Introduction of Neural Networks
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Welcome to another deep dive. We're tackling neural networks today. That's right. And wow, do we have some cool stuff to look at. Yeah, we've got. We've got academic papers, AI tool guides, even some real code snippets. It's going to be a good one. So if you're ready to boost your neural network knowledge. Yeah.

Buckle up. We're going to unpack how they work. How they work. Explore all the cool things they can do. What they can do. And set you up with everything you need to really start studying this field. I think what's so cool about this deep dive is that you're going to leave not just with like the facts about neural networks, but the actual like mental models to really grasp this whole complex field. I like that. It's like building a neural network. Yeah. For your own brain. Awesome. I love that. Yeah.

Okay. Well, speaking of building, let's jump right into some code. Let's do it. We've got this snippet here from Dyson to deep learning, and it features this function called get data loader. Now, this function is all about grabbing these mini batches of data. Okay. Have you ever tried to eat a whole pizza in one bite?

No. No. Good idea. Probably not a good idea, right? Not advisable. Yeah. So that's kind of why we use these mini batches. Instead of like overwhelming the entire network with the whole data set at once. Right. We break it down into these smaller, more manageable chunks. Like digestible little snacks. So this get data litter function is like our pizza slicer. Yeah. Carefully portioning out the data.

Yeah. I like that. Makes it efficient for the network to process. Yeah, that's a great analogy. And the code even hints at some of these really neat tricks. Okay. Like shuffling the data and controlling the batch size.

Oh, very important. It's almost like tossing a salad, you know. Yeah. Make sure the network gets a good mix of ingredients in each bite. Exactly. You don't want all the tomatoes at the bottom. You don't want just like cheese pizza every time. No. Got to mix it up. But why is all this so important for studying neural networks? Well, we'll think about it like this. Okay. The network learns by

By finding these patterns in the data and then figuring out how to differentiate between the different features. Right, right. Like how is a cat different from a dog? Yeah, yeah. Mini batches help make this process a lot smoother, a lot faster. So the network is basically chewing on these smaller pieces of data, adjusting its internal settings based on what it's finding, and then just repeating this process over and over.

Until it gets really good at recognizing those patterns. So like if you were training a network to tell cats and dogs apart. Exactly. You'd feed it those mini batches of cat and dog pictures. Exactly. And it would just kind of like fine tune its vision along the way. Exactly. Until it could be like, oh yeah, that's a bat. That's a dog. Yeah. And just like with our pizzas. Yeah. We want our toppings to be labeled correctly. So like no confusing pepperoni for mushrooms.

Absolutely. It's very important. No label noise. No label noise. We want clean data. Yeah, we want clean data. Just like you wouldn't want to study from a textbook full of errors. Right, exactly. The neural network needs accurate data to learn. So this whole foundation of beta prep, understanding mini batches. It's crucial. That's crucial. For building and studying neural networks that actually work. Absolutely.

All right. So speaking of things that work. Yes. Let's move on to some powerful applications of neural networks. Cool. So dive into deep learning takes us to modern convolutional neural networks. These CNNs are like the stars of computer vision. They are rock stars. They are. Yeah. I mean, think about it. Self-driving cars.

recognizing pedestrians your phone unlocking with face id yeah all thanks to cnn's it's pretty cool right it's very cool cnn's are incredible at extracting visual features from images so imagine them like these super sleuths analyzing every single part of an image to identify things like edges textures yeah and eventually the whole object itself right so they

they work by applying these series of filters. It's kind of like using magnifying glasses with different lenses to gradually understand what they're seeing. So it's like a detective piecing together clues with a magnifying glass. You got it. Oh, that's awesome. And a

really important moment for CNN's was the ImageNet competition. Okay. Where researchers were competing to classify a massive data set of images. Oh, wow. And this challenge really pushed CNN's to the forefront of computer vision. It led to some major breakthroughs. Wow. In how we actually teach machines to see. So CNN's revolutionized how we approach

image analysis. I mean, they're the foundation for so many AI driven features that we just like use every single day. Yeah. And our source also mentions this kind of new kid on the block. Okay. Transformers. Ooh. Are these like the shape-shifting robots from the movies that we're talking about?

Not quite, no. Okay. But they are transforming the AI landscape. Oh, okay. All right. Yeah. So CNNs are great with image tasks, but transformers have emerged as this really strong competitor. Okay. Particularly in natural language processing, and it's a rapidly evolving field. Yeah. New architectures are being developed all the time, like transformers. So this is definitely something to keep an eye on. Yes, definitely. Right. I'm adding transformers to my study list. Yeah. Good idea. Yeah.

Before we get too far ahead of ourselves, dive into deep learning. Introduce us another really fascinating concept. Okay. LSTMs. LSTMs, yes. Or long short-term memory networks. That's the name.

They're designed to handle sequential data like text or even time series. Think of them as the memory masters of the neural network world. They've got great memories. Yeah. And what's remarkable about LSTMs is their ability to remember processes.

previous inputs in a sequence. Okay. So imagine you're reading a book. Yeah. You need to remember what happened in the earlier chapters. Right. To understand what's going on now. Exactly. LSTMs work in a similar way. Okay. Which makes them perfect for things like machine translation. Okay. Or even text generation. Oh, wow. Yeah. So like

How a chatbot can remember what you said earlier and kind of hold a conversation. Exactly, yeah. It's like they have a built-in note-taking system. It's like a little notepad in their brain. That's wild. Yeah, and the code even shows an example of an LSTM. Oh, cool. Defined using LSTM Scratch. All right. So you can kind of see how these networks are built. So like when your phone predicts

the next word you're going to type. Exactly. That's an LSTM. That's an LSTM in action. Learning the patterns of language. Yeah. Figuring out what you're going to say next. It's

mind-blowing how these networks can just like mimic our own brains. They're very smart. But wait, dive into deep learning. Yes. Also dives into the concept of attention. Attention, yes. Is this like when the teacher tells you to pay attention in class? Well, it's kind of similar in the sense that it's about focusing on what's important. Okay. So attention mechanisms give neural networks the ability to focus on specific parts of the brain.

parts of the input, just like we do when we're trying to understand something complex. Right. So instead of processing everything equally, the network learns to prioritize what's most relevant. Oh, that's cool. Yeah. So it's almost like reading a long article and highlighting the key sentences. It's kind of like filtering out all the extra stuff. Filtering out the noise. Yeah. And just focusing on the main points. Exactly. Yeah.

That's a great analogy. Thank you. And you mentioned before something called dot product attention. Yes. Sounds pretty technical. It does sound a little scary. Yeah. But is there a way to understand it? Yeah. Without like needing a math degree? Okay, good. So dot product attention is basically all about figuring out how similar different parts of the input are. Okay. So imagine you're reading the sentence, the cat sat on the mat.

Right. Each of those words is represented by a vector. Okay. Which is like a unique numerical fingerprint that kind of captures its meaning. So each word has its own set of numbers. Exactly. That kind of define what it represents. Exactly. Okay. So dot product attention calculates the dot product between these vectors. Right. Which is essentially measuring how...

aligned or similar they are. Oh, okay. So words that are really closely related in meaning will have a higher dot product, whereas unrelated words will have a lower dot product. So it's like a mathematical way to figure out which words are friends and which ones aren't. You got it. That's awesome. And then the similarity score determines how much attention each word gets. So words that are really important for whatever task we're doing, they're going to get more attention.

Okay. Whereas less relevant ones kind of fade into the background. So the network's kind of like saying, these words over here are the VIPs. Exactly. Let's focus on these. That's a great way to put it. Yeah. And this ability to focus has revolutionized natural language processing. It really has. Yeah. Especially with those transformers we've been talking about. They could go hand in hand. Enough teasing. Enough teasing. All right. All right.

Let's finally talk about these transformers. Let's do it. What makes them so special? Okay. And why are they causing such a buzz in the AI world? Well, dive into deep learning gives us a great starting point with vision transformers. Okay. These are transformers specifically designed for computer vision tasks. Okay. And they've been making some serious waves. And the code even shows this.

VIT block. Yes. Is that like a transformer building block? It is. You got it. The VIT block is like a fundamental component of that vision transformer architecture. Gotcha. Here's the really interesting part. Okay. Instead of processing images pixel by pixel, like traditional CNNs, vision transformers break the image down into these

patches. Okay. And they treat each patch as a separate input element. So it's almost like taking a puzzle and analyzing each piece individually before figuring out the whole picture. That's a great way to think about it. Oh, that's cool. Yeah. And then these patches are transformed into these really meaningful representations that allows the network to capture both

local details within a patch, but also global relationships between them. Oh, wow. So it's getting like the small picture and the big picture at the same time. Oh,

Oh, that's wild. Yeah. You mentioned earlier that these vision transformers have something in common with the human brain? They do. How so? So just like our visual system processes information hierarchically. Okay. So like from simple edges to more complex objects. Okay. Vision transformers process

process information in stages too. So the early layers focus on understanding those individual patches, whereas later layers combine those representations to understand the whole image. - Oh wow. - Yeah, it's a fascinating parallel. - That is fascinating. - Between artificial and biological vision. - That we're creating AI that might see the world similar to how we do. - That's pretty amazing. - That's really cool. - Yeah. - Okay, so let's shift gears back to the learning process a bit.

Remember those pesky local minima we talked about? Yes, I do. How do these advanced architectures like transformers avoid getting stuck in those valleys while they're climbing toward peak performance? Well, that's a great question. Yeah. And it highlights how important these optimization algorithms are. Okay. Remember, dive into deep learning gave us that visual opportunity.

Yeah. Using the function FX to show these local minimal. You like that landscape analogy. Exactly. That hilly landscape. Yeah. We don't want our network getting complacent down in a valley. No, we don't. When there's a higher peak to be discovered. Exactly. We want them to reach their full potential. Exactly. So how do we avoid these pitfalls? Well, one of the algorithms that helps us navigate this tricky terrain. Okay. Is Adagrad. Ah, Adagrad. Our trusty compass. Exactly. Adagrad. Adagrad.

cleverly adapts what we call the learning rate during training, which basically ensures that the network is taking appropriate steps towards the optimal solution. So think of it as a dynamic step size. You take big steps when the terrain's nice and smooth, but you take smaller, more cautious steps when you're getting close to the peak. So it's like having a really smart guide. Exactly. Who knows when to sprint and when to carefully tread?

You got it. To make sure we don't miss this summit. That's the idea. Okay, cool. And this careful navigation. Through the complex world of a neural network's loss function helps us prevent those local minima pitfalls. So we've got these powerful architectures like vision transformers. Yes. Cleverly navigating the learning landscape with the help of algorithms like Adagrad.

We do. It feels like we're really starting to understand the inner workings. Yeah, we're getting there. Of neural networks. You're making progress. Yeah. But this is just the beginning. Oh, yeah. We have a whole other world to explore. Absolutely. The world of AI tools. The tools. Yeah. It's a fun part.

And how they're making neural networks accessible to everyone. Everyone, yeah. Not just computer science experts. Yeah, exactly. It's very exciting. So stay tuned because we're about to dive into that in the next part of our deep dive. Can't wait. Welcome back to the deep dive. Right. So before we jump into those transformers. Okay. Let's revisit this concept of attention. Yeah. That we talked about earlier. Okay. It's a real game changer. Yeah, it really is. In how neural networks process information. Yeah.

It's like giving the network this laser focus. Yeah. Letting it prioritize the most relevant info. Uh-huh. Filter out all that noise. The noise. Exactly. And you mentioned before something called dot product attention. Yes. Being a key player in all of this. It is. It sounds a little bit technical. Yeah. It sounds a little scary. Yeah. Yeah.

But is there a way to kind of wrap our heads around it? Yes. Without needing a math degree? Absolutely. Okay, good. So dot product attention boils down to calculating the similarity between different parts of the input. So imagine you're reading a sentence like the cat sat on the mat.

Each of those words is represented by a vector. Okay. Which is like a unique numerical fingerprint that captures its meaning. So each word has its own set of numbers. Exactly. That kind of define what it represents. Exactly. And then dot product attention calculates the dot product between these vectors. Okay. Which essentially measures how aligned or similar they are. Okay.

Okay, so words that are closely related in meaning will have a higher dot product. A higher dot product, yeah. While unrelated words have a lower one. Lower, exactly. So it's like a mathematical way of figuring out which words are buddies and which ones are strangers. That's a great way to put it. And then this similarity score determines how much attention is

each word receives. So words that are super relevant to whatever task we're doing are going to get more attention, while less relevant ones will fade into the background. So it's kind of like the network saying, these words over here, these are the VIPs. The VIPs, exactly. Let's focus on these. Focus on these guys. And this ability to selectively focus has really revolutionized natural language processing. It really has, yeah. Especially with these transformers we keep talking about.

Especially with transformers, yes. All right. Enough teasing. Okay, okay. Let's finally shine the spotlight on these transformers. Let's do it. What makes them so special? Okay. Why are they causing such a buzz in the AI world?

Well, dive into deep learning gives us a great entry point. Okay. With vision transformers. Okay. These are transformers that are designed specifically for computer vision tasks. Right. And they've been making waves lately. The code even shows this VIT block. Yes. Is that like a transformer building block? You got it. Okay. The VIT block is like a fundamental component of that vision transformer architecture. Gotcha. But here's the really interesting part. Okay. Instead of programming,

of processing images, pixel by pixel, like traditional CNNs. Vision transformers break the image down into these patches and treat each patch as a separate input. So it's kind of like taking a puzzle and analyzing each piece individually before figuring out the whole picture. That's a great way to think about it. Oh, that's cool. Yeah. And then these patches are transformed into these really meaningful representations.

Which allows the network to capture both the local details within a patch, but also the global relationships between them. So it's getting like the small picture and the big picture at the same time. Exactly. That's a great way to put it. That's wild. Yeah. You mentioned earlier that these vision transformers have something in common with the human brain. They do. How so? So just like all...

Our visual system processes information hierarchically, you know, from simple edges to more complex objects. Vision transformers process information in stages too. So those early layers focus on understanding the individual patches, whereas the later layers combine those representations to understand the whole image.

Oh, wow. Yeah. It's a fascinating parallel. That is fascinating. Between artificial and biological vision. That we're creating AI that might see the world. Yeah. Similar to how we do. It's pretty amazing. That's really cool. Yeah. Okay. Let's shift gears back to the learning process a little bit.

Okay. Remember those pesky local minimas we discussed earlier? I do. I do. How do these advanced architectures like transformers avoid getting stuck in those valleys while they're climbing toward peak performance? That's a great question. Yeah. And it really highlights the importance of those optimization algorithms. Okay. You know, dive into deep learning gave us that visual using the FX algorithms.

FX to show these local minima. Yeah, like that landscape, hilly landscape analogy. Exactly, that hilly landscape. We don't want our network getting complacent in a valley when there's a higher peak to be discovered. Exactly. We want them to reach their full potential. Exactly. So how do we avoid those pitfalls? Well, one of the algorithms that helps us navigate this tricky terrain is Adagrad. Ah, Adagrad.

Our trusty compass. Exactly. Adagrad cleverly adapts what we call the learning rate during training, which ensures the network is taking appropriate steps towards the optimal solution. So think of it as like a dynamic step size. You're taking big steps when the terrain is nice and smooth. And then you take steps.

smaller, more cautious steps as you're getting closer to that peak. So it's like having a really smart guide. Exactly. Who knows when to sprint and when to carefully tread. You got it. To make sure we don't miss the summit. That's the idea. Okay. And this careful navigation through this complex world of a neural network's loss function helps us prevent those local minima

So we've got these powerful architectures. Yes. Like vision transformers. Vision transformers, yeah. Cleverly navigating the learning landscape with the help of algorithms like Adagrad. Like Adagrad, yeah. It feels like we're really starting to understand the inner workings. Yeah, we're getting there. Of neural networks. We're making progress. Yeah, and in the next part of our deep dive. Yes.

We'll shift our focus to the practical side of things. Okay. Exploring the world of AI tools. The tools. And how they're making these neural networks accessible to everyone. Everyone. Not just the computer science experts. Exactly. Very exciting stuff. Yeah. Very cool.

Welcome back to the Deep Dive. It's time to wrap up our exploration of neural networks. And I'm really excited to get into like the practical side of all this. Yeah, the applications. Yeah, we've covered the theory, the architectures, the training. All the building blocks. Now let's talk about how we can put all of this knowledge into action. Yeah, how to actually use this stuff. Exactly.

Our chat GPT and AI tools notes document highlights a whole bunch of AI tools that are making neural networks more accessible and applicable than ever before. It's like having a superpower at your fingertips. It really is. Remember those coding assistants we mentioned like Code Peer and Ask a Die? Oh, yeah. Those are great. Imagine you're like a programmer working on this complex project. Yeah. These tools can be like having a really experienced partner. Yeah, like a mentor. Yeah.

suggesting code completions, identifying potential bugs. Yeah, even recommending solutions. Wow. It's really cool. It's amazing how AI is kind of like

leveling the playing field. Yeah, making things more accessible. Yeah, making those specialized skills more accessible to everyone. Exactly, democratizing AI. And speaking of accessibility, let's not forget about the creative applications. Oh yeah, the fun stuff. Yeah, remember those manga generating tools? Girl Canvas and Pixton, yeah. It's like having an AI collaborator

That can help bring your artistic vision to life. Exactly. Even if you can't draw. Even if you can't draw, which I can't. Yeah, me neither. So that's great for me. So Neural Canvas can create illustrations just from text prompts. It's like having an AI artist. Yeah. Bringing your ideas to life. That's incredible. And then Pixton helps you build those comic narratives. Wow. With AI assistance. So it's like AI is really bridging the gap between like

Imagination and creation. It really is. It's mind blowing. It's very exciting to see where things are going. So we've covered so much ground in this deep dive. We have. From the building blocks of neural networks to these cutting edge tools. That's some of the cool stuff. That are putting so much power at our fingertips. Yeah, we've explored so much. But before we wrap up. Yeah. I want to bring it back to you, the listener. Yes.

Why should you care about all of this? Yeah, why is this important? How can this knowledge about neural networks benefit you? Well, understanding neural networks really opens up

world of possibilities. Whether you're a student, a professional, or just someone who's curious about the future of technology. This knowledge can empower you to solve problems, to explore new ideas, to create things you never thought possible. It's like expanding your mental toolkit. Exactly. Equipping yourself to navigate a world where AI is becoming increasingly integrated. Yeah, AI is everywhere.

into our lives. Yeah, it's true. So as we conclude this deep dive, I want to leave you with a final thought. A parting thought.

Remember that neural networks are inspired by the human brain. They are, yeah. But they're not limited by the same constraints. That's right. They can process massive amounts of information. Huge amounts of data. Learn really complex patterns. Yeah. And generate creative output. In ways we don't even fully understand. In ways we're only beginning to understand. Exactly. So don't be afraid to dive in. Yeah. To experiment. That's right. To push the boundaries of what's possible. It's an exciting time.

The future of AI is being shaped by those who are willing to explore its potential. Absolutely. And apply their knowledge to these real world challenges. The real world. Yeah. And who knows, maybe someday you'll be the one creating the next groundbreaking AI application. Yeah. That changes the world. That's the dream. Thanks for joining us on this deep dive into the world of neural networks. Thanks for listening.