Deep Learning Framework Background

Contents of this article:

1. The concept of deep learning framework

2. Why use a deep learning framework

3. What preparations should be made to master the deep learning framework

4. Main application scenarios of deep learning

5. Comparison of common deep learning frameworks

6. Market share of deep learning frameworks

7. The status of open source frameworks for deep learning in China

8. Remarks

1. The concept of deep learning framework

　　Deep Learning Framework (Deep Learning Framework) refers to a software platform that can support the construction, training, debugging and deployment of artificial neural networks.

　　It simplifies the implementation process of deep learning tasks by providing efficient algorithm implementation, convenient data processing tools, data visualization and debugging tools, and makes deep learning applications easier and more efficient. In other words, the deep learning framework provides a fast, repeatable and scalable development environment for deep learning, helping researchers and engineers to research and develop complex deep learning models faster.

　　Common deep learning frameworks include TensorFlow, PyTorch, Caffe, Keras, etc.

Deep learning basic framework diagram (network):

2. Why use a deep learning framework

　　If you have mastered the core algorithms of deep learning, you can of course implement your own neural network model from scratch. But if you need to implement more complex models, such as Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN), you will find it impractical to implement complex models from scratch, so deep learning frameworks came into being, which Can help you easily implement your own neural network.

　　Deep learning frameworks can help us build, train, test, and deploy deep learning models more easily.

　　Using the framework allows developers to focus on model design and implementation, reducing the workload of manually writing the underlying code. In addition, frameworks often have a series of implemented algorithms and model architectures, which can help developers implement models faster and conduct experiments.

　　In addition, the framework can also provide functions such as distributed training, automatic derivation, and GPU acceleration. These functions can speed up training, shorten model design and implementation time, and speed up model iteration. No need to write CUDA code to run GPU;

　　Easy to build large computational graphs (computational graphs);

　　In short, using a deep learning framework can greatly improve the development efficiency of deep learning applications and the accuracy of the model.

3. What preparations should be made to master the deep learning framework

To master the deep learning framework, the following preparations are required in the early stage:

Basic mathematical knowledge : Deep learning involves a lot of mathematical knowledge, such as linear algebra, calculus, etc., which need to be learned and mastered.

Python programming language : Most deep learning frameworks use the Python programming language, so you need to master the Python programming language, including Python syntax, data types, functions, etc.

Data processing and analysis experience : Deep learning frameworks usually need to use large amounts of data for training and testing, so data processing and analysis skills are required.

Master the basic knowledge of deep learning : the deep learning framework is based on the theory of deep learning, so you need to master the basic knowledge of deep learning, such as neural network, activation function, loss function, forward and backward propagation, gradient descent and other algorithms; wait.

It is necessary to understand the basic principles of the framework , learn and master the basic concepts and usage of the framework.

Choose an appropriate deep learning framework : According to different application scenarios and requirements, choose an appropriate deep learning framework for learning and use, such as TensorFlow, PyTorch, Keras, etc.

4. Main application scenarios of deep learning

The main application scenarios of deep learning include but are not limited to:

Computer Vision : Deep learning has made remarkable progress in visual recognition, image classification, object detection, face recognition, etc.
Image recognition and classification : use convolutional neural network (CNN) to recognize and classify images, such as face recognition, vehicle recognition, etc.
Natural language processing : Deep learning is widely used in speech recognition, machine translation, sentiment analysis, question answering systems, etc. Use recurrent neural network (RNN) or long short-term memory network (LSTM) for natural language processing tasks such as text classification, machine translation, and sentiment analysis.
Speech recognition : use recurrent neural network (RNN) or convolutional neural network (CNN) for speech recognition, such as speech to text.
Intelligent interaction : use the deep learning model to realize intelligent question answering, intelligent customer service, intelligent assistant, etc.
Financial risk control : Use deep learning models to perform financial risk control tasks such as risk assessment and fraud detection. Such as transaction risk prediction, credit evaluation, etc.
Sound processing : Deep learning is also used in audio signal analysis, speech synthesis, speech conversion, etc. Speech recognition and natural language processing (such as voice assistants, intelligent customer service, machine translation, etc.);
Healthcare : Deep learning is also used in medical imaging diagnosis, disease prediction, and drug development. Such as medical imaging diagnosis, disease prediction, etc.;
Autonomous driving : Deep learning is also widely used in areas such as autonomous driving and intelligent transportation.
Game field : (such as game intelligence AI);
Agricultural field : (such as crop identification, pest prediction, etc.);
Industrial fields : (such as robot control, defect detection, etc.);
Energy field : (such as energy forecast, energy consumption reduction, etc.);
Security field : (such as anomaly detection, malicious attack recognition, etc.).
Recommender system : use deep learning model for user behavior analysis and personalized recommendation. It is also widely used in recommendation algorithms and advertising recommendations.

There are also some interesting applications:

Face2Face: A software developed by Stanford University in the United States that allows people in the video to talk to you. The software can recognize the movements and expressions of a person when they speak, and reproduce them on the face of another person, becoming a celebrity in seconds not a dream
Pix2pix: Image Translation with Adversarial Neural Networks (GANs). In short, it translates one image into another. For example, you can input a stick figure or some color blocks, and the network can retouch and create a new complete image based on the original input image, allowing you to become a soul painter in seconds.
Automatic writing generation : According to some handwriting, using deep learning technology can use these handwriting to write other words and sentences. For example, Alex Graves of the University of London taught the computer to write in many different styles.
Automatically identify text in pictures, crack verification codes, or quickly process scanned copies to generate electronic documents.
Predicting what people will look like when they are young or old can help police find missing persons.

　　In short, deep learning has a wide range of applications in various fields, and with the advancement of technology, its application scenarios will become more and more extensive.

5. Comparison of common deep learning frameworks

　　The following is a comparison table of several common deep learning frameworks (2021)

frame	release time	development company /organize	Programming language	GPU support	automatic Derivation	distributed training	Application field
TensorFlow	2015 _	Google	Python/C++	support	support	support	Speech recognition, image recognition, natural language processing, etc.
PyTorch	2016 _	Facebook	Python/C++	support	support	support	Image recognition, natural language processing, etc.
MXNet	2015 _	Apache Software Foundation	Python/C++/ Julia/R/Scala	support	support	support	Image processing, natural language processing, recommendation system, etc.
Caffe	2013 _	standard university	C++	support	not support	not support	Image processing, video analysis, etc.
Hard	2015 _	Francois Chollet	Python	support	support	support	image processing, natural language processing, etc.
Theano	2007 _	University of Montreal	Python	support	support	support	Image processing, speech recognition, recommendation system, etc.
Torch	2002 _	Ronan Collobert	Lua	support	support	support	Image processing, recommendation system, natural language processing, etc.

It should be noted that the above table is for reference only.

Each framework has a different function and scope,

Choosing an appropriate deep learning framework needs to be considered according to specific application scenarios and requirements.

6. Market share of deep learning frameworks

　　The latest market share data shows that the most popular deep learning frameworks include TensorFlow, PyTorch and Caffe.

　　According to the "2019 AI and Deep Learning Market Survey Report" released by market research company O'Reilly, TensorFlow is the most popular deep learning framework, with 57.2% of respondents using it. PyTorch was a close second with 37.1% of respondents using it. Caffe and Keras are also popular, capturing 16.2% and 13.7% of the market, respectively.

Comparison of the market share of several common deep learning frameworks (2021)
TensorFlow：	more than 40%,	It is one of the most popular deep learning frameworks.
PyTorch：	more than 25%,	Developed and maintained by Facebook, it has gradually gained attention and been widely used in recent years.
Hard:	more than 10%,	Often used with TensorFlow, providing a simpler and easier-to-use framework.
Caffe：	about 5%,	The market applies to areas such as computer vision and image processing.
MXNet：	about 5%,	The marketplace is developed and maintained by Amazon for large-scale distributed deep learning.

7. The status of open source frameworks for deep learning in China

China's deep learning open source framework market has formed a pattern of three strong players

　　IDC, an international authoritative data research organization, released the report "China's Deep Learning Framework and Platform Market Share, 2022H2". The report shows that Baidu ranks No. 1 in China's deep learning platform market in terms of overall market share, further expanding its lead. China's deep learning open source framework market has formed a top three structure, and the top three framework market shares exceed 80%.

IDC analysts concluded in the report that from the perspective of coverage, PaddlePaddle’s awareness in academia and industry has been further improved, and it is a frame known when it is mentioned; , transportation, logistics industry awareness and share increased significantly. Educational institutions, ISV/SI, engineers in the traditional IT industry, junior developers, etc. are increasingly using Paddle.

In recent years, technological innovation and industrial development in the field of artificial intelligence are inseparable from deep learning frameworks and platforms. The deep learning platform is connected to chips and applications on top, which is equivalent to "the operating system in the era of intelligence". As China's first self-developed, open source and open industry-level deep learning platform, Flying Paddle has reached the international leading level in core technologies such as unified dynamic and static core framework, large-scale distributed training, and high-performance reasoning deployment integrated with training and pushing; In terms of functions, it has a rich industrial-level model library, end-to-end development kits, tool components, and a zero-threshold AI development platform EasyDL, a full-featured AI development platform BML, a learning and training community AI Studio, and efficiently supports deep learning model development and training , deployment, etc., lowering the threshold for AI technology application; ecologically, Flying Paddle promotes the construction of a comprehensive ecological system, including developer ecology, education ecology, enterprise ecology, and hardware ecology, and accelerates industry-university-research applications through ecological sharing and co-creation Collaborative innovation and development.

IDC analysts pointed out in the report that under the current international environment, the share of domestic frameworks and platforms is expected to grow further. Supporting edge intelligence and providing rich scene-oriented pre-training models will become an opportunity to drive the growth of frameworks and platforms in the next 1-2 years.

China Academy of Information and Communications Technology released the "Deep Learning Platform Development Report (2022)" in-depth learning framework and empowerment platform evaluation dimensions

8. Remarks

This article is a personal learning record, and the information comes from the Internet