In the field of machine learning, building flexible and easily adjustable models is an essential skill for advanced machine learning engineers when faced with various complex and changing business problems. However, many engineers still have a misconception that mastering just one deep learning framework is enough to succeed.
In fact, there is no single framework in the machine learning field that can dominate the entire industry; every machine learning engineer must master multiple frameworks to adapt to business development needs.
So, is there a framework that is relatively more convenient and user-friendly? The answer to this question is yes, and here I would like to recommend PyTorch to you.
To be honest, PyTorch has gained a lot of popularity and praise among engineers in recent years due to its excellent scalability and extremely high implementation speed. First of all, PyTorch supports GPU, which can significantly enhance the efficiency of code execution. Additionally, compared to TensorFlow and Caffe, PyTorch has reverse automatic differentiation technology, allowing you to adjust custom models without starting from scratch, saving you considerable development time.
Moreover, the code in PyTorch is also more concise, intuitive, and user-friendly than that in TensorFlow, making it an excellent learning case that can help many engineers deepen their understanding of machine learning.
At this point, I must recommend Geek Time’s live course on PyTorch; this Wednesday and Thursday, there will be a 2-day live session from 20:00 to 22:00, teaching you how to get started with PyTorch hands-on. There are still a few free learning slots available!
Wang Ran Will Teach You How to Get Started with PyTorch
Head of AI Lab at Zhongwei Technology, dual master’s in Mathematics and Econometrics from the University of Amsterdam — Wang Ran, will guide you from scratch to understand the basic usage of PyTorch, the model training process, and how to use PyTorch Lighting to complete complex logic, ultimately implementing a custom neural network and validating its effectiveness.
Course Outline👇
What You Will Gain After Completing the Course
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How to implement training in various scenarios using PyTorch (multi-GPU, TPU, etc.)
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How to define your own network using PyTorch’s inherent network architecture
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How to write your own network using PyTorch’s tensor operations
Value-Packed Learning Services
This course will be taught through 4 hours of live instruction + practical training, along with value-packed learning services:
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Online Interaction with the Instructor: During the live learning process, you can ask questions to the instructor at any time;
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Efficient Community Connection: Equipped with a dedicated class manager throughout, creating an efficient learning community, gaining learning partners, with the community retained permanently;
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Practical Assignments: Besides attending classes, there will be practical assignments to verify your learning outcomes;
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Unlimited Replay of Recorded Sessions: The live instruction will be recorded and available for unlimited viewing.
How to Register?
How to Register?
Limited-Time Offer ¥8.8, refunded ¥8.8 after attending the live session, effectively making it free to learn! Scan the QR code below or click to read the original text to register👇
After purchasing the course, please be sure to add the class manager on WeChat to enter the class learning group and obtain the live session reservation link to avoid missing the course live.
Scan the WeChat QR code to add the operations manager
Reply “Live Course” to join the group and get the live link👇
