Welcome to the Big Models Course! This course will take you deep into the realm of big models and help you master the skills to apply these powerful models. Whether you're interested in the field of deep learning or looking to apply big models in real-world projects, this course will provide you with valuable knowledge and hands-on experience.

Big models refer to deep learning models with enormous parameters and complex structures. These models perform exceptionally well when dealing with large-scale datasets and complex tasks like image recognition, natural language processing, speech recognition, and more. The emergence of big models has sparked significant changes in the field of deep learning, leading to breakthroughs in various domains.

In this course, you'll learn the fundamental concepts and principles of big models. We'll delve into the foundational theory, developmental history, commonly used big models, and the evolving techniques like Prompts and In-context learning within LLMs (Large Language Models). As the course progresses, we'll dive into the practical applications of big models. You'll learn how to deploy highly regarded big models such as Stable Diffusion and ChatGLM2-6B onto SOPHON's latest generation deep learning processor, the SOPHON BM1684X. The SOPHON BM1684X is the fourth-generation tensor processor specifically introduced by SOPHON for the field of deep learning, capable of 32TOPS computing power, supporting 32 channels of HD hardware decoding, and 12 channels of HD hardware encoding, applicable in environments such as deep learning, computer vision, high-performance computing, and more.

Whether you're inclined toward in-depth academic research on big models or their industrial applications, this course will provide you with a robust foundation and practical skills. Are you ready to take on the challenge of big models? Let's delve into this fascinating field together!

SOPHON SE5 Deep Learning Computing Box is a high-performance, low-power edge computing product based on processors and modules, which targets a wider range of scenarios than module-shaped products. It is equipped with SOPHON's independently developed third-generation TPU processor BM1684, capable of an INT8 computing power of up to 17.6 TOPS, and can simultaneously process 16 channels of high-definition video, providing intelligent computing for various security, comprehensive security, education, finance, and security inspection projects.

The SE5 Deep Learning Computing Box is a small-scale server based on edge computing, supporting algorithms from various industries. With a complete ecosystem, it facilitates users in porting well-trained models. It not only supports facial recognition algorithm models but also supports dozens of auxiliary models, making it highly versatile for different scenarios. It can be applied in indoor and outdoor environments such as parks, communities, commercial buildings, and semi-enclosed integrated outdoor scenarios, without relying on X86 architecture servers. It fully utilizes its internal ARM resources, enabling independent integrated application development.

This computing box boasts high computing power and strong market competitiveness, while also preserving a portion of high-precision computing power. In scenarios requiring high-precision computing power, it retains the advantage of high precision, such as in dynamic visual unmanned retail cabinets and product recognition in smart refrigerator systems. Practical applications of the SE5 include deployment as an edge facial server in parks for entrance identification or park monitoring, facial payments in smart canteens, student facial recognition in home-school systems, access management in school dormitory systems, implementation of dish recognition algorithms in catering systems for billing purposes, replacing traditional security personnel in image recognition, higher accuracy in machine judgment, reduced training costs for security personnel, faster passage, and intelligent assistance in security checks. The diverse range of implantable algorithm models enables diversified application scenarios.

This course will explain the SE5 computing box and its application processes. By taking this course, you'll gain a clear understanding of this experimental box and become familiar with applying it to specific scenarios.

Course Highlights

Systematic teaching: From product introduction to environment setup and application processes.

• What is the SE5 Experiment Box?
• How to set up the application environment?
• How are applications developed?

Comprehensive materials: The course includes instructional videos, documentation, code scripts, etc., providing detailed and rich information

• Abundant video materials
• Comprehensive application guidance
• Clear code scripts

This course introduces the hardware circuit design and peripheral resource operation methods of the CV1812H development board from the "Huashan Pi" series. It also provides tutorials on using Deep learning hardware acceleration interfaces and some basic Deep learning examples.

Huashan Pi (CV1812H development board) is an open-source ecological development board jointly launched by TPU processor and its ecological partners. It provides an open-source development environment based on RISC-V and implements functions based on vision and Deep learning scenarios. The processor integrates the second-generation self-developed deep learning tensor processor (TPU), self-developed intelligent image processing engine (Smart ISP), hardware-level high-security data protection architecture (Security), speech processing engine, and H.264/265 intelligent encoding and decoding technology. It also has a matching multimedia software platform and IVE hardware acceleration interface, making Deep learning deployment and execution more efficient, fast, and convenient. The mainstream deep learning frameworks, such as Caffe, Pytorch, ONNX, MXNet, and TensorFlow (Lite), can be easily ported to the platform.

Course Features

1. Rich and complete content materials, including hardware design of the development board, SDK usage documents, platform development guides, and sample code scripts.

2. Scientific and reasonable learning path. The course introduces the development board and basic routines, and then delves into the internal system architecture and code learning to understand the development details. Finally, practical projects are introduced to fully utilize the development board, which can also serve as a reference for users to develop on their own. 

3. Suitable for different audiences. For users who want to quickly use the development functions, the course provides many code samples for use and function display, which can be easily modified and combined to achieve different functions. For enthusiasts or developers in related industries, the course also provides detailed SDK development usage guidelines and code sample analysis documents, which can help users to gain in-depth understanding. 

4. long-term maintenance of the course. In the future, we will launch more development courses to communicate with developers and grow together.

Link to the open-source code for the Huashan Pi development board：https://github.com/sophgo/sophpi-huashan.git