Video Understanding, automates a wide range of business use cases, from retail to health care to agriculture, it enables computers to identify behaviors, objects, and activities in images. In its latest release, Lightning Flash provides support for Video Understanding using Facebook AI Research's new PyTorchVideo library powered by Lightning. Flash is a library for fast prototyping, baselining, and fine-tuning scalable Deep Learning tasks. Using Flash for Video Understanding enables you to train, finetune and infer PyTorch Video models on your own data without being overwhelmed by all the details. Once you get a baseline model you can then seamlessly override the default configurations and experiment with the full flexibility of PyTorch Lightning to get state-of-the-art results on your dataset.

Karl Tuyls, a former RoboCup participant and local chair of the 2D simulation league (2013), recently published an article along with his colleagues at DeepMind called Game plan: what AI can do for football, and what football can do for AI. Karl Tuyls: The long-term vision in this project is to advance research in multi-agent decision-making by building an automated video assistant coach for real-world soccer (or football), that can help coaches and teams in analyzing games, making tactical choices in a match (e.g. in set pieces situations), improve their overall game-play, and even assist with in-game analysis and decision-making. Next to that one can also think additionally of human factors like injury prediction and the search for new players. For this we are blending research from game theory, vision and machine learning. So far our work has focused on game-theoretic analysis of set pieces and on trajectory predictions of players and ball with the purpose to allow for counterfactual reasoning (what happens if player X moves in direction Y, for example).

The internet is flooded with the resources and tutorials related to Data Science-AI-Machine Learning-Deep Learning. However, once you have read enough theory (and wrote some preliminary code), next step should be (ideally) to understand how machine learning is applied at scale in the Tech companies and how you can deep dive in the area of your interest.

I recently started an AI-focused educational newsletter, that already has over 80,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Principal component analysis(PCA) is one of the key algorithms that are part of any machine learning curriculum. Initially created in the early 1900s, PCA is a fundamental algorithm to understand data in high-dimensional spaces which are common in deep learning problems.

While artificial intelligence (AI) applications are becoming increasingly capable of solving even the most complex of our problems requiring human-like cognition, the black box of artificial intelligence makes it difficult for us to understand how these systems actually go about solving these problems. Although humans had always known about the existence of fire, it was only when we learned to control or "tame" fire that we really kickstarted the journey of rapid technological progress and evolution we currently find ourselves in. Now, over a million years later, we find ourselves at a similar juncture--albeit faced with an entity that we created instead of a natural phenomenon. The creation of artificial intelligence, undoubtedly, is a step into an era of unprecedented growth unlike any we've seen before. But, a true leap can only be achieved once we "tame" the technology, as it were, by first illuminating the black box of artificial intelligence that will enable us to better control the outcomes affected by the technology.

A generative Adversarial Network (GAN) is a powerful algorithm of Deep Learning. Generative Adversarial Network is used in Image Generation, Video Generation, and Audio Generation. In short, GAN is a Robot Artist, who can create any kind of art perfectly. And in this Generative Adversarial Networks (GANs) Specialization, you will learn how to build basic GANs using PyTorch and advanced DCGANs using convolutional layers. You will use GANs for data augmentation and privacy preservation, survey GANs applications, and examine and build Pix2Pix and CycleGAN for image translation. There are 3 courses in this Specialization program where you will gain hands-on experience in GANs. Now, let's see all the 3 courses of this Specialization Program-

I have trained a model through PyTorch and wish to bring it into production through a Windows desktop app for real-time inference. My current plan is to convert my PyTorch model to a TorchScript. Then in actual C inference, I'll then use LibTorch to call the TorchScript-formatted model. My goal is to integrate this into a desktop app and run it in real-time on a stream of data. Also, I plan on only running on CPU, not GPU.

Deep learning is a state-of-the-art field in machine learning domain. Deep learning models can learn from examples and they need to be trained with sufficient data. The predictions of deep learning algorithms can boost the performance of businesses. However, they have challenges such as being data hungry, hard to interpret and can be expensive due to the cost of collecting and labelling data. Deep learning, also called deep structured learning or hierarchical learning, is a set of machine learning methods which is part of the broader family of artificial neural network based machine learning methods.

The Way of the Future, a church founded by a former Google and Uber engineer, is now a thing of the past. It's been a few months since the world's first AI-focused church shuttered its digital doors, and it doesn't look like its founder has any interest in a revival. But it's a pretty safe bet we'll be seeing more robo-centric religious groups in the future. Perhaps, however, they won't be about worshipping the machines themselves. The world's first AI church "The Way of the Future," was the brainchild of Anthony Levandowski, a former autonomous vehicle developer who was convicted on 33 counts of theft and attempted theft of trade secrets. In the wake of his conviction, Levandowski was sentenced to 18 months in prison but his sentence was delayed due to COVID and, before he could be ordered to serve it, former president Donald Trump pardoned him.

LaMDA's conversational skills have been years in the making. Like many recent language models, including BERT and GPT-3, it's built on Transformer, a neural network architecture that Google Research invented and open-sourced in 2017. That architecture produces a model that can be trained to read many words (a sentence or paragraph, for example), pay attention to how those words relate to one another and then predict what words it thinks will come next. But unlike most other language models, LaMDA was trained on dialogue. During its training, it picked up on several of the nuances that distinguish open-ended conversation from other forms of language.