This article was published as a part of the Data Science Blogathon. Social Media platforms are a common way to share interesting and informative images. Food images, especially related to different cuisines and cultures, are a topic that appears to be frequently trending. Social media platforms like Instagram have a large number of images belonging to different categories. We all might have used the search options on google images or Instagram to browse through yummy-looking cake images for ideas.

Computer vision is one of the most trending subfields in Artificial Intelligence because of its wide variety of applications. In some domains, they even suppress human intelligence in recognising images with speed and accuracy. In this article, we will demonstrate one of the most popular computer vision applications -- multiclass image classification problems using fastAI library and TPU as the hardware accelerator. TPU, or Tensor Processing Unit accelerates the training process of heavy deep learning models. We use image classification for recognising objects in the image and can be used in detecting brand names using logo, classifying objects, etc.

In this tutorial, you will learn how to perform video classification using FastAI, Python, and Deep Learning. FastAI is a Deep Learning library that is built on the top of Pytorch. There are freely available tutorials/courses for FastAI. I am also currently enrolled in Practical Deep Learning for Coders course.

You may have also used or heard of their equally high quality deep learning, machine learning, linear algebra, and natural language processing courses. It has also been a major protagonist in the development of transfer learning for natural language processing; performed an investigation and evaluation of the research into the use of face masks for suppressing the spread of COVID-19; been a voice at the forefront of applied data ethics. This library provides easier API access to a variety of machine learning-related functionality, especially when it comes to neural networks. Much of this aspect of the library sits atop PyTorch, making the creation of neural networks with this lower level library easier and flexible for machine learning coders of all skill levels. As a bridge between their courseware and the fastai library which it uses, Jeremey Howard and Sylvain Gugger are working on a book titled Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD, which is not yet available.

With a few lines of code, you can train top quality models for vision, text and structured problems. Wouldn't it be great if by adding just 4 more lines, you could track your entire experimentation process? This is what NeptuneMonitorcallback, that we have just open-sourced, is all about. Check the example versioned fastai project here. Let's take a slightly modified mnist example from fastai.

Classical machine learning and deep learning have fantastic applications. One of these applications is the multiclass classification where the last layer may have more than one node (or neuron) predictor. For instance, the most popular multiclass classifier in machine learning is the MNIST digits classifier whilst for deep learning, there is the must try version of MNIST which is the dogs and cats classifier. Now, which one is the best to predict other types of object, insects for example? Thus, I took seven classes of insects (actually, it was six because spiders are not insects but arachnids. The seven chosen classes are: beetles, cockroaches, dragonflies, flies, spiders, termites, and thrips.

In the data-driven future of project management, project managers will be augmented by artificial intelligence that can highlight project risks, determine the optimal allocation of resources and automate project management tasks. For example, many organisations require project managers to provide regular project status updates as part of the delivery assurance process. These updates typically consist of text commentary and an associated red-amber-green (RAG) status, where red indicates a failing project, amber an at-risk project and green an on-track project. Wouldn't it be great if we could automate this process, making it more consistent and objective? In this post I will describe how we can achieve exactly that by applying natural language processing (NLP) to automatically classify text commentary as either red, amber or green status.

By Rachel Thomas, Co-founder at fast.ai There is a powerful technique that is winning Kaggle competitions and is widely used at Google (according to Jeff Dean), Pinterest, and Instacart, yet that many people don't even realize is possible: the use of deep learning for tabular data, and in particular, the creation of embeddings for categorical variables. Despite what you may have heard, you can use deep learning for the type of data you might keep in a SQL database, a Pandas DataFrame, or an Excel spreadsheet (including time-series data). I will refer to this as tabular data, although it can also be known as relational data, structured data, or other terms (see my twitter poll and comments for more discussion). Tabular data is the most commonly used type of data in industry, but deep learning on tabular data receives far less attention than deep learning for computer vision and natural language processing.

There is a powerful technique that is winning Kaggle competitions and is widely used at Google (according to Jeff Dean), Pinterest, and Instacart, yet that many people don't even realize is possible: the use of deep learning for tabular data, and in particular, the creation of embeddings for categorical variables. Despite what you may have heard, you can use deep learning for the type of data you might keep in a SQL database, a Pandas DataFrame, or an Excel spreadsheet (including time-series data). I will refer to this as tabular data, although it can also be known as relational data, structured data, or other terms (see my twitter poll and comments for more discussion). Tabular data is the most commonly used type of data in industry, but deep learning on tabular data receives far less attention than deep learning for computer vision and natural language processing. The material from this post is covered in much more detail starting around 1:59:45 in the Lesson 3 video and continuing in Lesson 4 of our free, online Practical Deep Learning for Coders course.