The Power and Versatility of Neural Networks: Unlocking Value Creation through Machine Learning

Neural networks have revolutionized the field of machine learning, enabling organizations to create value through various applications. One of the key factors behind their success is the clever selection of what should be X and what should be Y for a particular problem. This involves fitting the supervised learning component into a bigger system, such as an autonomous vehicle, where multiple types of neural networks can be used depending on the specific application.

In real-world applications, 20 different types of neural networks are useful for different tasks. For example, in the real estate application we saw in the previous video, a standard neural network architecture is often used. On the other hand, online advertising might require a relatively standard new network like the one that was shown for image applications. Image recognition problems often use convolutional neural networks (CNNs), which are well-suited for processing images. In contrast, sequence data such as audio or language can be represented as one-dimensional temporal sequences, making recurrent neural networks (RNNs) a good fit.

The choice of network architecture depends on the type of data and the specific problem at hand. For instance, in the case of autonomous driving, which involves complex interactions between multiple sensors, a more custom or hybrid neural network architecture might be necessary. On the other hand, in applications like real estate or online advertising, a standard CNN architecture can often suffice. Understanding the different types of neural networks and their strengths is essential for selecting the right architecture for a specific application.

Standard Neural Network Architectures

One common neural network architecture is the convolutional neural network (CNN), which is well-suited for image data. This type of network consists of multiple layers, each with its own set of weights and biases. The first layer applies a series of filters to the input image, scanning it in horizontal and vertical directions. Each filter computes the dot product between the input feature map and a set of learnable weights, resulting in an output feature map.

The next few layers typically consist of a series of convolutional, pooling, and flattening operations. The convolutional operation slides a window over the entire input image, performing a series of dot products with the learned weights. This process is repeated multiple times, allowing the network to learn complex features from the input data. Pooling operations downsample the feature maps by reducing their spatial dimensions, which helps reduce the number of parameters in the network.

Recurrent Neural Networks

Another common neural network architecture is the recurrent neural network (RNN), which is well-suited for sequence data such as audio or language. RNNs are designed to handle sequential input data, where each element is dependent on previous elements. This type of network consists of multiple layers, each with its own set of weights and biases.

The most common type of RNN is the long short-term memory (LSTM) network, which uses a combination of memory cells and gates to process sequential input data. The LSTM architecture allows the network to learn long-term dependencies in the input sequence, making it well-suited for applications like speech recognition or machine translation.

Structured vs. Unstructured Data

Data can be broadly categorized into two types: structured and unstructured. Structured data refers to organized data that has a predefined format, such as databases with well-defined columns and rows. Unstructured data, on the other hand, is raw and unorganized, such as audio or images.

Historically, computers have struggled to make sense of unstructured data compared to structured data. However, thanks to the rise of neural networks, computers are now much better at interpreting unstructured data. This has opened up exciting opportunities for new applications in areas like speech recognition, image recognition, and natural language processing.

The Rise of Neural Networks

Neural networks have transformed supervised learning by enabling organizations to create value through various applications. However, the basic technical ideas behind neural networks have been around for many decades. So, why are they only now becoming this incredibly powerful tool?

The answer lies in advances in computing power, data availability, and algorithmic breakthroughs. The development of deep learning techniques has enabled neural networks to learn complex patterns in large datasets, leading to significant improvements in performance and accuracy. Additionally, the increasing availability of labeled datasets has made it easier for organizations to train and deploy neural networks.

The Power of Neural Networks

Neural networks have the potential to create tremendous economic value by solving complex problems in areas like computer vision, natural language processing, and speech recognition. By leveraging their ability to learn complex patterns in data, organizations can gain a competitive edge and improve decision-making. In this course, we will explore the techniques and algorithms behind neural networks, providing you with the skills and knowledge necessary to unlock their full potential.

"WEBVTTKind: captionsLanguage: enthere's been a lot of hype about new networks and perhaps some of that hype is justified given how well they're working but it turns out that so far almost all the economic value created by new networks has been through one type of machine learning called supervised learning let's see what that means and let's go to some examples in supervised learning you have some input X and you want to learn a function mapping it to some output Y so for example just now we saw the holding price prediction application where you input some features of a home and try to output or estimate the price Y here are some other examples that neural networks have been applied to very effectively possibly the single most lucrative application of deep learning today is online advertising of maybe not the most inspiring but certainly very lucrative in which by inputting and add information about an ad to the website is thinking of showing you and some information about the user new networks have gotten very good at predicting whether or not you click on an ad and by showing you and showing users the ads that you're most likely to click on this has been an incredibly lucrative application of neural networks and multiple companies because the ability to show you as that you're more likely to click on as a direct impact on the bottom line of some of the very large online advertising companies computer vision has also made huge strides in the last several years mostly due to deep learning so you might implement image and once you output an index say from one to a thousand trying to tell you if on this picture it might be anyone of say a thousand different images so you might use that for photo tagging I think the recent progress in speech recognition has also been very exciting where you can now input an audio clip to a neural network and have it output a text transcript machine translation has also made huge strides thanks to deep learning where now you can have a neural network input an English sentence and directly output so your Chinese sentence and in autonomous driving you might input an image say a picture of what's in front of your car as well as some information from a radar and based on that maybe a neural network can be trained to tell you the position of the other cars on the road so this becomes a key component in autonomous driving systems so a lot of the value creation through neural networks has been through your cleverly selecting what should be X and what should be Y for your particular problem and is fitting the supervised learning component into often a bigger system such as an autonomous vehicle it turns out that 20 different types of neural networks are useful for different applications for example in the real estate application that we saw in the previous video we use a gyro so be standard neural network architecture right maybe for real estate and online advertising might be a relatively standard new network like the one that we saw for image applications will often use convolutional neural networks often abbreviated CNN and for sequence data so for example audio as a temporal component right audio is played out over time so audio is most naturally represented as a one dimensional time series there's a one dimensional temporal sequence and so for sequence data you often use an RNN or recurrent neural network language you know English and Chinese the alphabets or the words come one at a time so language is also most naturally represented as sequence data and so in more complex versions of RNAs are often used for these applications and then for more complex applications like autonomous driving where you have an image then I suggest more of a CN n convolutional neural net structure and radar info which is you know something something quite different you might end up with more custom or some more complex hybrid neural network architecture so just a bit bit more concrete about what are the standard CNN and RNN architectures so in the literature you might have seen pictures like this so that's a standard neural net you might have seen pictures like this but this is an example of a convolutional neural network and we'll see in a later course exactly what this picture means and how you can implement is but convolutional networks are often used for image data and you might also have seen pictures like this and you learned how to implement this in a later course recurrent neural networks are you're very good for this type of one-dimensional right sequence data there is a may be a temporal component you might also have heard about applications of machine learning to both structured data and unstructured data here's what determines mean structured data means basically you know databases of data so for example in housing price prediction you might have a database with a column that tells you the size in the number of bedrooms so this is structured data or you know in predicting whether or not a user will click on an ad you might have information about the user such as the age some individual the ad and then labels Y that you're trying to predict so that's structured data meaning that each of the features such as size of the house or number bedrooms or the age of a user has a very very well-defined meaning in contrast unstructured data refers to things like audio raw audio or images where you might want to recognize what's in the image or text here the features might be the pixel values in an image or the individual words any piece of text historically has been much harder for computers to make sense of unstructured data compared to structured data and in fact the human race has evolved to be very good at understanding audio tubes as well as images and then text was a more recent invention but people are just really good at interpreting unstructured data and so one of the most exciting things about the rise of neural networks is that thanks to deep learning thanks for neural networks computers are now much better and interpreting unstructured data as well compared to just a few years ago and this creates opportunities for many new exciting applications that use speech recognition image recognition natural language processing on text much more than was possible even just two or three years ago I think because people have a natural empathy to understanding unstructured data you might hear about neural network successes on unstructured data more in the media because it's just cool when the neural network recognizes a cat we all like that that we all know what that means but it turns out that a lot of short-term economic value that new networks are creating has also been on structured data such as much better advertising systems much better product recommendations and just a much better ability to process the giant databases that many companies have to make accurate predictions from them so in this course a lot of the techniques will go over will apply to both structured data and to unstructured data for the purposes of explaining the algorithms we will draw a little bit more on examples that use unstructured data but as you think through applications of your networks within your own team I hope you find both uses for them in both structured and unstructured data so neural networks have transformed supervised learning and are creating tremendous economic value it turns out though that the basic technical ideas behind your networks have most even around sometimes for many decades so why is it then that they're only just now taking off and working so well in the next video we'll talk about why is only quite recently that neural networks have become this incredibly powerful tool that you can usethere's been a lot of hype about new networks and perhaps some of that hype is justified given how well they're working but it turns out that so far almost all the economic value created by new networks has been through one type of machine learning called supervised learning let's see what that means and let's go to some examples in supervised learning you have some input X and you want to learn a function mapping it to some output Y so for example just now we saw the holding price prediction application where you input some features of a home and try to output or estimate the price Y here are some other examples that neural networks have been applied to very effectively possibly the single most lucrative application of deep learning today is online advertising of maybe not the most inspiring but certainly very lucrative in which by inputting and add information about an ad to the website is thinking of showing you and some information about the user new networks have gotten very good at predicting whether or not you click on an ad and by showing you and showing users the ads that you're most likely to click on this has been an incredibly lucrative application of neural networks and multiple companies because the ability to show you as that you're more likely to click on as a direct impact on the bottom line of some of the very large online advertising companies computer vision has also made huge strides in the last several years mostly due to deep learning so you might implement image and once you output an index say from one to a thousand trying to tell you if on this picture it might be anyone of say a thousand different images so you might use that for photo tagging I think the recent progress in speech recognition has also been very exciting where you can now input an audio clip to a neural network and have it output a text transcript machine translation has also made huge strides thanks to deep learning where now you can have a neural network input an English sentence and directly output so your Chinese sentence and in autonomous driving you might input an image say a picture of what's in front of your car as well as some information from a radar and based on that maybe a neural network can be trained to tell you the position of the other cars on the road so this becomes a key component in autonomous driving systems so a lot of the value creation through neural networks has been through your cleverly selecting what should be X and what should be Y for your particular problem and is fitting the supervised learning component into often a bigger system such as an autonomous vehicle it turns out that 20 different types of neural networks are useful for different applications for example in the real estate application that we saw in the previous video we use a gyro so be standard neural network architecture right maybe for real estate and online advertising might be a relatively standard new network like the one that we saw for image applications will often use convolutional neural networks often abbreviated CNN and for sequence data so for example audio as a temporal component right audio is played out over time so audio is most naturally represented as a one dimensional time series there's a one dimensional temporal sequence and so for sequence data you often use an RNN or recurrent neural network language you know English and Chinese the alphabets or the words come one at a time so language is also most naturally represented as sequence data and so in more complex versions of RNAs are often used for these applications and then for more complex applications like autonomous driving where you have an image then I suggest more of a CN n convolutional neural net structure and radar info which is you know something something quite different you might end up with more custom or some more complex hybrid neural network architecture so just a bit bit more concrete about what are the standard CNN and RNN architectures so in the literature you might have seen pictures like this so that's a standard neural net you might have seen pictures like this but this is an example of a convolutional neural network and we'll see in a later course exactly what this picture means and how you can implement is but convolutional networks are often used for image data and you might also have seen pictures like this and you learned how to implement this in a later course recurrent neural networks are you're very good for this type of one-dimensional right sequence data there is a may be a temporal component you might also have heard about applications of machine learning to both structured data and unstructured data here's what determines mean structured data means basically you know databases of data so for example in housing price prediction you might have a database with a column that tells you the size in the number of bedrooms so this is structured data or you know in predicting whether or not a user will click on an ad you might have information about the user such as the age some individual the ad and then labels Y that you're trying to predict so that's structured data meaning that each of the features such as size of the house or number bedrooms or the age of a user has a very very well-defined meaning in contrast unstructured data refers to things like audio raw audio or images where you might want to recognize what's in the image or text here the features might be the pixel values in an image or the individual words any piece of text historically has been much harder for computers to make sense of unstructured data compared to structured data and in fact the human race has evolved to be very good at understanding audio tubes as well as images and then text was a more recent invention but people are just really good at interpreting unstructured data and so one of the most exciting things about the rise of neural networks is that thanks to deep learning thanks for neural networks computers are now much better and interpreting unstructured data as well compared to just a few years ago and this creates opportunities for many new exciting applications that use speech recognition image recognition natural language processing on text much more than was possible even just two or three years ago I think because people have a natural empathy to understanding unstructured data you might hear about neural network successes on unstructured data more in the media because it's just cool when the neural network recognizes a cat we all like that that we all know what that means but it turns out that a lot of short-term economic value that new networks are creating has also been on structured data such as much better advertising systems much better product recommendations and just a much better ability to process the giant databases that many companies have to make accurate predictions from them so in this course a lot of the techniques will go over will apply to both structured data and to unstructured data for the purposes of explaining the algorithms we will draw a little bit more on examples that use unstructured data but as you think through applications of your networks within your own team I hope you find both uses for them in both structured and unstructured data so neural networks have transformed supervised learning and are creating tremendous economic value it turns out though that the basic technical ideas behind your networks have most even around sometimes for many decades so why is it then that they're only just now taking off and working so well in the next video we'll talk about why is only quite recently that neural networks have become this incredibly powerful tool that you can use\n"