Artificial intelligence is quickly changing the way fintech, insurtech and 5G operate during the covid-19 crisis and beyond. Machine learning and artificial intelligence are improving Fintech by increasing the accuracy and personalization of payment, lending, and insurance services while also helping to discover new borrower pools. Since that time the Covid-19 crisis and tragedy arose. On the one hand Paul Clarke noted that UK fintech investment slumps by 40% amid Covid-19 crisis, whilst on the other Deloitte in Beyond COVID-19: New opportunities for Fintech companies note that "As the COVID-19 pandemic continues to create uncertainty, many fintechs are under stress on a number of fronts. But, as the broader economy shifts from "respond" to "recover", new opportunities may be created for some fintechs. A key question is how fintechs may leverage their unique assets and skills to seize new opportunities in the future. It could be an opportune time to think big and act boldly." Pavitra R considered the impact of Covid-19 and noted in 5 U.S. FinTech startups reimagining the healthcare industry notes that FinTech is undoubtedly shaping the face of the Health Care industry. "FinTech companies leverage powerful innovations blockchain, Artificial Intelligence, and Machine Learning to eliminate the inefficiencies and knowledge gaps endemic to most healthcare payment plans." The likes of Nigel Wilson (@nigewillson) and Brian Ahier (@ahier) have stressed the importance of applying AI to positive use cases such as preventative medicine and improved Health Care outcomes. McKinsey in an article entitled AI-bank of the future: Can banks meet the AI challenge? " The potential for value creation is one of the largest across industries, as AI can potentially unlock $1 trillion of incremental value for banks, annually (Exhibit 1)." Source for image above: AI-bank of the future: Can banks meet the AI challenge? "While for many financial services firms, the use of AI is episodic and focused on specific use cases, an increasing number of banking leaders are taking a comprehensive approach to deploying advanced AI, and embedding it across the full lifecycle, from the front- to the back-office (Exhibit 2)" Source for image above: AI-bank of the future: Can banks meet the AI challenge?

Artificial intelligence (AI) has been hugely transformative in industries with access to huge datasets and trained algorithms to analyze and interpret them. Probably the most obvious examples of this success can be found in consumer-facing internet businesses like Google, Amazon, Netflix, or Facebook. Over the last two decades, companies such as these have grown into some of the world's largest and most powerful corporations. In many ways, their growth can be put down to their exposure to the ever-growing volumes of data being churned out by our increasingly digitized society. But if AI is going to unlock the truly world-changing value that many believe it will – rather than simply making some very smart people in Silicon Valley very rich – then businesses in other industries have to consider different approaches.

Without much ado, let's explore some more ML project ideas that will not just make your portfolio look good but will also significantly improve your machine learning skills. This is a curated list of some of the best machine learning projects for students, aspiring machine learning practitioners, and individuals from non-technical domains. You can work on these projects regardless of your background, as long as you have some coding and know-how of machine learning skills. This is a list of beginner and advanced-level machine learning projects. If you are new to the data industry and have little experience with real-life projects, start with beginner-level ML projects before moving on to the more challenging ones.

Image classification basically helps us in classifying images into different labels. It is like bucketing different images into the bucket they belong to. For, e.g. a model trained to identify the image of a cat and a dog will help in segregating different images of cats and dogs respectively. There are multiple deep learning frameworks like Tensorflow, Keras, Theano, etc that can be used to create image classification models. Today we will create an image classification model from scratch using Keras and Tensorflow.

Over the past few years, and after decades as little more than a mathematical curiosity, useful industrial applications of AI have become commonplace. AI is now recognised as one of the primary drivers of computing development. In 2018, Canadians Yoshua Bengio, Geoffrey Hinton and Yann LeCun – the'godfathers of AI' – received the Turing Award, computing's highest honour, for their foundational work on deep learning. The International Data Corporation forecasts that worldwide revenues for the AI market will grow to nearly $330 billion in 2021, and will exceed $550 billion by 2024 (IDC Semiannual AI Tracker, January 2021). Driven and enabled by the extraordinary growth of data globally, this surge in the AI industry has also spurred a flood of AI-related patenting.

This phenomenal rise in AI has also created some subset applications that can be used in various fields. One such technology is Deep Learning. Deep Learning involves an AI-based technique that teaches computers to structure data in the form of layers the same way humans do. It uses an artificial neural network to create layers of data. Its innovation has helped overcome limitations in ML and made AI applicable to a broader set of use cases. In this article, we have shared some use cases of Deep Learning creating value in our everyday life.

To answer these questions in the context of face perception, we joined forces with our collaborators at Caltech (Doris Tsao) and the Chinese Academy of Science (Le Chang). We chose faces because they are well studied in the neuroscience community and are often seen as a "microcosm of object recognition". In particular, we wanted to compare the responses of single cortical neurons in the face patches at the end of the visual processing hierarchy, recorded by our collaborators to a recently emerged class of so called "disentangling" deep neural networks that, unlike the usual "black box" systems, explicitly aim to be interpretable to humans. A "disentangling" neural network learns to map complex images into a small number of internal neurons (called latent units), each one representing a single semantically meaningful attribute of the scene, like colour or size of an object (see Figure 1). Unlike the "black box" deep classifiers trained to recognise visual objects through a biologically unrealistic amount of external supervision, such disentangling models are trained without an external teaching signal using a self-supervised objective of reconstructing input images (generation in Figure 1) from their learnt latent representation (obtained through inference in Figure 1).

Workshops generally run from 9:00am - 5:00pm. Simultaneous access to two screens is highly recommended for best learning experience. Examples include one computer with two screens, two computers, one laptop and one tablet, etc. Overview In the past decade, neural networks have become a valuable tool for data scientists, revolutionizing fields such as text processing, image analysis, genomic/proteomic data analysis, data clustering, and much more. However, these algorithms can be very difficult to understand, interpret, and program. This workshop will first cover the theory and proper applications of various neural networks (multilayer perceptrons, convolutional neural networks, long-short term memory models, autoencoders, etc.).

Not a week goes by without hearing about new applications of computer vision. If you take a look at the job market for machine learning, you'll notice that there are so many companies using computer vision to do all sorts of cool things. This is thanks to deep learning! I've seen mobile apps that use computer vision to tell you how many calories you have in your food from a picture of your plate. I've seen products that use computer vision to detect ships docked in the port.

Microsoft recently announced the launch of a new service that makes AI models from OpenAI available on Microsoft's Azure platform. The company specifically mentions GPT-3, its groundbreaking language model capable under certain circumstances of producing text with humanlike accuracy and fluency. The most well-known example of a new generation of AI language models is GPT-3. These systems generally serve as autocomplete: feed them the text, whether it's an email or poem; they can finish what you started by interpreting languages with their capacity for summarizing papers assessing sentiment in texts while also generating project ideas which Microsoft claims its Azure OpenAI Service will help users accomplish more easily. Using the OpenAI API in Azure, Microsoft is making it possible for companies of all kinds to deploy GPT-3 legally and securely.