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) …
PyTorch online course has been designed for those students who can learn the concepts at a fast pace. We will provide in-depth knowledge with the help of different PyTorch examples. We will also provide PyTorch tutorial in which you will learn different concepts like how to install PyTorch. You will also learn the process of configuring PyTorch. First the instructors will tell you about what is PyTorch and then they will gradually move towards basic and then to advanced topics.
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.
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.
Image classification is a task where we want to predict which class belongs to an image. This task is difficult because of the image representation. If we flatten the image, it will create a long one-dimensional vector. Also, that representation will lose the neighbor information. Therefore, we need deep learning for extracting features and predict the result.
Google's TensorFlow and Facebook's PyTorch are the most popular machine learning frameworks. The former has a two-year head start over PyTorch (released in 2016). TensorFlow's popularity reportedly declined after PyTorch bursted into the scene. However, Google released a more user-friendly TensorFlow 2.0 in January 2019 to recover lost ground. PyTorch is emerging as a leader in terms of papers in leading research conferences.
Kaggle Kernels and Google Colab are great. I would drop my mic at this point if this article was not about building a custom ML workstation. There are always some "buts" that make our lives harder. When you start to approach nearly real-life problems and you see hundreds of gigabytes of large datasets, your gut feeling starts to tell you that your CPU or AMD GPU devices are not going to be enough to do meaningful things. This is how I came here. I was taking part in Human Protein Atlas (HPA) -- Single Cell Classification competition on Kaggle. I thought I would be able to prototype locally and then execute notebooks on the cloud GPU. As it turned out, there are a lot of frictions in the mentioned workflow. First of all, my solution source code quickly became an entire project with a lot of source code and dependencies. I used poetry as a package manager and decided to generate an installable package every time I made meaningful changes to the project in order to test them in the cloud. These installable packages I was uploading into a private Kaggle dataset which in turn was mounted to a notebook.
Last week, Facebook said it would migrate all its AI systems to PyTorch. Facebook's AI models currently perform trillions of inference operations every day for the billions of people that use its technology. Its AI tools and frameworks help fast track research work at Facebook, educational institutions and businesses globally. Big tech companies including Google (TensorFlow) and Microsoft (ML.NET), have been betting big on open-source machine learning (ML) and artificial intelligence (AI) frameworks and libraries. Predominantly, Facebook has been using two distinct but synergistic frameworks for deep learning: PyTorch and Caffe2.
In this 2 hour-long project-based course, you will learn to implement neural style transfer using PyTorch. Neural Style Transfer is an optimization technique used to take a content and a style image and blend them together so the output image looks like the content image but painted in the style of the style image. We will create artistic style image using content and given style image. We will compute the content and style loss function. We will minimize this loss function using optimization techniques to get an artistic style image that retains content features and style features.
This could be a command you give one of your drones walking in the forest. The technology we're gonna use here is so light, I'm sure this is far from a fantasy. In my previous article, I walked through a first draft to classify mushrooms using CNNs with Tensorflow libraries. I used the Fungus competition dataset available on Kaggle. Many images of this dataset contain multiple objects with a rich background.
Non Maximum Suppression (NMS) is a technique used in numerous computer vision tasks. It is a class of algorithms to select one entity (e.g., bounding boxes) out of many overlapping entities. We can choose the selection criteria to arrive at the desired results. The criteria are most commonly some form of probability number and some form of overlap measure (e.g. This post will go over how it works and implement it in Python using the PyTorch framework.