deep learning with python tutorial

Keras Tutorial for Beginners: This learning guide provides a list of topics like what is Keras, its installation, layers, deep learning with Keras in python, and applications. Now, in the next blog of this Deep Learning Tutorial series, we will learn how to implement a perceptron using TensorFlow, which is a Python based library for Deep Learning. After, you can train the model for 20 epochs or iterations over all the samples in X_train and y_train, in batches of 1 sample. This tutorial was just a start in your deep learning journey with Python and Keras. Extreme volatile acidity signifies a seriously flawed wine. There is only one way to find out: preprocess the data and model it in such a way so that you can see what happens! Now that you have already inspected your data to see if the import was successful and correct, it’s time to dig a little bit deeper. Recall is a measure of a classifier’s completeness. You have made a pretty accurate model despite the fact that you have considerably more rows that are of the white wine type. \(f(x) = 1\) if \(x>0\). You follow the import convention and import the package under its alias, pd. You’ve successfully built your first model, but you can go even further with this one. Before you start re-arranging the data and putting it together in a different way, it’s always a good idea to try out different evaluation metrics. One variable that you could find interesting at first sight is alcohol. That’s right. By now, you might already know machine learning, a branch in computer science that studies the design of algorithms that can learn. Depending on whichever algorithm you choose, you’ll need to tune certain parameters, such as learning rate or momentum. The number of layers is usually limited to two or three, but theoretically, there is no limit! Multi-layer perceptrons are often fully connected. You can circle back for more theory later. Also, don’t miss our Keras cheat sheet, which shows you the six steps that you need to go through to build neural networks in Python with code examples! Computer Vision. If you would allow more hidden units, your network will be able to learn more complex representations but it will also be a more expensive operations that can be prone to overfitting. Besides adding layers and playing around with the hidden units, you can also try to adjust (some of) the parameters of the optimization algorithm that you give to the compile() function. Note that you can double check this if you use the histogram() function from the numpy package to compute the histogram of the white and red data, just like this: If you’re interested in matplotlib tutorials, make sure to check out DataCamp’s Matplotlib tutorial for beginners and Viewing 3D Volumetric Data tutorial, which shows you how to make use of Matplotlib’s event handler API. The output of this layer will be arrays of shape (*,8). You will put wines.quality in a different variable y and you’ll put the wines data, with exception of the quality column in a variable x. The focus of this tutorial is on using the PyTorch API for common deep learning model development tasks; we will not be diving into the math and theory of deep learning. On the top right, click on New and select “Python 3”: Click on New and select Python 3. The best way to learn deep learning in python is by doing. As you sort of guessed by now, these are more complex networks than the perceptron, as they consist of multiple neurons that are organized in layers. Welcome to part two of Deep Learning with Neural Networks and TensorFlow, and part 44 of the Machine Learning tutorial series. But that doesn’t always need to be like this! Traffic Signs Recognition. If you instead feel like reading a book that explains the fundamentals of deep learning (with Keras) together with how it's used in practice, you should definitely read François Chollet's Deep Learning in Python book. You can do this by using the IPython shell of the DataCamp Light chunk which you see right above. Some of the most popular optimization algorithms used are the Stochastic Gradient Descent (SGD), ADAM and RMSprop. That was a piece of cake, wasn’t it? Consider taking DataCamp’s Deep Learning in Python course! The human brain is then an example of such a neural network, which is composed of a number of neurons. Today’s Keras tutorial for beginners will introduce you to the basics of Python deep learning: Would you like to take a course on Keras and deep learning in Python? As you briefly read in the previous section, neural networks found their inspiration and biology, where the term “neural network” can also be used for neurons. As stated in the description, you’ll only find physicochemical and sensory variables included in this data set. Audience. The units actually represents the kernel of the above formula or the weights matrix, composed of all weights given to all input nodes, created by the layer. With the data at hand, it’s easy for you to learn more about these wines! What’s more, I often hear that women especially don’t want to drink wine precisely because it causes headaches. An introductory tutorial to linear algebra for machine learning (ML) and deep learning with sample code implementations in Python The choice for a loss function depends on the task that you have at hand: for example, for a regression problem, you’ll usually use the Mean Squared Error (MSE). Since Keras is a deep learning's high-level library, so you are required to have hands-on Python language as well as … The final layer will also use a sigmoid activation function so that your output is actually a probability; This means that this will result in a score between 0 and 1, indicating how likely the sample is to have the target “1”, or how likely the wine is to be red. If you would be interested in elaborating this step in your own projects, consider DataCamp’s data exploration posts, such as Python Exploratory Data Analysis and Python Data Profiling tutorials, which will guide you through the basics of EDA. Just use predict() and pass the test set to it to predict the labels for the data. Knowing this is already one thing, but if you want to analyze this data, you will need to know just a little bit more. What would happen if you add another layer to your model? Deep Learning with Python, TensorFlow, and Keras tutorial Welcome everyone to an updated deep learning with Python and Tensorflow tutorial mini-series. In other words, you’re setting the amount of freedom that you’re allowing the network to have when it’s learning representations. You thus need to make sure that all two classes of wine are present in the training model. Additionally, use the sep argument to specify that the separator, in this case, is a semicolon and not a regular comma. You can visualize the distributions with any data visualization library, but in this case, the tutorial makes use of matplotlib to plot the distributions quickly: As you can see in the image below, you see that the alcohol levels between the red and white wine are mostly the same: they have around 9% of alcohol. These algorithms are usually called Artificial Neural Networks (ANN). Keras in a high-level API that is used to make deep learning networks easier with the help of backend engine. Precision is a measure of a classifier’s exactness. Take advantage of this course called Deep Learning with Python to improve your Programming skills and better understand Python.. You used 1 hidden layers. You can get more information here. With your model at hand, you can again compile it and fit the data to it. Now you’re completely set to begin exploring, manipulating and modeling your data! After the completion of this tutorial, you will find yourself at a moderate level of expertise from where you can take yourself to the next level. The scikit-learn package offers you a great and quick way of getting your data standardized: import the StandardScaler module from sklearn.preprocessing and you’re ready to scale your train and test data! It’s a type of regression that is used for predicting an ordinal variable: the quality value exists on an arbitrary scale where the relative ordering between the different quality values is significant. As for the activation function that you will use, it’s best to use one of the most common ones here for the purpose of getting familiar with Keras and neural networks, which is the relu activation function. Like you read above, the two key architectural decisions that you need to make involve the layers and the hidden nodes. In this case, you can use rsmprop, one of the most popular optimization algorithms, and mse as the loss function, which is very typical for regression problems such as yours. Your classification model performed perfectly for a first run! At the moment, there is no direct relation to the quality of the wine. Go to this page to check out the description or keep on reading to get to know your data a little bit better. Next, it’s best to think back about the structure of the multi-layer perceptron as you might have read about it in the beginning of this tutorial: you have an input layer, some hidden layers and an output layer. As you can imagine, “binary” means 0 or 1, yes or no. You can visually compare the predictions with the actual test labels (y_test), or you can use all types of metrics to determine the actual performance. To do this, you can make use of the Mean Squared Error (MSE) and the Mean Absolute Error (MAE). Note that you could also view this type of problem as a classification problem and consider the quality labels as fixed class labels. In this case, you’ll use evaluate() to do this. For that, I recommend starting with this excellent book. Dense layers implement the following operation: output = activation(dot(input, kernel) + bias). You do not need to understand everything (at least not right now). The higher the precision, the more accurate the classifier. Also, by doing this, you optimize the efficiency because you make sure that you don’t load too many input patterns into memory at the same time. Machine Learning. This maybe was a lot to digest, so it’s never too late for a small recap of what you have seen during your EDA that could be important for the further course of this tutorial: Up until now, you have looked at the white wine and red wine data separately. One of the first things that you’ll probably want to do is to start with getting a quick view on both of your DataFrames: Now is the time to check whether your import was successful: double check whether the data contains all the variables that the data description file of the UCI Machine Learning Repository promised you. The intermediate layer also uses the relu activation function. For regression problems, it’s prevalent to take the Mean Absolute Error (MAE) as a metric. Instead of relu, try using the tanh activation function and see what the result is! You might also want to check out your data with more than just info(): A brief recap of all these pandas functions: you see that head(), tail() and sample() are fantastic because they provide you with a quick way of inspecting your data without any hassle. In this step-by-step Keras tutorial, you’ll learn how to build a convolutional neural network in Python! An example of a sigmoid function that you might already know is the logistic function. Besides the number of variables, also check the quality of the import: are the data types correct? In other words, you have to train the model for a specified number of epochs or exposures to the training dataset. Keras is easy to use and understand with python support so its feel more natural than ever. Now that you have explored your data, it’s time to act upon the insights that you have gained! In this tutorial, we are going to be covering some basics on what TensorFlow is, and how to begin using it. This is just a quick data exploration. For this tutorial, you’ll use the wine quality data set that you can find in the wine quality data set from the UCI Machine Learning Repository. Tip: also check out whether the wine data contains null values. You can always change this by passing a list to the redcolors or whitecolors variables. The number of hidden units is 64. We mostly use deep learning with unstructured data. Lastly, with multi-class classification, you’ll make use of categorical_crossentropy. Up until now, you have always passed a string, such as rmsprop, to the optimizer argument. At first sight, these are quite horrible numbers, right? Also, try out experimenting with other optimization algorithms, like the Stochastic Gradient Descent (SGD). Since neural networks can only work with numerical data, you have already encoded red as 1 and white as 0. If you’re a true wine connoisseur, you probably know all of this and more! Using this function results in a much smoother result! Off to work, get started in the DataCamp Light chunk below! And, as you all know, the brain is capable of performing quite complex computations, and this is where the inspiration for Artificial Neural Networks comes from. Additionally, you can also monitor the accuracy during the training by passing ['accuracy'] to the metrics argument. The first step is to define the functions and classes we intend to use in this tutorial. How to get started with Python for Deep Learning and Data Science ... Navigating to a folder called Intuitive Deep Learning Tutorial on my Desktop. This could maybe explain the general saying that red wine causes headaches, but what about the quality? Standardization is a way to deal with these values that lie so far apart. Do you think that there could there be a way to classify entries based on their variables into white or red wine? You have an ideal scenario: there are no null values in the data sets. Before going deeper into Keras and how you can use it to get started with deep learning in Python, you should probably know a thing or two about neural networks. The model needs to know what input shape to expect and that’s why you’ll always find the input_shape, input_dim, input_length, or batch_size arguments in the documentation of the layers and in practical examples of those layers. Load Data. Some more research taught me that in quantities of 0.2 to 0.4 g/L, volatile acidity doesn’t affect a wine’s quality. For this, you can rely on scikit-learn (which you import as sklearn, just like before when you were making the train and test sets) for this. An epoch is a single pass through the entire training set, followed by testing of the verification set. The most simple neural network is the “perceptron”, which, in its simplest form, consists of a single neuron. For now, use StandardScaler to make sure that your data is in a good place before you fit the data to the model, just like before. Ideally, you will only see numbers in the diagonal, which means that all your predictions were correct! Don’t worry if you don’t get this entirely just now, you’ll read more about it later on! Today, we will see Deep Learning with Python Tutorial. The good thing about this, though, is that you can now experiment with optimizing the code so that the results become a little bit better. Let’s put your model to use! This means that there’s a connection from each perceptron in a specific layer to each perceptron in the next layer. In fact, we’ll be training a classifier for handwritten digits that boasts over 99% accuracy on the famous MNIST dataset. Pass in the train data and labels to fit(), determine how many epochs you want to run the fitting, the batch size and if you want, you can put the verbose argument to 1 to get more logs because this can take up some time. Note that when you don’t have that much training data available, you should prefer to use a small network with very few hidden layers (typically only one, like in the example above). A new browser window should pop up like this. Now that you have built your model and used it to make predictions on data that your model hadn’t seen yet, it’s time to evaluate its performance. Deep Learning is a part of machine learning that deals with algorithms inspired by the structure and function of the human brain. Statistics. The main intuition behind deep learning is that AI should attempt to mimic the brain. For now, import the train_test_split from sklearn.model_selection and assign the data and the target labels to the variables X and y. You’ll see that you need to flatten the array of target labels in order to be totally ready to use the X and y variables as input for the train_test_split() function. 3. Also, we will learn why we call it Deep Learning. Python is a general-purpose high level programming language that is widely used in data science and for producing deep learning algorithms. This tutorial explains how Python does just that. Since you only have two classes, namely white and red, you’re going to do a binary classification. At higher levels, however, volatile acidity can give the wine a sharp, vinegary tactile sensation. This brief tutorial introduces Python and its libraries like Numpy, Scipy, Pandas, Matplotlib; frameworks like Theano, TensorFlow, Keras. This tutorial has been prepared for professionals aspiring to learn the basics of Python and develop applications involving deep learning techniques such as convolutional neural nets, recurrent nets, back propagation, etc. Next, describe() offers some summary statistics about your data that can help you to assess your data quality. Some of the most basic ones are listed below. In this case, you see that both seem very great, but in this case it’s good to remember that your data was somewhat imbalanced: you had more white wine than red wine observations. The two seem to differ somewhat when you look at some of the variables from close up, and in other cases, the two seem to be very similar. Dive in. For the white wine, there only seem to be a couple of exceptions that fall just above 1 g/\(dm^3\), while this is definitely more for the red wines. You’ll read more about this in the next section. Use the compile() function to compile the model and then use fit() to fit the model to the data. This Keras tutorial introduces you to deep learning in Python: learn to preprocess your data, model, evaluate and optimize neural networks. The additional metrics argument that you define is actually a function that is used to judge the performance of your model. There is still a lot to cover, so why not take DataCamp’s Deep Learning in Python course? Are there any null values that you should take into account when you’re cleaning up the data? It might make sense to do some standardization here. Most of you will know that there are, in general, two very popular types of wine: red and white. Imbalanced data typically refers to a problem with classification problems where the classes are not represented equally.Most classification data sets do not have exactly equal number of instances in each class, but a small difference often does not matter. You can clearly see that there is white wine with a relatively low amount of sulfates that gets a score of 9, but for the rest, it’s difficult to interpret the data correctly at this point. You can also specify the verbose argument. This is something that you’ll deal with later, but at this point, it’s just imperative to be aware of this. Try this out in the DataCamp Light chunk below. However, before you start loading in the data, it might be a good idea to check how much you really know about wine (in relation to the dataset, of course). When you’re making your model, it’s therefore important to take into account that your first layer needs to make the input shape clear. The F1 Score or F-score is a weighted average of precision and recall. Since it can be somewhat difficult to interpret graphs, it’s also a good idea to plot a correlation matrix. Before you start modeling, go back to your original question: can you predict whether a wine is red or white by looking at its chemical properties, such as volatile acidity or sulphates? Your goal is to run through the tutorial end-to-end and get results. In this Python Deep Learning Tutorial, we will discuss the meaning of Deep Learning With Python. This is usually the first step to understanding your data. Next, you also see that the input_shape has been defined. That means that you’re looking to build a fairly simple stack of fully-connected layers to solve this problem. Today, you’re going to focus on deep learning, a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain. Deep Learning SQL. Hello and welcome to my new course "Computer Vision & Deep Learning in Python: From Novice to Expert" Making a computer classify an image using Deep Learning and Neural Networks is comparatively easier than it was before. This layer needs to know the input dimensions of your data. A PyTorch tutorial – deep learning in Python; Oct 26. The accuracy might just be reflecting the class distribution of your data because it’ll just predict white because those observations are abundantly present! Note that without the activation function, your Dense layer would consist only of two linear operations: a dot product and an addition. Python Tutorial: Decision-Tree for Regression; How to use Pandas in Python | Python Pandas Tutorial | Edureka | Python Rewind – 1 (Study with me) 100 Python Tricks / Q and A – Live Stream; Statistics for Data Science Course | Probability and Statistics | Learn Statistics Data Science The data points should be colored according to their rating or quality label: Note that the colors in this image are randomly chosen with the help of the NumPy random module. This can be easily done with the Python data manipulation library Pandas. Deep Learning basics with Python, TensorFlow and Keras An updated series to learn how to use Python, TensorFlow, and Keras to do deep learning. The batch size that you specify in the code above defines the number of samples that going to be propagated through the network. Python. However, the score can also be negative! You Can Do Deep Learning in Python! In this case, the tutorial assumes that quality is a continuous variable: the task is then not a binary classification task but an ordinal regression task. eyJsYW5ndWFnZSI6InB5dGhvbiIsInNhbXBsZSI6IiMgSW1wb3J0IHBhbmRhcyBcbmltcG9ydCBwYW5kYXMgYXMgcGRcblxuIyBSZWFkIGluIHdoaXRlIHdpbmUgZGF0YSBcbndoaXRlID0gX19fX19fX19fX18oXCJodHRwOi8vYXJjaGl2ZS5pY3MudWNpLmVkdS9tbC9tYWNoaW5lLWxlYXJuaW5nLWRhdGFiYXNlcy93aW5lLXF1YWxpdHkvd2luZXF1YWxpdHktd2hpdGUuY3N2XCIsIHNlcD0nOycpXG5cbiMgUmVhZCBpbiByZWQgd2luZSBkYXRhIFxucmVkID0gX19fX19fX19fX18oXCJodHRwOi8vYXJjaGl2ZS5pY3MudWNpLmVkdS9tbC9tYWNoaW5lLWxlYXJuaW5nLWRhdGFiYXNlcy93aW5lLXF1YWxpdHkvd2luZXF1YWxpdHktcmVkLmNzdlwiLCBzZXA9JzsnKSIsInNvbHV0aW9uIjoiIyBJbXBvcnQgcGFuZGFzIFxuaW1wb3J0IHBhbmRhcyBhcyBwZFxuXG4jIFJlYWQgaW4gd2hpdGUgd2luZSBkYXRhIFxud2hpdGUgPSBwZC5yZWFkX2NzdihcImh0dHA6Ly9hcmNoaXZlLmljcy51Y2kuZWR1L21sL21hY2hpbmUtbGVhcm5pbmctZGF0YWJhc2VzL3dpbmUtcXVhbGl0eS93aW5lcXVhbGl0eS13aGl0ZS5jc3ZcIiwgc2VwPSc7JylcblxuIyBSZWFkIGluIHJlZCB3aW5lIGRhdGEgXG5yZWQgPSBwZC5yZWFkX2NzdihcImh0dHA6Ly9hcmNoaXZlLmljcy51Y2kuZWR1L21sL21hY2hpbmUtbGVhcm5pbmctZGF0YWJhc2VzL3dpbmUtcXVhbGl0eS93aW5lcXVhbGl0eS1yZWQuY3N2XCIsIHNlcD0nOycpIiwic2N0IjoiRXgoKS50ZXN0X2ltcG9ydChcInBhbmRhc1wiKVxuRXgoKS50ZXN0X29iamVjdChcIndoaXRlXCIpXG5FeCgpLnRlc3Rfb2JqZWN0KFwicmVkXCIpXG5zdWNjZXNzX21zZz1cIlBlcmZlY3QhIFlvdSdyZSByZWFkeSB0byBnbyFcIiJ9, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, \(y = f(w_1*x_1 + w_2*x_2 + ... w_D*x_D)\), understand, explore and visualize your data, build up multi-layer perceptrons for classification tasks, Python Machine Learning: Scikit-Learn Tutorial, Convolutional Neural Networks in Python with Keras, Then, the tutorial will show you step-by-step how to use Python and its libraries to, Lastly, you’ll also see how you can build up, Next, all the values of the input nodes and weights of the connections are brought together: they are used as inputs for a.

Yamaha Pacifica 112j Specs, Apsley Trail Palos Verdes, Where Can I Buy Jerusalem Artichoke Tubers, Kneeboard For Sale, Chief Royal Engineer, Best Bushcraft Knife Under $50, Crab Meadow Golf Course Layout, What Do New Yorkers Wear, How To Keep Bobcats Away From Property, Factorial Using Recursion In Python,