72 页 PPT,带你梳理神经网络架构含 PyTorch (English)

Generated: 2026-06-21 00:00:22

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I finally found a deep learning architecture resource that actually saves lives. It's not one of those "master in three days" packages—it's a 72-page PPT recommended by Red Stone, written by Santiago Pascual de la Puente, with every page containing runnable PyTorch code snippets. What it solves is very simple: when you need to quickly review mainstream architectures before an interview, or suddenly have to build a model for a new project and need reference code, you don't have to flip through hundreds of pages of documentation or dig through papers for details. Fully connected, RNN, CNN, GAN, U-Net, QRNN—it strings them all together, each structure accompanied by code. In simple terms, it's an "architecture quick-reference manual" you can sink your teeth into and use right away.

Basic Architectures? Skip Them and You'll Hit a Wall

The PPT is divided into three parts. The first is called Basic Architectures—fully connected, recurrent, and convolutional. When I first saw it, I thought: "Do I really need to look at this? Too basic!" I wanted to skip straight to the advanced stuff.

Reality slapped me hard. Once, while working on a sequence model, I messed up the input dimensions of an LSTM. I debugged forever and couldn't find the problem. It turned out I didn't even understand how hidden_state is passed in an RNN. It was so embarrassing.

I had no choice but to sheepishly flip back to the basic section of the PPT and carefully go through the computational graph of a fully connected layer and the usage of nn.Linear. It not only drew the network structure but also mapped the mathematical formulas to code. For instance, for a fully connected layer: the formula is Y = activation(X·W + b), and the code is F.relu(linear(x)). The best part was that it explicitly annotated the weight shapes—most blogs skip that, but it's crucial for debugging. Later, when I wrote CNNs, I relied on it to calculate the output size of convolutional layers, getting it right every time without trial and error.

If you still think the basics aren't important? I've taken that fall for you.

Advanced Architectures: This Is Where It Gets Awesome

The second part, Advanced Architectures, I can't recommend enough. It covers six directions: Hybrid CNN/RNN (QRNN), Autoencoders, Deep Classifier/Regressor, Residual Connections and U-Net/SEGAN, GAN (DCGAN). Each structure takes just three to five pages, but the core ideas are explained crystal clear.

Speaking of which, another story. Once, I wanted to use an autoencoder for anomaly detection. I built my own network, but the loss just wouldn't go down, and the reconstructed images were a blurry mess. Then I flipped to the Auto-Encoders pages in the PPT and found it emphasized one thing: the encoder output dimension shouldn't be too small, otherwise you lose too much information. I had set the latent size to 2; the example in the PPT used at least 128. After I changed it, the effect was immediate. I felt saved. The code might be simple, but the key parameters and design principles are all there.

And the GAN part—it's worth a special shout-out. The PPT uses DCGAN as an example, giving the standard implementation for both the generator and discriminator: kernel sizes, strides, where to put batch norm. The first time I trained a DCGAN, I copied it but got clever—I changed the order of nn.BatchNorm2d, putting it after the convolution but before the activation? No, I placed it elsewhere. The training blew up—the generator couldn't learn anything. After that, I strictly followed the PPT's order, and it stabilized immediately.

Order, parameters, details—if any one is off, everything collapses. That's the counterintuitive part: the things you think are simple are often the deadliest.

Don't Skip the Conclusions, Even Though It's Only Two or Three Pages

The PPT ends with a Conclusions section on how to choose an architecture based on the task. It's short, but oh so useful. For example, classification tasks should prioritize CNN + fully connected layers, sequence prediction uses RNN or Transformer, and generation tasks use GAN or autoencoder depending on the situation. Later, when I was doing technical proposal reviews, I often referenced these pages' logic. A whole bunch of people would be arguing in meetings over model selection, going at it. I'd just flip to those pages, and the room went quiet. Saved so much time on debates. So don't underestimate that little ending part—it can save you when it counts.

What the PPT Doesn't Tell You, But You Must Know

I have to be honest with you: this PPT was put together in 2019, and the code is based on PyTorch 1.0. Now PyTorch is at 2.x, and some APIs have changed. Although the usage of torch.nn.functional is mostly the same, the default parameters for torch.optim are no longer what they were. When I ran its DCGAN code, I found that using Adam's default lr (0.001) caused the discriminator loss to drop to zero quickly, and the generator couldn't learn at all. You need to change it to 0.0002 and betas to (0.5, 0.999)—this is old wisdom from the original DCGAN paper, which the PPT doesn't mention. Don't change it? Prepare for a crash.

Also, the code in the PPT is more for demonstration, keeping only the core logic. For example, in the QRNN part, it only implements the convolutional gated recurrent unit; the full training loop? Not there. You'll need to add the DataLoader, loss calculation, and backpropagation yourself. Beginners might get stuck right here. I suggest you use it together with the "Zero to Mastery PyTorch" project—its code comments are very detailed and fill in the engineering details the PPT skips.

How I Use It: As a Dictionary, Not a Novel

I don't read through this PPT from start to finish—that would be silly. I use it as an index. Want to learn about a new architecture? First flip to the PPT and see if it's there. If it is, spend 10 minutes going over the structure and code, then reproduce it in your framework. If it's not in the PPT, go to the original paper or an in-depth article on Zhihu (like those on Transformer full picture, BERT principles). It's ten times faster than diving into a paper directly, believe it or not.

The PPT also mentions an online URL (docs.google.com/present...), but Google Drive might need a proxy to access. In China, you can find a backup link on Red Stone's personal site, or just search the PPT title—someone has reposted it.

Advanced Advice: After Digesting These 72 Pages, If You Want to Fly, Follow This Order

If you've absorbed these 72 pages and want to go further, I suggest this order:

First, actually reproduce every code snippet in the PPT. Don't just look at it. Print out the inputs and outputs to understand how the dimensions change. If you don't run it yourself, you'll always think you understand when you don't.

Second, use a self-check checklist (like the one from that Transformer article) to verify if you truly understand. Many pitfalls only show up when you test.

Third, extend to graph neural networks. The PPT doesn't cover GNNs, but you can go to a tutorial on Zhihu about Graph Attention Networks, and run a node classification task with PyTorch Geometric. Feel the world get bigger again.

Fourth, focus on engineering deployment. The PPT only covers model architecture,