AI, ML, and Neural Networks Explained — Full Breakdown & Transcript

[00:02] something that's absolutely transforming our digital landscape. AI, machine learning, neural networks, and deep learning. And we're going to demystify

[00:14] every single one of these terms, all the confusing buzzwords, and tech terms like supervised learning, reinforcement learning, all of them once and for all. If you've been hearing these terms everywhere, but you're not quite sure

[00:29] how they all fit together, don't worry, because that's exactly the purpose of this video to break it down in a way that finally makes everything click. Not just understanding each term separately, but more importantly, how they all

[00:44] but more importantly, how they all relate in this huge sea of AI ecosystem. And we will sprinkle in a bit of history to understand the evolution and timelines behind these terms as well. So, it's going to be a fun but

[00:57] valuepacked and very informative video. So, by the end of this video, you will know not just what these technologies are, but how they're reshaping our digital world and why they are so

[01:10] powerful and exciting. So, let's get started and demystify the world of artificial intelligence. But first, a quick thing. We noticed that 80% of people who watch our videos every month are still not subscribed yet. Me and our

[01:27] entire video editor team are putting immense time and effort into each and every video to make it as valuable and highquality as possible for you. So, if any of these videos have helped you at all, giving it a like and subscribing to

[01:42] the channel and becoming officially part of our community is the simplest way for you to support us and it would mean the world to us. Seriously, thank you and let's get into it. Imagine you're working at Netflix and you're

[01:57] responsible for recommending shows to millions of users. Every day, people watch different content. They rate shows and browse through different categories

[02:09] and lists and so on. Your job is to make sure each user sees recommendations they'll actually enjoy. Now, back in Netflix's early days, they

[02:21] might have used a simple approach like if a user watches Stranger Things, recommend other sci-fi shows. or if they watch three romantic comedies in a row, show them more romantic comedies. Engineers would write these rules

[02:37] explicitly in the code. And they would need to define every pattern and rule by hand. This approach works for simple cases, but it's very limited because what about the countless subtle patterns in viewing behavior? What if someone

[02:53] in viewing behavior? What if someone watches sci-fi movies on week nights but prefers documentaries on weekends? Or what about shows that combine multiple genres or appeal to unexpected audiences? This rule-based approach is

[03:09] what we call symbolic AI where human programmers explicitly code the rules and knowledge into the logic. And this was actually how AI started in the

[03:21] was actually how AI started in the 1950s. and dominated until 1990s. viewing history from millions of Netflix users, you have the viewing patterns,

[03:36] ratings, just a bunch of interaction data from all the users. Now, instead of data from all the users. Now, instead of trying to write all the rules yourself as a logic, what if you could build a system that looks at this massive data

[03:51] system that looks at this massive data set and discovers patterns on its own? This system could analyze what shows are watched together or how ratings correlate with viewing habits or how preferences change over time. It could

[04:06] preferences change over time. It could identify patterns so complex that no human could have programmed them explicitly because we would just not know that these patterns even exist. And this approach of letting computers learn

[04:20] patterns from data rather than following handcoded rules is what we call machine learning. So machine learning is a subset of AI or in other words just one

[04:32] way to achieve artificial intelligence by giving machines an ability to learn automatically and self-improve from experience without being explicitly programmed to follow some rules. And here is an interesting fact. In 1997,

[04:49] a chess playing supercomputer developed by IBM called Deep Blue defeated Gary by IBM called Deep Blue defeated Gary Kasparov in chess, a chess grandmaster at the time. And it was this breakthrough moment in AI that everybody

[05:05] was talking about. But interestingly, Deep Blue did not use machine learning. Deep Blue did not use machine learning. It used symbolic AI with brute force search and handcrafted or manually programmed evaluation functions which

[05:20] means it did not learn from data or improve its chess skills through experience. All the strategies and evaluation criteria were programmed by evaluation criteria were programmed by human experts and chess grandmasters

[05:35] into deep blue. So for a long time machine learning was not even considered the best and the most efficient way to create artificial intelligence. Instead a lot of focus and work was actually done in symbolic AI field. Now to

[05:50] understand the comparison between these two better let me give you an analogy. Imagine you are teaching someone to bake cookies by giving them an explicit cookies by giving them an explicit recipe with precise steps like add two

[06:05] recipe with precise steps like add two cups of flour, mix for 3 minutes, bake at 350° for exactly 12 minutes. So this approach uses clear rules that must be followed exactly step by step. So this approach

[06:20] of creating explicit rules and logic for a computer to follow where everything is defined through logical rules would be the symbolic AI. Now imagine instead of

[06:32] giving someone a recipe, you show them 100 samples of successful cookies and failed cookies. You don't tell them exact measurements or temperature or

[06:44] duration, but they observe patterns. When the Dow looks this way, the cookies turn out well. When temperature is turn out well. When temperature is between, I don't know, 347 and 351

[06:56] degrees, the cookies turn out the best. So, they learn from examples rather than following explicit rules. And this approach of having computers learn patterns from data, kind of reverse engineer the rules from the data instead

[07:11] of following pre-programmed rules. This would be machine learning. Now this is very important to understand in the comparison of these two and why the breakthrough of machine learning was much bigger than the breakthrough of

[07:26] symbolic AI. The biggest difference between these two was that machines could now learn patterns that humans never thought about. Let me explain. Alpha Zero was a computer program developed by Deep Mind that learned to

[07:42] play chess by playing against itself without human intervention. And what was remarkable is that it developed strategies that were completely strategies that were completely different from traditional human chess

[07:56] theory. For example, it would make moves that looked bizarre to grandmasters, like sacrificing pieces with no immediate tactical advantage. But these immediate tactical advantage. But these moves would later prove to be brilliant

[08:11] long-term strategic decisions because Alpha Zero beat all the human chess players. And AlphaZero discovered these patterns entirely on its own through self-play which was a moment that showed us how machine learning can find

[08:25] solutions that humans never programmed or even considered logically. So this was the first time that a machine went beyond the human logic or knowledge

[08:37] base. And before AlphaZero's breakthrough, symbolic AI was actually the dominant approach for decades in artificial intelligence. It was actually the primary paradigm between 50s and all the way through '90s. The symbolic

[08:52] approach was much more popular because it aligned with how we thought human reasoning worked through explicit rules and logic. So most of the work was happening in this field. Computer scientists built expert systems with

[09:06] hard-coded rules for everything from medical diagnosis to chess playing to everything that an intelligent machine was supposed to do. Now, machine learning existed as a concept in parallel, but only started to become a

[09:21] dominant approach after 2000s with breakthroughs like Alexnet in 2012 and breakthroughs like Alexnet in 2012 and Alpha Zero in 2017. And as I said, that changed everything because that breakthrough was revolutionary and

[09:36] completely different from the breakthrough of Deep Blue because people saw that machine learning programs could suddenly discover patterns and strategies that were not explicitly programmed and might never have been

[09:50] discovered through traditional human reasoning. And as a result of that started absolutely outperforming traditional symbolic systems in more and more domains. So that specific differentiator or that skill of machine

[10:05] learning algorithms actually made machines way more intelligent than any symbolic systems had ever been before. So starting from year 2000, we had a big

[10:18] shift from symbolic AI to machine learning. And that's where things got interesting as machine learning field developed more and more. So let's continue our story. Machine learning comes in different

[10:33] flavors. Have you heard all these weird terms like supervised learning, unsupervised learning, reinforcement learning, all these strange names that don't make any sense if you don't know what they are. So, let's break them down

[10:48] simply so you understand clearly what they all mean. they all mean. Let's say Netflix has data on hundreds of thousands of users who have already rated the show. So, they can use this

[11:03] so-called label data to train their system. Now, let's stop here for a second. Label data, what does it mean exactly? Well, since we are on Tech with Donna, where we explain everything in detail, we don't skip things here. We're

[11:17] going to explain simply what label data means. Think about how you learned to recognize fruits as a child. Your parents showed you an apple and said, "This is an apple." They showed you banana and said, "This is banana." Or

[11:32] showed you a picture of an orange and told you, "This is called an orange." Those names were the labels that helped you learn. They didn't just throw a bunch of fruits in front of you and say just figure out what each one is called

[11:45] by yourself. So that's how we teach machines what a specific piece of data means by adding labels to them. In our Netflix example, label data works like

[11:57] this. When a user gives a show five stars, they're essentially telling Netflix's algorithm, "This is something I like." When a user gives a show one star, they're saying, "This is something I don't like." These ratings are the

[12:10] labels. They're teaching the algorithm what good user review and bad user review looks like for the shows. So, when Netflix wants to recommend shows to you, even if you haven't rated shows yourself, the algorithm can look at

[12:25] users who have similar viewing habits as you, and what those similar users rated highly or poorly. then it's going to assume people who watch what you watch tend to love this new show. So there's a high probability that you will like it

[12:41] too. Or you can think users with viewing patterns like yours typically hate this genre. So let's not recommend it. Without these star ratings as labels,

[12:53] Netflix would not know what you might enjoy or not. So instead of random guessing, using ratings as labels helps algorithm make educated predictions by algorithm make educated predictions by associating viewing behavior to actual

[13:08] user satisfaction. So now Netflix collects data points like user A watched 20 comedies last month, binged Korean dramas, never finishes horror movies, and rated this new show five stars. They

[13:23] gather thousands of these subtle patterns from millions of users with different viewing habits and their corresponding ratings. The machine learning system then identifies patterns like users who watch comedies and Korean

[13:38] dramas but avoid horror tend to rate this show highly. Netflix does not explicitly program these rules. The algorithm discovers them by analyzing the labeled data. This approach of learning from labeled examples, this is

[13:53] called supervised learning because we are supervising the algorithm by showing it the correct answers during training. The labels guide the learning process,

[14:05] helping the system understand which patterns lead to which outcomes. And this is why companies invest so much in collecting and maintaining highquality label data sets because the quality and quantity of your label data often

[14:22] matters more than the sophistication of your algorithm. Because as the saying goes in machine learning, garbage in, garbage out. Even the most advanced machine learning algorithms cannot produce great results with bad training

[14:35] data. Now what if Netflix wants to understand different types of viewers without using any ratings or labels? Let me explain.

[14:47] Imagine Netflix algorithm looking at all their user data. What people watch, when they watch, how much they watch, but without looking at any ratings. It's just curious. Are there natural groupings of viewers that we haven't

[15:01] noticed before? The algorithm might discover something interesting on its own, like, "Hey, I've noticed there is a group of people who only watch content group of people who only watch content between 11 p.m. and 2:00 a.m., mostly

[15:14] watch 20 to 30 minutes at a time, and jump between different shows. We didn't tell the algorithm to look for this pattern. It found it by itself. This might reveal a viewing category Netflix hadn't even thought of. Let's call them

[15:28] Insomnia viewers. Or it might find weekend binge watchers group who consume entire seasons in 2 days or background noise viewers who put on shows while

[15:41] difference here, nobody told the algorithm what groups to look for. There were no labels saying this person is a binge watcher or this person watches for background noise. The algorithm discovered these patterns entirely on

[15:56] its own by looking at the raw viewing data. So these are patterns that we could not have known even existed. So there's no way to have labels for patterns that we don't even know are out there. In this approach where the

[16:11] algorithm finds patterns and structures without being given correct answers, this is called unsupervised learning. So machine is learning without any supervision or guidance of what each data means. We're saying here's a bunch

[16:26] of data. Go figure out if there are any interesting patterns worth paying attention to. It is like giving a child a box of toys and watching how they naturally sort them by color, by size, by type without telling them how they

[16:41] should be organized. and the child may discover the categories by themselves just like the algorithm discovers viewer segments on its own. Now, why is this important? Unsupervised learning gives us superpowers that supervised learning

[16:57] simply cannot because it helps us discover things that we don't know that we don't know. In the real world, we often have situations where we don't even know what patterns exist in our data or what questions to ask. Even

[17:12] unsupervised learning can expose entirely new categories or segments or anomalies we never thought to look for. Plus, it saves tremendous resources

[17:24] because labeling data is an expensive and timeconuming process. Imagine having to manually rate millions of Netflix shows at manually tag millions of images

[17:37] or show ratings. But in addition to that, unsupervised learning also works with raw unlabelled data, which means that it's perfect for situations where labeled data is either scarce or it's impossible to get.

[17:55] Now, let's talk about the third major type of machine learning called reinforcement learning. And this is quite different from what we've discussed so far. Think about how you learned to ride a bike. Nobody gave you

[18:08] thousands of labeled examples of correct and incorrect bike riding. And nobody just let you figure out random patterns on your own. Instead, you tried something, you fell down, which was a negative feedback. You adjusted your

[18:22] approach and eventually stayed upright, which was positive feedback. And that's essentially how reinforcement learning works through trial and error with rewards and penalties. Now, let's use a fun example. Imagine Netflix is creating

[18:38] a system that autonomously edits movie trailers to maximize viewer interest. The system starts by creating random trailers. Some of them showing action scenes first, others revealing plot twists right away, trailers that are

[18:54] highlighting the main character and so on. Each trailer then gets shown to users and Netflix then tracks whether people watch the movie after seeing that trailer. When the users watch the movie after seeing a specific trailer style,

[19:09] the algorithm gets a reward. So, it gets a positive feedback. When users ignore the movie, it gets a penalty as a negative feedback. The system does not know beforehand which trailer style will work best, right? So, it discovers this

[19:26] through experimentation and feedback. And over time, as it shows more and more trailers and then analyzes the effects of each trailer, this reinforcement learning system learns that, let's say, action movie fans respond better to

[19:43] trailers with explosions in the first 5 seconds, as an example. Or drama viewers relationships are highlighted in the trailer. Then we have horror movie fans that click more when the trailer builds suspense without revealing the monster.

[20:00] The algorithm was not supervised with labeled examples saying this is a good trailer and this is a bad trailer. It figured out what works by trying different approaches and then measuring the results every time through

[20:13] reinforcement. And this approach is exactly how reinforcement learning works. The algorithm learns by interacting with an environment, in this case Netflix users, taking actions like showing different trailer styles and

[20:27] receiving feedback whether users watch the movie or not, and then adjusts the strategy and repeats the process. What makes reinforcement learning special is that it can optimize for long-term rewards, not just immediate feedback.

[20:43] rewards, not just immediate feedback. Because maybe a mysterious trailer leads to fewer immediate clicks but higher viewer satisfaction later resulting in more completed movies and subscriptions or higher ratings. So a good

[20:57] reinforcement learning system can figure this out as well. And there are some really interesting realworld applications for reinforcement learning. applications for reinforcement learning. For example, think of self-driving cars

[21:10] that are learning to navigate safely through countless road scenarios. For example, let's say when a car breaks too hard and passengers jolt forward, that's a negative feedback. When it drifts over a lane marking, that's another negative

[21:26] feedback. But when it maintains exactly 3 seconds of following distance on highways and executes turns that feel natural to passengers, it gets positive

[21:38] feedback or reward. And this feedback helps the car learn the ideal driving behavior. So every action it takes results back into feedback like gaining points or losing points just like in a video game. And the car learns that was

[21:54] a good behavior, that was a bad behavior and adjusts accordingly and over time learns what good is and what bad is. Or think about robots learning to walk or manipulate objects. Again, they do something, they get immediate feedback

[22:08] something, they get immediate feedback when they drop an object or fall over so that they can adjust their movement through lots of trial and error. Game playing AIs are actually other example of reinforcement learning like the ones

[22:21] that mastered chess because they receive very clear rewards for winning and penalties for losing. And this actually helps them discover strategies by learning what chain of moves lead to

[22:36] winning and what lead to penalties. And that's exactly what helped them discover strategies that humans never even thought of. So in each case the system thought of. So in each case the system improves through experience rather than

[22:49] explicit teaching or unsupervised pattern finding. So we have explored the three main approaches machines use to learn from data. Now you might be

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[24:58] to find the link in the description. And now, let's continue with the video. So supervised learning, unsupervised learning and reinforcement learning are different ways of how machine learning systems learn from data. And to make

[25:13] these concepts click even better, let's see an analogy of how these three actually all compare to each other. So supervised learning is like learning to bake cookies without a detailed recipe but with many examples of both perfect

[25:30] and failed cookies to examine. So you're given ingredients which is the input given ingredients which is the input data and actual finished cookies which are the labeled examples where in each cookie you can observe their golden

[25:45] brown color. Feel the perfect chewy centers with crispy edges. Maybe smell the vanilla butter balance and taste the sweetness. And on the other side, you

[25:58] may have burnt, rock hard, failed cookies or undercooked ones which are too soft. So you basically know which ones are good and which ones are bad examples. And without being told exact temperatures or timing or techniques for

[26:15] baking these cookies, you must now study the patterns across hundreds of examples the patterns across hundreds of examples until you recognize which combination of ingredients, heat, time, and so on consistently produce that ideal cookie.

[26:31] learning entirely from these examples rather than following explicit rules or recipe just as supervised learning algorithms discover patterns from pre-labeled training data. So the goal here is to figure out the best recipe to

[26:47] baking perfect cookies better than any existing recipe. Now unsupervised learning is like being given a refrigerator full of ingredients but no guidance whatsoever. Nobody tells you what dishes to make or how the

[27:03] ingredients should be combined. Nobody gives you a sample data of this is wellmade, this is badly made. You have to figure out everything from scratch. So you start cooking from all these ingredients and you start noticing

[27:16] natural patterns like these ingredients share similar textures and these ones share similar textures and these ones create comparable colors and these ones create this type of colors when combined. You might group ingredients

[27:30] into categories, which is called clustering, or even identify unusual combinations, which is anomaly detection. And as a result, you may discover entirely new recipes that no human has tried before, but taste

[27:46] amazing. As an example, maybe finding that cardamom enhances chocolate in a way that traditional recipes never considered. So the goal here is to discover new taste combination and dishes that don't yet exist. And the

[28:01] reinforcement learning is like being a chef who wants to maximize the customer satisfaction for her restaurant. So, you start experimenting in your kitchen, ingredients and cooking techniques, similar to being given a refrigerator

[28:17] with all the ingredients, and you were just basically trying to figure out what new dishes to make, but with a difference here, which is every time you prepare a dish, you go out and get feedback from your customers. They rate

[28:31] the taste, presentation, the texture, the originality maybe, and so on. The main part here is that you get feedback and you adjust based on that feedback. important to note that nobody tells you exactly what to change. No one says add

[28:48] more salt here or cook it 2 minutes less because your customers don't know what you need to add or adjust. They just know whether they like it or not. So you only see the feedback scores of your customers, how they rate your dishes,

[29:02] and those scores are either going up or down. And through this constant cycle of experimentation and feedback, you gradually understand which actions lead to higher scores, meaning customers liking your dishes and which ones make

[29:18] them dislike your dishes. So you can adjust your experience accordingly. And after hundreds of iterations, you develop an optimal cooking strategy that consistently produces highscoring dishes. Even though no one ever

[29:32] explained exactly what makes a great dish, you've learned the perfect balance of ingredients, timing, techniques, purely through this feedback loop of rewards and penalties. So the goal here is to create signature dishes that

[29:48] maximize customer satisfaction in your restaurant. So we've explored supervised, unsupervised, and reinforcement learning, which are the three main approaches machines use to learn from

[30:03] data. But you might be wondering what's actually happening under the hood. like how does a computer process all this information and make these intelligent decisions? Where's the mechanism that powers all this? Well, we had

[30:18] traditional or classical machine learning algorithms with some confusing names like linear regression or decision trees that actually showed some impressive results but still had very limited learning capabilities because

[30:35] they were all designed to follow strict recipes with clear instructions. For example, for our Netflix use case, a linear regression model might look at factors like how many action movies you've watched and how many Tom Cruz

[30:51] films you've rated highly in order to calculate a simple score for recommending Mission Impossible. But it would struggle to understand more complex patterns like a reason why you might enjoy one movie but not another

[31:06] with very similar surface features. a decision tree. Another algorithm might decision tree. Another algorithm might say if user watched sci-fi and is under say if user watched sci-fi and is under 30, then recommend Star Trek, which is

[31:19] again following predefined rules. And Netflix could build these trees to make pretty reasonable recommendations. But they would also quickly hit some limitations because imagine trying to capture all the nuances of movie

[31:34] preferences in simple yes or no questions like did they watch more than three comedies last month or did they finish watching or abandoned the last drama. These trees simply could not capture the subtle patterns that make

[31:49] capture the subtle patterns that make recommendations truly personalized. So these traditional algorithms hit a performance ceiling around 70 to 80% accuracy for complex tasks. Now, they had a lot of advantages like they were

[32:04] computationally efficient and worked pretty well for basic recommendations, but they could not capture the intricate relationships between users, content,

[32:16] viewing patterns, the contextual factors that make Netflix's recommendation feel that make Netflix's recommendation feel almost magical today. But in parallel to these algorithms, there was another one that mimicked human brain. Basically, it

[32:32] was a way for machines to learn similar to how human brain learns. So, how does to how human brain learns. So, how does a human brain learn or how does a child learn and make associations? Think about how a child learns to identify a dog for

[32:48] the first time. They don't start with a rule book or definition that lists all the specific features that dogs have like this kind of fur and they bark like this and they have this type of mouth. Instead, this is how it happens. At

[33:03] first, they might see a dog and a parent points out and says, "Oh, there's a dog." And this creates a very basic connection in their brain. Then they see more dogs, bigger dogs, small ones, different colors, different breeds. And

[33:17] different colors, different breeds. And each time their brain strengthens certain neural connections and they start to associate all the different features that they see in these different types of dogs under one

[33:30] category of dog. Like they know that dogs have fur. Most of them they have four legs, they bark, they wake tail. So all of these become associated with dog in their little brains. But then they might see a cat and excitedly say dog.

[33:47] Their parent would correct them saying no that's a cat. And this is the brain's error correction process. So those neural connections get adjusted. The brain essentially thinks hm prediction was wrong. Let me adjust my

[34:00] understanding. Then the child sees a bunch of cats. So they categorize what is what by seeing a bunch of examples and identifying patterns between all the dogs. and patterns between all the cats and so on. So through hundreds of

[34:15] examples and correlations, the child's brain builds a complex network of connections that can identify dogs with amazing accuracy, even dogs that they've

[34:27] amazing accuracy, even dogs that they've never seen before. And brain is made up of neurons that connect to each other to exchange information. And they build or adjust knowledge constantly with every piece of the information they get from

[34:41] the world. And the specific way of machines learning that mimics this human machines learning that mimics this human learning process is called a neural network. So neural networks were actually a step up from these

[34:55] traditional algorithms because they could model nonlinear relationships. they could discover some patterns on their own rather than following explicit rules which I described as being the major the core part of machine learning

[35:11] breakthrough. They could start to identify relationships between features that weren't obvious to humans that were programming the system. However, these early neural networks still had significant limitations. Basically they

[35:28] had input which was the data then one layer that was processing that input that information like human brain and then outputting the result. So it was like a super simplified brain and with this one layer between input and output

[35:42] they could not build hierarchical representations of data. So for Netflix example they were not much more complicated and sophisticated as the traditional algorithms. So they could also understand some basic relationships

[35:57] like this user likes action movies, but they couldn't understand more nuanced patterns like this user enjoys character-driven stories with moral ambiguity regardless of genre. Like that was not possible for those earlier

[36:13] neural networks to understand. The real revolution came that changed everything completely and gave us the breakthrough of AI that we are experiencing today and that change was adding more layers to

[36:28] that change was adding more layers to process the data in layers just like our brain does. So each additional layer allows the network to learn even more allows the network to learn even more complex and detailed patterns. The first

[36:41] layer might detect simple patterns like user likes action scenes. The next layer might identify a more detailed nuance like user enjoys complex characters. An

[36:53] even deeper layer might recognize that user loves plot twists in character-driven stories. So with each additional layer, the network can learn more complex, more detailed patterns. And that's why Netflix could now predict

[37:10] with pretty amazing accuracy not just what shows you like, but even what specific elements of a show will keep you watching. And neural networks with you watching. And neural networks with multiple layers is what's called deep

[37:24] neural networks because of the depth of the layer hierarchy. And the field became known as deep learning. And to simplify this whole concept even more, think of neural network like a team of detectives working together to solve a

[37:41] mystery. And they work in layers. The first layer of detectives, they collect the basic clues, which is the input layer. They pass these clues to specialized investigators who look for specific patterns, which are the hidden

[37:56] layers. And each one of them basically does the work and passes that on to the next one for even more detail analysis. And finally, the chief detective puts everything together and makes a prediction about who committed the

[38:11] crime, which is the output layer. So basically, you can think of neural networks like a team of people passing information to each other. The first person sees the raw data like user's viewing history, picks out some basic

[38:25] patterns and passes this information to the next person. And each subsequent person in the chain identifies increasingly detailed and more sophisticated patterns based on what the previous people noticed. And by the end

[38:39] of the chain, the team has transformed this raw data into highly complex this raw data into highly complex insights. So in Netflix recommendation example, the input layer would receive all the data about a user. What they've

[38:54] watched before, how long they watched, what they rate it highly, what type of day they usually watch, and many other factors like this. This data gets passed through these hidden layers where each artificial neuron processes a specific

[39:12] pattern or a feature in the data. And finally, the output layer generates predictions like this user has a 95% chance of enjoying Stranger Things or this user would probably not enjoy this documentary. So they can automatically

[39:27] learn that people who watch cooking shows on weekends but extra movies on week nights might enjoy a specific new thriller series without us ever programming that specific pattern. Because these layers learn this type of

[39:42] detailed nuance associations from the vast amount of data by processing them layer after layer. So the first layer might have identified basic patterns like this user watches action movies while deeper layers capture more complex

[39:58] patterns like this user enjoys character-driven dramas where every following layer extracts more detailed nuances from the data. And this is why deep neural networks have become the dominant approach for complex machine

[40:15] learning tasks. They can discover incredibly complex patterns without being explicitly told what to look for because we humans might never thought of programming this explicitly because we don't know that these patterns even

[40:28] exist. And that makes deep neural networks incredibly powerful for everything from recommendation systems to image recognition to language translation. And neural networks serve as the underlying technology for any of

[40:44] the three learning approaches in machine learning, whether supervised, unsupervised, or reinforcement learning. Now, here's what's the most interesting. We actually knew about neural networks with multiple layers for decades. The

[40:59] fundamental math and concepts actually existed back in 1980s. So why were neural networks not even considered seriously back then? And why did people

[41:12] work on symbolic AI instead of machine learning? What changed? Well, neural networks with many layers require significantly more computing power than traditional machine learning algorithms or neural networks with just one or two

[41:29] layers. So there's a direct relationship between the depth of your network or number of layers it has and the computational resources needed to train it effectively. So here's what was happening before the deep learning

[41:44] revolution. There were two massive barriers that prevented these models from working well. The first one was computing power limitations. Think about what happens in a neural network. For each additional layer, you're adding

[42:00] potentially millions of parameters that need to be calculated, adapted, optimized. In the early days, trying to train even a modest deep neural network

[42:12] could take weeks or months. on hardware that was available back then which made experimentation and progress painfully slow. The explosion of GPU computing power changed that. So graphics processing units or GPUs which were

[42:29] originally designed for video games turned out to be perfect for the parallel computations needed in neural networks. So suddenly training that might have taken months could be done in days or even hours with GPUs instead of

[42:46] much less powerful CPUs. And the second critical factor was data. Deep neural networks are extremely data hungry. A shallow network might function pretty

[42:58] well with thousands of examples, but deep networks often need millions or billions of examples to produce impressive results that it is producing now. So before the internet era, collecting and storing massive amounts

[43:12] collecting and storing massive amounts of data was very difficult. Now we have billions of images online. Think of social media and all the content that we humans have generated. We also have massive sea of text data from the web

[43:25] massive sea of text data from the web like blog posts, articles, books, social media posts. Plus, we have detailed user behavior data from online platforms like Google search, the click patterns, the content consumption patterns. All of

[43:40] these are data on user behavior. And finally, we humans are not the only ones generating data. Think of the IoT devices like cars and robots that

[43:52] produce sensory data. Like a Tesla car, for example, generates up to 25 GB of data per hour from hundreds of sensors, monitoring everything from engine

[44:04] performance to driver behavior. Or think of a small factory that uses smart sensors to track how efficient the production is or monitors the sensors of production is or monitors the sensors of a machine or equipment to predict when

[44:18] that machine would need maintenance before they break down. So all this human and device generated data became the food that these deep neural networks needed and still need as training material. So in our Netflix's

[44:32] recommendation example, a deep neural network today might analyze viewing patterns of tens of millions of users like what you watch, when you watch, in

[44:44] what intervals, like very specific pieces of data on the viewing pattern, as well as detailed metadata about thousands of shows themselves. the data on how users interact with the user interface like which thumbnails they

[44:58] click, what sections they expand to read and so on, where you pause or rewind. So the deep learning revolution and what makes deep neural networks feel almost

[45:11] magical compared to their predecessors was not primarily about new algorithms. Though there were also important innovations, but it was about finally having the computational resources and the data volume to make existing neural

[45:27] network concepts work at scale. Now while deep learning with neural networks completely transformed areas like image recognition and recommendation systems

[45:39] it still had limitations in understanding language and the context of the language in the way that humans do. So these neural networks were not enough to give us chedg which was the mass hype or the public breakthrough of

[45:55] mass hype or the public breakthrough of AI. So what was the missing link or the breakthrough that resulted in Chad Gubet and was quickly followed by an array of other tools like claude perplexity and so on? Well, that's going to be exactly

[46:09] the topic of the next video. So make sure to activate the notification bell not to miss it. And with that, as always, thanks for watching. Hope you always, thanks for watching. Hope you learned a lot. I'll see you in the next