If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Some of my most popular blogs on Medium are about libraries that I believe you should try. In this blog, I will focus on low-code machine learning libraries. The truth is that many data scientists believe that low-code libraries are shortcuts and should be avoided. I'm afraid I have to disagree! I think that low-code libraries should be included in our pipeline to help us make important decisions without wasting time.
Learning Python can be difficult. You might spend a lot of time watching videos and reading books; however, if you can't put all the concepts learned into practice, that time will be wasted. This is why you should get your hands dirty with Python projects. A project will help you bring together everything you've learned, stay motivated, build a portfolio and come up with ways of approaching problems and solving them with code. In this article, I listed some projects that helped me level up my Python code and hopefully will help you too.
I have developed a fascinating dataset -- a list of users, installed applications, user gender, and statistics on the gender distribution for apps. For a successful advertising campaign, working with a segment is vital, and the gender of the user simplifies the work of selecting segments at times. I will tell you how collecting statistics on applications allow ML to predict a user's gender. First of all, it is interesting to look at how gender is distributed among devices. One might expect the devices to be roughly equally divided, but this has not happened.
PyTorch, the Facebook-backed open-source library for the Python programming language, has reached version 1.9 and brings major improvements for scientific computing. PyTorch has become one of the more important Python libraries for people working in data science and AI. Microsoft recently added enterprise support for PyTorch deep learning on Azure. PyTorch has also become the standard for AI workloads at Facebook. Google's TensorFlow and PyTorch integrate with important Python add-ons like NumPy and data-science tasks that require faster GPU processing.
Sentiment Analysis, as the name suggests, it means to identify the view or emotion behind a situation. It basically means to analyze and find the emotion or intent behind a piece of text or speech or any mode of communication. In this article, we will focus on the sentiment analysis of text data. We, humans, communicate with each other in a variety of languages, and any language is just a mediator or a way in which we try to express ourselves. And, whatever we say has a sentiment associated with it.
Data exploration is by far one of the most important aspects of any data analysis task. The initial probing and preliminary checks that we perform, using the vast catalog of visualization tools, give us actionable insights into the nature of data. However, the choice of visualization tool at times is more complicated than the task itself. On the one hand, we have libraries that are easier to use but are not so helpful in showing complex relationships in data. Then there are others that render interactivity but have a considerable learning curve.
Is it difficult for you to develop your infrastructure costs? In this article, I provide a list of platforms for AI/ML developers offered as a service. The platforms give a Web interface that may scale your computer as required. The technologies have been developed and maintained over time. The products that use these technologies are hungry for resources and need enough power to create and launch.
Tensorflow/Keras & Pytorch are by far the 2 most popular major machine learning libraries. Tensorflow is maintained and released by Google while Pytorch is maintained and released by Facebook. There are multiple changes between Tensorflow 1 and Tensorflow 2.x, I am going to try to pinpoint the most important ones. The first one is the release of Tensorflow.js. With web applications being more and more dominant, the need for deploying models on browsers has grown quite a lot.
Keras is a very powerful open source Python library which runs on top of top of other open source machine libraries like TensorFlow, Theano etc, used for developing and evaluating deep learning models and leverages various optimization techniques. There are many in-built layers in Keras like Conv2D, MaxPooling2D, Dense, Flatten etc for different use cases and applications. In this project we are going to create custom(Parametric ReLU) layer and use it in the NN model to solve a multi classification problem (We will be using MNIST dataset) . We will be using the popular MNIST dataset. We will load the data using utils and then visualize it.
All others have a large and varying degree of missing values. Within the missingno library, there are four types of plots for visualising data completeness: the barplot, the matrix plot, the heatmap, and the dendrogram plot. Each has its own advantages for identifying missing data. Let's take a look at each of these in turn. The barplot provides a simple plot where each bar represents a column within the dataframe. The height of the bar indicates how complete that column is, i.e, how many non-null values are present.