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Raymond Castillo
Raymond Castillo

AI With Python

We assume that the reader has basic knowledge about Artificial Intelligence and Python programming. He/she should be aware about basic terminologies used in AI along with some useful python packages like nltk, OpenCV, pandas, OpenAI Gym, etc.

AI with Python

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A common machine learning task is supervised learning, in which you have a dataset with inputs and known outputs. The task is to use this dataset to train a model that predicts the correct outputs based on the inputs. The image below presents the workflow to train a model using supervised learning:

Another name for input data is feature, and feature engineering is the process of extracting features from raw data. When dealing with different kinds of data, you need to figure out ways to represent this data in order to extract meaningful information from it.

Deep learning is a technique in which you let the neural network figure out by itself which features are important instead of applying feature engineering techniques. This means that, with deep learning, you can bypass the feature engineering process.

Vectors, layers, and linear regression are some of the building blocks of neural networks. The data is stored as vectors, and with Python you store these vectors in arrays. Each layer transforms the data that comes from the previous layer. You can think of each layer as a feature engineering step, because each layer extracts some representation of the data that came previously.

With neural networks, the process is very similar: you start with some random weights and bias vectors, make a prediction, compare it to the desired output, and adjust the vectors to predict more accurately the next time. The process continues until the difference between the prediction and the correct targets is minimal.

Working with neural networks consists of doing operations with vectors. You represent the vectors as multidimensional arrays. Vectors are useful in deep learning mainly because of one particular operation: the dot product. The dot product of two vectors tells you how similar they are in terms of direction and is scaled by the magnitude of the two vectors.

If you add more layers but keep using only linear operations, then adding more layers would have no effect because each layer will always have some correlation with the input of the previous layer. This implies that, for a network with multiple layers, there would always be a network with fewer layers that predicts the same results.

To restate the problem, now you want to know how to change weights_1 and bias to reduce the error. You already saw that you can use derivatives for this, but instead of a function with only a sum inside, now you have a function that produces its result using other functions.

Now you can start updating the weights. You want to know how to change the weights to decrease the error. This implies that you need to compute the derivative of the error with respect to weights. Since the error is computed by combining different functions, you need to take the partial derivatives of these functions.

You can say that the derivative of y = f(x) is the derivative of f with respect to x. Using this nomenclature, for derror_dprediction, you want to know the derivative of the function that computes the error with respect to the prediction value.

In Machine Learning it is common to work with very large data sets. In this tutorial we will try to make it as easy as possible to understand the different concepts of machine learning, and we will work with small easy-to-understand data sets.

Python is commonly used to develop AI applications, such as improving human to computer interactions, identifying trends, and making predictions. One way that Python is used for human to computer interactions is through chatbots. Chatbots use artificial intelligence and natural language processing to allow us to communicate with a computer more naturally. We interact with chatbots using text or voice commands when we are trying to contact customer service or when we are asking our Alexa or Google Home to answer a question or perform a task. Let's learn how to make AI in Python!

Today you will learn how to make your first AI in Python using some basic techniques. Through this tutorial, you will get a basic understanding of how chatbots work. The chatbots you interact with everyday are pretty smart because they use additional algorithms and libraries. You can explore some examples of these at the bottom of this tutorial or in our live online, small-group Python for AI class, led by an expert, and designed by professionals from Google, Stanford, and MIT.

Let's create a couple more lists of keywords and responses that your AI chatbot will know. Name these lists keywords and responses. Make sure the keywords and responses are in the same order. For example, if the user enters something containing the keyword "book", then the AI chatbot will respond with "I know about a lot of books." The keyword "book" and the response "I know about a lot of books" are both in the third position of both lists.

We can choose a random greeting and goodbye each time the user interacts with the AI chatbot. First, we need to import the random module to include this capability in our program. At the top of your program (first line), add:

If the user's response does not contain a keyword the AI chatbot already knows, we need to teach it how to respond. Let's start by updating our while and for loops with a keyword_found variable. At the beginning of the while loop, we'll set it to false to indicate that it has not been found. In the if statement inside the for loop, we'll set the keyword_found variable to true.

Written by Jamila Cocchiola who has always been fascinated with technology and its impact on the world. The technologies that emerged while she was in high school showed her all the ways software could be used to connect people, so she learned how to code so she could make her own! She went on to make a career out of developing software and apps before deciding to become a teacher to help students see the importance, benefits, and fun of computer science.

In this article we will show you why Python is used in artificial intelligence and machine learning and what are the benefits behind it. And if you have ideas for the implementation of projects with AI or ML, but you need help in their implementation, you can immediately contact us for additional advice.

Working in the ML and AI industry means dealing with a bunch of data that you need to process in the most convenient and effective way. The low entry barrier allows more data scientists to quickly pick up Python and start using it for AI development without wasting too much effort on learning the language.

Moreover, flexibility allows developers choose the programming styles which they are fully comfortable with or even combine these styles to solve different types of problems in the most efficient way.

The next advantage of python for AI and ML development is platform independence. Python is not only comfortable to use and easy to learn but also very versatile. What we mean is that Python for machine learning development can run on any platform including Windows, MacOS, Linux, Unix, and twenty-one others. To transfer the process from one platform to another, developers need to implement several small-scale changes and modify some lines of code to create an executable form of code for the chosen platform. Developers can use packages like PyInstaller to prepare their code for running on different platforms.

Another case of how Python is used in AI and ML development. For example, travel industry giant Skyscanner used a Python unsupervised ML algorithm to predict the behavior of new airplane routes. They compared thousands of origins and destinations, evaluating each one of them with 30 different criteria to define the demand of passengers. Their results are displayed on a dashboard, where you can choose any origin city to see the groups of destinations numbered from 0 to 9 and their characteristics. Such an example of AI implementation in the traveling industry is extremely helpful for suggesting destinations to the users, assisting the creation of marketing budgets , as well as setting an initial price for new routes.

Perhaps one of the most successful examples of using Python in AI. Uber developed an ML platform Michelangelo PyML using Python. They use it for online and offline predictions solving day-to-day tasks. The Michelangelo PyML is the extension of the initial Michelangelo product which was scalable but not flexible enough. Now, users can validate models with PyML and then replicate them in Michelangelo for full efficiency and scalability.

AI and ML are fast-growing and universal technologies that let scientists resolve real-life dilemmas and come up with clever solutions. The reason why many of them consider Python the perfect programming language for AI is due to the following advantages: So, we have seen that the role of Python in AI and data science is really very strong. That is why Python and artificial intelligence are so popular in combination with each other.

Learn the foundations of calculus to understand how to train a neural network: plotting, derivatives, the chain rule, and more. See how these mathematical skills visually come to life with a neural network example.

Juan is a computational physicist with a Masters in Astronomy. He is finishing his PhD in Biophysics. He previously worked at NASA developing space instruments and writing software to analyze large amounts of scientific data using machine learning techniques.

Mike is a Content Developer with a BS in Mathematics and Statistics. He received his PhD in Cognitive Science from the University of Irvine. Previously, he worked on Udacity's Data Analyst Nanodegree program as a support lead. 041b061a72


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