With the promotion of the Internet of Things and artificial intelligence, embedded systems will usher in more development opportunities in the next 5-10 years. On 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.
We must improve together; when the wave comes, we won’t be overturned.
It is essential to understand that while historical processes affect individual destinies, they often do so dramatically. The wheels are rolling forward; how can you stay in place?
Recently, I have compiled a set of essential learning materials for beginners in AI, strongly recommending everyone to study them. The author, Wang Xiaotian, has 8 years of practical experience in the field of artificial intelligence and is currently employed at one of the BAT companies as a senior AI algorithm technical expert, holding dual master’s degrees from one of the top three universities in France (in computer science and applied mathematics).
He has published over ten papers in the fields of artificial intelligence and chips, possessing a solid academic background and rich project and business implementation experience.
During his work, he is mainly responsible for AI business line algorithms related to CV and NLP, 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 in image classification, object detection, target tracking, autonomous driving, and computer architecture.
He combines theoretical and practical experience, deeply understanding the learning pain points of beginners. To be honest, it is rare to find someone with such qualifications.
(The material is extensive; only a portion is extracted)
Due to work requirements, I am also learning this tutorial myself. Although I have been in this industry for many years, I still find gaps and gain a lot from 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 relevant content has been packaged into a link on Baidu Cloud. A small thoughtful detail is that for those who haven’t purchased a Baidu Cloud membership, you can download it at a speed of 2MB+/S, and we have specially prepared a download tool for you.
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In such a historical context, I have three suggestions for technical personnel engaged in embedded development to enhance their workplace value:
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Further enrich your knowledge structure, focusing on artificial intelligence technology;
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Accumulate industry experience, as embedded development is closely related to various industry fields (future embedded development will gradually cover traditional industries);
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Pay attention to relevant technologies in the industrial internet field.
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 scratch to train networks, independently building and designing convolutional neural networks (including mainstream classification and detection networks), and conducting 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’s a glimpse of the framework learning section directory.
Deep Learning and Neural Networks
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Introduction to Deep Learning
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Basic Deep Learning Architecture
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Neurons
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Activation Function Details (sigmoid, tanh, relu, etc.)
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Understanding Hidden Layers
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How to Define Network Layers
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Loss Functions
Inference and Training
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Inference and Training of Neural Networks
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Detailed BP Algorithm
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Normalization
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Detailed Batch Normalization
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Solving Overfitting
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Dropout
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Softmax
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Manually Pushing the Neural Network Training Process
Training Neural Networks from Scratch
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Implementing Neural Network Training from Scratch using Python
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Summary of Experience in Building Neural Networks
Deep Learning Open-Source Frameworks
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PyTorch
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TensorFlow
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Caffe
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MXNet
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Keras
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Detailed Optimizers (GD, SGD, RMSprop, etc.)
In the field of computer vision technology, convolutional neural networks, object detection, OpenCV, etc., will be systematically explained, gradually deepening from detection model teaching until achieving core capabilities in CV algorithms.
There are many related AI beginner resources online, but many technical contents are too scarce, lack structure, or are not comprehensive, with repetitive content occupying the majority (here’s a slight complaint about the diversity of Baidu’s search results).
Voiceover: One copy of homogeneous tutorials is enough; be sure to filter and not waste unnecessary time.
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