In the field of machine learning, it is necessary for advanced machine learning engineers to build flexible and easily adjusted models in the face of various complex and changeable business problems. However, many engineers have the misconception that they can go anywhere if they master a deep learning framework.
In fact, there is no single framework for machine learning to dominate the industry, and every machine learning engineer must master multiple frameworks to adapt to the needs of business development.
Is there a framework that is more convenient to use? The answer to that question is yes, and HERE I recommend PyTorch.
To be fair, PyTorch has won over many engineers in recent years for its excellent scalability and high implementation speed. First, PyTorch supports gpus, which makes code much more efficient. PyTorch also has reverse auto-derivation technology compared to TensorFlow and Caffe, allowing you to adjust your custom model without having to start from scratch, saving you a lot of development time.
In addition, PyTorch’s code is much cleaner, more intuitive, and more user-friendly than TensorFlow’s, making it a good case study for many engineers to understand machine learning in greater depth.
PyTorch will be taught by Ran Wang, the head of the AI Lab at Zhongweizhi. There are still a few free places available!
Teach you how to use the PyTorch
Ran Wang, head of the AI Lab at The University of Amsterdam, who has a master’s degree in mathematics and econometrics, will walk you through the basic usage of PyTorch, model training process and complex logic using PyTorch Lighting from scratch. Finally, a self-defined neural network is implemented and the effect is verified.
Course outline????
After learning, you will gain
- How to use PyTorch to train in various scenarios (multi-GPU, TPU, etc.)
- How can I use PyTorch’s inherent network architecture to define my own network
- How do YOU use PyTorch’s Tensor calculations to write your own network
How to watch the class?
Scan the QR code below, or click to read the original article to sign up at????
Read the original article to learn! PyTorch!