"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
As social media is increasingly being used as people's primary source for news online, there is a rising threat from the spread of malign and false information. With an absence of human editors in news feeds and a growth of artificial online activity, it has become easier for various actors to manipulate the news that people consume. RAND Europe was commissioned by the UK Ministry of Defence's (MOD) Defence and Security Accelerator (DASA) to develop a method for detecting the malign use of information online. The study was contracted as part of DASA's efforts to help the UK MOD develop its behavioural analytics capability. Our study found that online communities are increasingly being exposed to junk news, cyber bullying activity, terrorist propaganda, and political reputation boosting or smearing campaigns.
Digital generated image of data. Lemonade is one of this year's hottest IPOs and a key reason for this is the company's heavy investments in AI (Artificial Intelligence). The company has used this technology to develop bots to handle the purchase of policies and the managing of claims. Then how does a company like this create AI models? Well, as should be no surprise, it is complex and susceptible to failure.
It's not an exaggeration to say that when it comes to the future of human progress, nothing is more important than Artificial Intelligence (AI). Although often thought to only be associated with everyday entities such as self-driving cars and Google search rankings, AI is in fact the driving force behind virtually every major and minor technology that's bringing people together and solving humanity's problems. You'd be hard-pressed to find an industry that hasn't embraced AI in some shape or form, and our reliance on this field is only going to grow in the coming years--as microchips become more powerful and quantum computing begins to be more accessible. So it should go without saying that if you're truly interested in staying ahead of the curve in an AI-driven world, you're going to have to have at least a baseline understanding of the methodologies, programming languages, and platforms that are used by AI professionals around the world. This can be an understandably intimidating reality for anyone who doesn't already have years of experience in tech or programming, but the good news is that you can master the basics and even some of the more advanced elements of AI and all of its various implications without spending an obscene amount of time or money on a traditional education.
Dmitry Dolgorukov is the Co-Founder and CRO of HES Fintech, a leader in providing financial institutions with intelligent lending platforms. Artificial intelligence survived the early stages of the maturity cycle and reached the plateau of productivity to the extent that Andrew Ng claimed, "AI is the new electricity." Stanford University indicates that the number of active AI-based startups increased by 1,400% between 2000 and 2017. In this regard, Forbes cites research findings revealing that AI-associated startups attract up to 50% more in funds than "ordinary" technological companies. If you want an analogy, it's the Gold Rush, but digital.
Why? Existing tools are not well-suited to time series tasks and do not easily integrate together. Methods in the scikit-learn package assume that data is structured in a tabular format and each column is i.i.d. Packages containing time series learning modules, such as statsmodels, do not integrate well together. Further, many essential time series operations, such as splitting data into train and test sets across time, are not available in existing python packages. To address these challenges, sktime was created.
We will cover various fundamental topics and areas in AI including: Deep Learning, Computer Vision, Natural Language Processing, Time Series, and more. Every day covers a different AI topic starting with theory and building on the theory with interactive coding projects. You will leave every day of the bootcamp with a new AI project in a unique area. At the end of the bootcamp, you will put all that you have learned into a final project of your choice and present the outcome of your work to a panel of judges on the last day! You can check the schedule on the website for more details on the covered topics.
PYRO: Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. These are a few frameworks and projects that are built on top of TensorFlow and PyTorch. You can find more on Github and the official websites of TF and PyTorch. In a world of TensorFlow, PyTorch is capable of holding on its own with its strong points. PyTorch is a go to framework that lets us write code in a more pythonic way.
In this article, we will explain briefly about some of the best books that can help you understand the concepts of Machine Learning, and guide you in your journey in becoming an expert in this engaging domain. Moreover, these books are a great source of inspiration, filled with ideas and innovations, granted that you are familiar with the fundamentals of programming languages. As the title explains, if you're an absolute beginner to Machine Learning, this book should be your entry point. Requiring little to no coding or mathematical background, all the concepts in the book have been explained very clearly. Examples are followed by visuals to present the topics in a friendlier manner, for understanding the vitals of ML.
In this part I will mostly focus on the part relevant for the GAN, so I start, after loading and transforming the dataset and moving it to the GPU. If you would like to see the whole code you can find it here. There you can also find the final weights. If you want to know more about how GANs generally work, you can find some information in my notebook or you can watch this video. First we'll look at some of the hyperparameters I defined at the beginning of the code: The discriminator takes a 3x64x64 tensor as input.
You can use MATLAB with AutoML to support many workflows, such as feature extraction and selection and model selection and tuning. Feature extraction reduces the high dimensionality and variability present in the raw data and identifies variables that capture the salient and distinctive parts of the input signal. The process of feature engineering typically progresses from generating initial features from the raw data to selecting a small subset of the most suitable features. But feature engineering is an iterative process, and other methods such as feature transformation and dimensionality reduction can play a role. Feature selection identifies a subset of features that still provide predictive power, but with fewer features and a smaller model.