduce it in detail, how do this information learn?

First, entry AI, master a deep learning framework is one of the necessary survival skills.

Therefore, the tutorial will start from the deep learning framework, bring you from scratch training network, to independently build and design convolutional neural networks (including mainstream classification and detection networks), and conduct training and reasoning of neural networks (involving pytorch, tensorflow Multiple mainstream frameworks such as Caffe, MXNET, let you master a variety of depth learning open source frameworks through actual combat.

Intercepting the Framework Learning Site Directory Everyone feels.

Deep study and neural network

  • Deep learning profile

  • Basic depth learning architecture

  • Neurons

  • Activate function detailed solution (Sigmoid, Tanh, Relu, etc.)

  • Sensibility understand hidden layers

  • How to define a network layer

  • Loss function

Reasoning and training

  • Neural network reasoning and training

  • Detailed BP algorithm

  • Normalized

  • BATCH NORMALIZATION

  • Solve the fit

  • DrOPOUT

  • Softmax

  • Training process of hand pushing neural network

Training neural network from zero

  • Use Python from zero to achieve neural network training

  • Experience summary of building neural networks

Deep learning open source framework

  • Pytorch

  • Tensorflow

  • Caffe

  • Keras

  • Optimizer detailed (GD, SGD, RMSProp, etc.

In terms of computer visual technology, the system will systematically explain the convolutional neural network, target detection, OpenCV, etc., from the test model teaching, gradually, until the core capacity of the CV algorithm is reached.

Online-related AI has a lot of entry resources, but many technical content is too small, no system, or write uncommon and semi-understanding, duplicate content accounts for the vast majority (herein, there is a variety of search results of Baidu here).

Painting outside: Homogeneous tutorials have a sufficient, pay attention to screening, do not waste unnecessary time.

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