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Do you also have such confusion:
“After watching countless introductory tutorials, I still haven’t crossed this threshold…”
The course starts with an introduction to basic concepts of PyTorch, making it completely stress-free for beginners.
Step by step, it will guide you through modeling, training, and deployment.
In just eight short lessons, you will truly get hands-on experience with PyTorch!
Since it’s so impressive, let’s take a look at this tutorial together~
Course Outline
1. Introduction to PyTorch
Although this course leans towards practical operations, the official content is still very friendly, assuming everyone is a beginner and gradually introducing from the basic concepts.
2. Introduction to PyTorch Tensors
In the second lesson, we start formally introducing concepts:
Tensors are the core of PyTorch, similar to NumPy’s ndarrays, representing a multi-dimensional matrix.
Tensors in PyTorch can run on GPUs and significantly speed up execution.
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Specific lessons include:
Creating PyTorch Tensors Mathematical or logical applications Tensor copying How to transfer to GPU Manipulating tensor shapes PyTorch-Numpy Bridge
The tutorial provides many typical examples of tensor operations:
For example, creating a random tensor between -1 and 1, taking its absolute value results in all positive values; you can take its arcsine value, as the values are between -1 and 1, returning an angle.
In addition, tensors in PyTorch can perform linear algebra operations, such as determinants or singular value decomposition;
Data statistics, summaries, calculating mean, standard deviation, maximum, minimum, etc., are all within reach.
3. Automatic Differentiation Mechanism
Automatic differentiation is an important reason why PyTorch can run quickly; it can build flexible frameworks in deep learning.
Gradient computation can also push machine learning based on backpropagation.
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Specific lessons include:
Why use automatic differentiation? Typical case demonstration Automatic differentiation in model training Using automatic differentiation Automatic differentiation analysis Advanced APIs
In specific examples, the tutorial uses a simple recurrent neural network or RNN to demonstrate.
4. Building Models
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Specific lessons:
Modules and parameters Common neural network layer types Other layers and functions
Using an example of a letter recognition model, the tutorial first demonstrates how to build a neural network:
Once the network is built, converting it to code completes the model construction.
5. PyTorch TensorBoard Support
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Specific lessons:
TensorBoard visualization Drawing scalars & visualizing training models Visualizing using embeddings Visualizing data sets
In this step, the tutorial uses a model to perform a simple training: recognizing different types of clothing.
By tracking training, data is visualized, and then TensorBoard is used to view the model itself, further visualizing the data and its internal relationships.
6. Training Models
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Specific lessons:
Modeling with Torch.NN Automatic gradient computation Learning TensorBoard visualization
7. Using Captum to Explore PyTorch Model Interpretability
Captum is a model interpretation library that provides interpretability for many new algorithms (such as: ResNet, BERT, some semantic segmentation networks, etc.).
It can help us better understand the specific features, neurons, and neural network layers that affect model prediction results.
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Specific lessons:
Basic concepts Special examples Layer attributes Examples Exploring model interpretability with Captum & Captum insights
8. Model Deployment and Inference
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Specific lessons:
Evaluating PyTorch models TorchScript TorchScript & C++ TorchServe deployment
In the final step, the constructed model is evaluated.
Links
PyTorch official tutorial: https://www.youtube.com/channel/UCWXI5YeOsh03QvJ59PMaXFw/videos
If the purely English tutorial is a bit challenging, you can refer to the Bilibili Chinese version (@爱可可-爱生活): https://www.bilibili.com/video/BV1qh411U73y?p=1
And the Chinese text tutorial: https://pytorch-cn.readthedocs.io/zh/latest/notes/extending/#pytorch
Reference links:[1]https://www.youtube.com/channel/UCWXI5YeOsh03QvJ59PMaXFw/videos[2]https://www.bilibili.com/video/BV1qh411U73y?p=1[3]https://pytorch-cn.readthedocs.io/zh/latest/notes/extending/#pytorch
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