And our best guess was a two. You have a very simple concept, very simple model. Ah, this little spike here that's doing the wrong thing, and it can't seem to quite think its way out of that one gave a few more in Iran's, though it might able to figure it out. But I did run it earlier, and you can see the results here. And we're going to try to see if we can predict if a politician is Republican or Democrat, just based on how they voted on 17 different issues. You know, with tensorflow, you have to think about every little detail at a linear algebra level of how these neural networks are constructed because it doesn't really natively support neural networks out of the box. People get that an example shortly. Let's go ahead and run that previous block there before we forget and we will run. I started off by just toe blindly reading into CSP file using pd dot reid CSP and taking a look at it. Here I’m going to import only one library, ie. So on Windows you would do that by going to the Anaconda prompt. Keras is a powerful and easy-to-use free open source Python library for developing and evaluating deep learning models.. So even if they don't solve the specific problem you're trying to solve, you can still use thes pre trained models as a starting point to build off of that is, you know a lot easier to get going with. But there's only so much we have time to do verbosity level two because that's what do you want to choose for running within an eye python notebook and we will pass in our A validation test data for it to work with us? They were indicated by question Mark, so have to read that in a little bit more intelligently. We are going to use the MNIST data-set. In particular, there's also one called Alex Net, which is appropriate for image classification. Good. You can add to the cost function during training. It would be better to be insistent with your superiors that this system is only marketed as an supplementary tool to aid doctors in making a decision and not as a replacement for human beings, making a decision that could affect life or death again. Getting Started With Deep Learning, Deep Learning with Python : Beginners Guide to Deep Learning, What Is A Neural Network? Automatic language translation and medical diagnoses are examples of deep learning. Obviously, that's a one D and three d very into that as well. In this case, we're gonna be drawing something more like a hyperbolic tangent because mathematically, you want to make sure that we preserve some of the information coming in and more of a smooth manner. And as it goes as it generates through it, you will start to reinforce the connections that lead to the correct classifications through Grady into center. What is a tensor anyway? But I mean still, I mean, this is a pretty complicated classifications problem. We decide one line to actually load up the resident 50 model and transfer that learning to our application, if you will, specifying a given set of weights that was pre learned from a given set of images. You might follow that up with a max pulling two D layer on top of that that distills that image down just shrinks the amount of data that you have to deal with. And that's quick as well. So there's a possibility that we're over fitting here to really evaluate how well this model does. So let's go ahead and load up the model and thats done. It's either on or off. So we have to talk about one hot encoding at this point. We're not going to reshape that data into flat one D arrays of 768 pixels. And when you're done with Jupiter entirely for this session, just quit. But let's see if it works. OK, so people are very eager to apply deep learning to different situations in the real world. Using Tensorflow for Handwriting Recognition, Part 2, 11. Teoh, define that optimizer. A GPU is just your video card, the same video card that you're using to play fortnight on or whatever it is you play. Okay, so a little bit different there for the biases By default. I'm not gonna preach to you about cinci in robots taking over the world. Deep Learning Project Intro: So it's time to apply what you've learned so far in this deep learning course. To actually load up these CSB files or just comma separated value data files and massage that data, clean it up a little bit and get it into a form that caress can accept. How many layers do you have? It's just processing and massaging your input data and making sure that your input data looks good to go. We can reinforce those weights over time and reward the connections that produced the behavior that we want. So it's a way of just making the grating to send happened faster by kind of skipping over those steeper parts of your learning curve. Using Keras to Learn Political Affiliations, 13. So it's just, you know, getting you the the actual. This is your, ah, in our case, the images themselves. Frank Kane, Founder of Sundog Education, ex-Amazon, Building neural networks for handwriting recognition, Learning how to predict a politician's political party based on their votes, Performing sentiment analysis on real movie reviews, Interactively constructing deep neural networks and experimenting with different topologies. So you can even use variations of resident 50 that were trained on different sets of images . So we've built up to convolution layers here. That's when you say that something is something that it isn't. So let's try 1500. Obviously could start from different locations, try to prevent that sort of thing. But there are some key differences that we should talk about. So again, remember that if you actually have a job in deep learning and machine learning, you can go anywhere you want to. This Deep Learning with Python article will help you understand what exactly is Deep Learning and How this transition has been made possible. Using RNN's for Sentiment Analysis: what we're gonna do here is try to do sentiment analysis. So we're going to use an L S T m cell to try to counter act that effect that you see in normal RN ends where data becomes diluted over time or as the sequence progresses in this example. There potentially. Now you can see there are some areas of improvement here for this idea. This one's a little bit more diagonal to the bottom, right? It's a deeper neural network than Lynette, you know. So this will represent whether that image represents the numbers zero through nine. That says F, represents the addition of whatever's in A and B together. All you have to do is use the conduct commanding your anaconda environment to install tensorflow. Basically, we started some random set of parameters, measured the error, move those parameters in a given direction, see if that results in more error or less error and just try to move in the direction of minimizing error until we find the actual bottom of the curve there, where we have a set of parameters that minimizes the air of whatever it is you're trying to do. And this is older data is from 1984. You know, maybe there is more of a tendency for Republicans to not vote than Democrats or vice versa . Steps end up getting diluted over time because we just keep feeding in behavior from the previous step in our run to the current step. As you know, our brain is made up of billions of neurons that allows us to do amazing things. But obviously for just adding one plus two, there's no need for all that. Teoh make things sound more complicated than they really are. The teacher's recommendation is shown until at least 5 student responses are collected. Where instead of really thinking about neurons or units, you're thinking more about 10. That's what we call a type one error, which is a false positive. Then we set up a be variable that's assigned to the value, too, and given the name be, here is where the magic starts to happen. And since it does have to do some thinking, it doesn't come back instantly but pretty quick. And this one's even mawr bottom right heavy. Next, we have the patient's age. This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. Okay, now we can build up the model itself. So think twice before you publish stuff like that, think twice before you implement stuff like that for an employer because your employer only cares about making money about making a profit. We'll get there. Like I said later in the course, we'll talk about some better approaches that we can use. That's all the tensor is using. If you do want to play with them, you'll have to return for to the documentation here. And that's done now. Hopefully, you could play around here. So cool, let's move on to another type of neural network next. So that's a good way to wrap your head around what we're dealing with. So on my second try here, I called read, see SV passing and explicitly the knowledge that question marks mean missing values or any values and passing an array of column names like we did before and did another head on the resulting Panis data frame. Please mention it in the comments section of “Deep Learning with Python” and we will get back to you. Net Inception, Mobile Net in Oxford, v. G. Or some examples. The activities in this course air really interesting. And my really short the details of that particular bill were. Let's try out, um, or interesting example. Carrots actually comes in its documentation with an example of using amnesty, and this is the actual topology that they use in their examples. We need to scale that down to the range of 0 to 1. And he could actually distribute that across an entire cluster if it had to. I've sort of embellished on it a little bit here, but the idea is there says to give credit where credit's due and it does warm my heart by the way that they include the IMDb data set as part of caress free to experiment with. The History of Artificial Neural Networks, 8. Come to mind for that, you don't necessarily know where the noun or the verb or a phrase that you care about might be in some paragraph percent and say you're analyzing, but a CNN confined it and pick it out for you. It learns cross Val score to evaluate its performance. And we will import the RMS problem optimizer, which is what we're going to use for our Grady and dissent. So we're going to import the caress library and some specific modules from it. We have the amnesty data set here that we're going to experiment with the sequential model , which is a very quick way of assembling the layers of a neural network. We've talked about how this all works at a low level and intensive flow to It's still possible to implement a complete neural network, basically from scratch, but intense airflow to they have replaced much of that low level functionality with a higher level AP I called caress. We need an accuracy metric as well, So a loss function isn't enough. So there's a picture. I know you're probably itching to dive into some code by now, but there's a little more theory we need to cover with deep learning. So you definitely need to be of a certain age, shall we say, to remember what these issues were. We thought it was 1/6. The activation function we talked about not using a step function and using something else , some other ones that are popular Rally was actually very popular right now of realization function we haven't talked about yet. Make sure you pay attention to the dashes and the capitalization. OK, so we're going to go to a initial input layer of six neurons to a hidden layer of four neurons and then a layer of two neurons which will ultimately produce a binary output at the end. I mean, that's kind of amazing that one, and I probably would've guessed a three on that one, but again. So someone uses some really obscure word. I would not get that. So you know where our algorithm is kind of at a disadvantage compared to those human doctors to begin with. You know it might be completely useless, even after you've running for hours to see if it actually works. It's pretty much straight up. So in this case, we have 16 different issues that people voted on. Introduction To Artificial Neural Networks, Deep Learning Tutorial : Artificial Intelligence Using Deep Learning. Deep learning is a subset of ML which make the computation of multi-layer neural network feasible. So, you know, it's not quite the exact science that you might think it ISS. Here. Now remember, we're going to go up to 3000 and again, you know, you just gonna have to watch this and see where it starts to converge. And you can see that we have are two convolution layers here, followed by a pooling layer, followed by a drop out of flatten. We talked about this in the ethics lecture, actually of masses detected and mammograms, and just based on the measurements of those in massive see if you can predict whether they're benign or malignant. So let's try this out now. They are very heavy and your CPU, your GP you and your memory requirements shuffling all that data around involving it adds up really, really fast. Let's start by creating some vectors were input here. Our features are 784 in number, and we get that by saying that each image is a 28 by 28 image, right, so we have 28 times 28 which is 784 individual pixels for every training image that we have . Turns out that sometimes you don't need a whole lot to actually get the optimal result from the data that you have. So again, you might have the example of a neural network that's trying to drive your car for you, and it needs to identify pictures of stop signs or yield signs or traffic lights. We want to use batch sizes of 100. You know, if you find yourself being asked to be doing something that's morally questionable, you can say no, someone else will hire you tomorrow. You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in Python, right?. All right, one more. So when we talk about what this command does, first of all, nothing unusual here just says that we're going to run batches of 32 which is smaller than before, because there is a much higher computational cost of. Okay, so there are ways to combat that. And if people are using your system more than 1000 times, there's going to be some bad consequence that happens. And if we do that enough times, it should converge to a neural network that is capable of reliably classifying these things . So if you haven't already taken care of that, you can just say Kanda install tensorflow. Not too bad, you know. Remember, your model is only as good as the data that you train it with. One is called the buyer as assessment, and that's basically a measurement of how confident are diagnosis was of this particular mass. We use the weights that we're currently using in our neural network to back propagate that our error to individual connections. What is the area that you actually involve across? I just, you know, didn't get into because that lecture was long enough. So technically, we call this feature location. Step here. Open up the transfer learning notebook in your course materials, and you should see this, and you will soon see just how crazy easy it is to use and how crazy good it can be. It was true. You can see that this neuron is receiving not only a new input but also the output from the previous time step and those get some together, the activation function gets applied to it, and that gets output as well. So just because the artificial neural network you've built is not human does not mean that it's inherently fair and unbiased. Ah, higher level might take those edges and recognize the shape of that stop Science says. So go to Anaconda in your start menu and open up Anaconda prompt on Mac OS or Lennox. We then say TF dot add to add in the biased terms, which again are stored in the B variable that we just defined above. 4. The idea behind AI is fairly simple yet fascinating, which is to make intelligent machines that can take decisions on its own. So we have a data set of mammogram masses that were detected in real people, and we've had riel. That's just one example. Picture of a cast lives Did Goto whales. Right? All right, so with that out of the way, let's move on. But you know, you can see there's a wide variety of handwriting capabilities of people who made this test data. How cool is that? Artificial Intelligence – What It Is And How Is It Useful? 4. Your final project is to take some real world data. I assume it's scaling it down into whatever range it wants and maybe doing some pre processing of the image itself to make it work better. 2. So the way your eyes work is that individual groups of neurons service a specific part of your field of vision. Nothing's really happening until we actually kick off the model, so that doesn't take any time at all. Let's take the example of a self driving car. Support this Website! So by saying Max Line equals 80 that means we're only going to look at the 1st 80 words in each review and limit our analysis to that. That's kind of the It's gaining popularity now that computing resource is air becoming less and less of a concern now that you can actually do deep learning over a cluster of PCs on network in the cloud. But even after just 10 at box or 10 iterations, we ended up with a accuracy of over 99%. For years, it was thought that computers would never match the power of the human brain. Keras is a neural network API written in Python and integrated with TensorFlow. Basically, it's a data set of 70,000 handwriting samples where each sample represents someone trying to draw the numbers zero through nine. So, yeah, again, An impressive job here on a random photo from vacation. And they come pre trained with a very wide variety of object types. All right, so these are some pretty messy examples in this example. Google again is your friend in this stuff, because this stuff is always changing. You even saw in some of the examples that we ran in the Tensorflow playground, that sometimes we don't with neurons that were barely used it all, and by using drop out that would have forced that neuron to be to have been used more effectively. Instead, we're going to shape it into the with times the length times the number of color channels. I mean, this whole field of artificial intelligence is based on an understanding of how our own brains work. You know where self driving cars are being oversold and there are a lot of edge cases in the world still where self driving cars just can't cut it where human could, And I think that's very dangerous. I mean, obviously, making an actual feature for this model that includes age or sex or race or religion would be a pretty bad idea, right? Now, here we have a diagram of the neural network itself, and we can play around with this. I mean, Well, this was actually room service, but you could definitely imagine that's in a restaurant instead. The one said It's getting wrong are pretty wonky. Let's dive into more details on how it actually works up next. So this is just It's not even a multi layer perceptron. You just have a bunch of neurons with a bunch of connections that individually behave very simply. It should come when you're done experimenting and playing around with this notebook. That's a very important good thing, because for a long time people believe that AI would be limited by this local minimum effect. And we don't really need to go into the hardcore mathematics of how auto def works. if you think Perceptron solves the problem, then you are wrong. He uses something called Eager Execution Toe. I think it's a pretty good bet. You do need to know what image dimensions that it expects the input in, for example, or someone working all. So we start by creating these numb pyre rays of the underlying training and test data and converting that to end peed off low 32 data types. They've just replaced all of the words with unique numbers that represent each word. And by reshaping that 768 pixel array into a two D shape, we can see that this is somebody's attempt at drawing the number three. You have a bunch of neurons, thes air individual nerve cells, and they are connected to each other via Exxon's and dendrites. We can manipulate it. We could use any of the one that we wanted to. It's complicated, you know. I need to scale my data down. Or you can use Anaconda Navigator to do it all through a graphical user interface. For example, that's a pretty funny looking to. We're going to have more than one, and we actually have now a hidden layer in the middle there, so you can see that are inputs air going into a layer at the bottom. Try different sample numbers is to get a better feel of what the state is like. Um, I took a picture of my breakfast once at a fancy hotel in London. But there are other models included with caress, including Inception and Mobile Net that you might want to try out. Like I said, Caris has a handy dandy IMDb data set preinstalled. Let's take a peek at what this data looks like. But if you were able to substantially improve upon that result, congratulations. Let's build up the model itself. Very simple, and I've then gone ahead and used the caress classifier to build a psychic learn compatible version of this neural network, and I've passed that into cross Val score toe actually do K fold cross validation in this case with 10 folds and print out the results. You can also choose different optimization functions. So there are ways of accelerating this. So again, it's important to work upon previous research. We've made a neural network that can essentially read English language reviews and determine some sort of meaning behind them. You're just creating a binary representation of a integer value, if you will. And if you can get more than 93% accuracy, we'd love to hear about it in the Q and A. For example, we might start with a sequence of information from, ah, sentence of some language, embody what that sentence means as some sort of a vector representation and then turn that around into a new sequence of words in some other language. I took absolutely no, uh, put no thought into making sure this was a picture that would work well with machine learning. So I've given you some data and a template to work from. But later on in the course, I'll show you an example of actually using standard scaler. Convolutional Neural Networks - Deep Learning basics with Python, TensorFlow and Keras p.3 Convolutional Neural Networks - Deep Learning with Python, TensorFlow and Keras p.3 Welcome to a tutorial where we'll be discussing Convolutional Neural Networks (Convnets and CNNs), using one to classify dogs and cats with the dataset we built in the previous tutorial. And then this other one is picking out stuff on the top and bottom. It still works. So So please consider these concerns as you delve into your deep learning career. Start to struggle a little bit. If you're running this on Windows, I wouldn't go there quite yet. Just watch it in action. And then we're going to drop out 20% of the neurons that the next layer to force the learning to be spread out more and prevent over fitting. So for every training step, we're going to take a batch from our training data. So nothing has actually happened here except for constructing that graph. You know, the blue areas are converging on some blue areas, and it's it's really trying hard, but it's just not enough neurons to pull this one off. Run. You can start with them all that's already figured all that out for you and just add on top of it. And let's just walk through is going on here. That means that we're only going to train our neural network using that 60,000 set of training samples and were holding a side of 10,000 test samples so we can actually test how well are trained network works on data that it's never seen before. So our mess prop it's just a more sophisticated way of trying to figure out the right direction. All right, so go play with that mom, come back. I’ll be covering the following topics in this article: Well, Data Science is something that has been there for ages. First of all, you need to make sure your source data is of the appropriate dimensions of the appropriate shape if you will, and you are going to be preserving the actual two D structure of an image. But one of the first things I built in my career was actually a military flight simulator and training simulator. Identify the business problem which can be solved using Neural network Models. Basically, it converts each of the final weights that come out of your neural network into a probability. We're going to extract all of the feature columns into the features array and all of the actual labels the actual parties into in all classes array. That's it. On Lee, 387 of them actually had a vote on the water project cost sharing bill, for example. Resident 50 was actually the model that worked the best for my photos. The way that it actually works is that you might push the actual trained neural network down to the car itself and actually execute that neural network on the computer that's running embedded within your car because the heavy lifting of deep learning is training that network. There's no useful derivative there at all. But it is what it is. I mean, what does this shape really mean? There's also a new book that just came out new. It's a very popular framework developed by the folks at Google, and they have been kind enough to make it open source and freely available to the world. Get an idea of what's happening here. That's ah, handy property for making training go quickly. So it's pretty cool stuff. Mind you, I mean, arguably, it's worse to leave a cancer untreated than to have a false positive or one. Air developed primarily for linnet systems running on a cluster. Apparently, that was supposed to be an eight. In this case, the reshape command is what does that So by saying reshape negative one numb features some features. So, for example, this hidden layer here this neuron is saying I want to wait things a little bit more heavily in this corner, okay? So this isn't isn't actually relate to deep learning. So we just say, Add in L S T M. And we can go through the properties here once they wanna have 128 recurrent neurons in that Ellis TM layer. All right, so we start off, like encoding, that known label to a one hot, encoded array. Remember back to how greeting to sent works. It's just one of many that I offer in the fields of AI and Big Data, and I hope you want to continue your learning journey with me. Here we have an input dimension of however many features you have coming into the system. See if you can improve upon things. Turns out, that's the number nine, um, kind of a weird looking nine, so that might be a little bit of a challenge. So one optimization that we'll talk about later is using the constant mo mo mentum. Those go to this layer here. Let's go ahead and shift. We talked about that in the slides as well. This is basically a an array of giant radio astronomy dishes with only 1000 classifications . You may have misclassified. And then the output of those neurons can then get fed back to the next step to every neuron in that layer. It's a lot less code and a lot less things that could go wrong. It's never been easier to use artificial intelligence in a real world application now. I mean, if it's a black and white image, there's only one color black and white, so you don't have one color channel for a grayscale image. I've added in another layer with one that does my final sigmoid classifications, my binary classification on top of that, and I have compiled that with the Adam Optimizer and the binary Cross entropy lost function . Interesting example. And so try different values. Well, that's where Soft Max comes in. Here, I will train our perceptron in 100 epochs. These ones are also misclassified. Right now. But when you put them together in these layers and you have multiple layers all wired together, you can get very complex behavior because there's a lot of different possibilities for all the weights between all those different connections. Okay, So as we keep running this thing over and over again will have some new data coming in that gets blended together with the output from the previous run through this neuron, and that just keeps happening over and over and over again. Did the person like this movie or not, will use the Adam Optimizer this time just because that's sort of the best of both worlds for Optimizers, and then we can kick it off. Let's dive in. So the first thing we need to do is load of the training data that contains the features that we want to train on and the target labels To train a neural network, you need to have a set of known inputs with a set of known correct answers that you can use to actually descend er converge upon the correct solution of weights that lead to the behavior that you want. So, you know, we didn't really spend any time tuning the topology of this network. A lot of this complexity from you And things that are like in the lower left hand corner, not so much. The second type is a false negative, and, for example, you might have breast cancer but failed to detect it. Using CNN's for Handwriting Recognition: and we're going to revisit the M NIST handwriting recognition problem where we try to classify a bunch of images of people are drawing the number is zero through nine and see if we could do a better job of it. We can then set up an estimator using the caress classifier function there, and that allows us to get back an estimator that's compatible with psych. You can see that within your cortex, neurons seem to be arranged into stacks or cortical columns that process information in parallel. How do I add in the bias terms? So if you're trying to classify something in your neural network like for example, decide if a an image is a picture of a face or a picture of a dog or a picture of a stop sign. update values of weights and bias in the successive iteration to minimize the error or loss. So we'll pick a random training set sample toe print out here on display. Same exact idea. But that's much Wow. Well, I never used the word learning curve in the context. So if this is time Step one with the same neuron. And again, convolution is just breaking up that image into little sub fields that overlap each other for individual processing. So we call these local receptive fields there just groups of neurons that respond only to a part of what you're. A neural networks, because in a neural network you tend to have an artificial neuron that have very many inputs, but probably only one output or very few outputs and comparison to the inputs. So traditional resource is for learning. We also want to display the actual accuracy at each stage two and all this accuracy metric does is say, let's compare of the actual maximum argument from each output array that's gonna correspond to our one hot encoded value. But you can take things up a notch. That's why the letters on this slide catch your attention because there's high contrast between the letters and the white background behind them. We have the variables defined for our weights and biases. So if you care about speed of convergence, adding more layers is often the right thing to do. That's what happened. So I'm gonna help you load up this data and clean it up. All of these features required applying machine learning techniques to real world data sets , and that's what this course is all about. You know, wires, if you will, that connect different accents together. Okay, and that's how creative dissent works. But I can see some people doing that. Maybe this would be a perceptron that tries to classify an image into one of three things or something like that. In this course, you'll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python. So basically, you want to see. That's it. We over 3000 that box we ended up with an accuracy of 92.8% and this is actually remember using our training data set. None of that probably means anything to you right now. So we have 70,000 images that are 28 by 28 images of people drawing the number zero through nine. And that means that we can add individual layers to our neural network one layer at a time , sequentially, if you will.