Original Source Here What is your learning budget? The first thing you need to determine is how much time you are able to dedicate to studying. It is important to be able to set aside decent blocks of time for periods of intensive study. Ideally, these should be at least two hours in length, and I recommend trying to set aside time for at least five of these periods per week. In order to stick to a learning schedule that includes consistent blocks of time, it needs to be realistic and needs to fit around your other commitments. To determine how much time you realistically have available for these intense periods of study I advise first taking a pen and paper and writing down the time commitments you already have on a typical day, including weekends. Let’s say you have to work for 8 hours, you need to sleep (8 hours), you then need time for exercise, eating, household chores and family. Let’s say these take another 2 -3 hours. This might leave you with a daily learning budget o...

Original Source Here 3D scan is a very expensive technology but you can reach good result with MeshRoom but you need to much photos. But in some simple cases… Continue reading on Medium » AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.io/2021/09/01/dense-depth-3d-from-a-single-image/

https://venturebeat.com/wp-content/uploads/2017/11/shutterstock_344231354-e1628723101457.jpg?w=1200&strip=all Original Source Here The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Register now! Trengo , a Dutch omnichannel communications platform, today announced that it raised $36 million in series A funding led by Insight Partners and Peak Capital. With the new funds, Trengo aims to attract engineering talent, grow internationally, and develop its platform further, according to CEO Patrick Meutzner. The pace of digital transformation in communications can be daunting, creating business inefficiencies. Increased remote work put pressure on alignment within teams, giving rise to the market for enterprise communication platforms. There’s been a significant increase in customer channels — for example, WhatsApp Business, Instagram Direct Message and Google Business Messages — as customers expect better and faster service...

Original Source Here Easy to digest information on Pose Estimation Continue reading on Medium » AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.io/2021/08/31/quick-ml-introduction-to-pose-estimation/

Original Source Here This is a guest post from Mikael Graindorge, Sales Operations Leader at Thermo Fisher Scientific. In the life sciences industry, data is growing in abundance and is getting increasingly complex, which makes it challenging to use traditional analytics methodologies. At Thermo Fisher Scientific , our mission is to make the world healthier, cleaner, and safer, and to realize this vision, we need to make optimal decisions by extracting insights from the large volume and variety of data available to us. To do this effectively, we need to empower team members with machine learning (ML) skills so we can achieve our vision as an organization. ML, as a capability, has the transformative power to enable people from all backgrounds to effectively use data in their decision-making processes. However, using ML isn’t the same thing as using ML effectively. To share an analogy, having access to a kitchen doesn’t necessarily mean you can cook well. There is a big difference b...

Original Source Here The last few years have seen the rise of transformer deep learning architectures to build natural language processing (NLP) model families. The adaptations of the transformer architecture in models such as BERT, RoBERTa, T5, GPT-2, and DistilBERT outperform previous NLP models on a wide range of tasks, such as text classification, question answering, summarization, and text generation. These models are exponentially growing larger in size from several million parameters to several hundred billion parameters. As the number of model parameters increases, so does the computational infrastructure that is necessary to train these models. This requires a significant amount of time, skill, and compute resources to train and optimize the models. Unfortunately, this complexity prevents most organizations from using these models effectively, if at all. Wouldn’t it be more productive if you could just start from a pre-trained version and put them to work immediately? T...

Original Source Here I have owned dozens of “Where’s Waldo?” books when I was young and I am sure you have too. If you don’t know what it is, “Where’s Waldo?”… Continue reading on Medium » AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.io/2021/08/31/finding-waldo-using-a-simple-convolutional-neural-network/

Original Source Here Posted by Forrester Cole, Software Engineer and Tali Dekel, Research Scientist Image and video editing operations often rely on accurate mattes — images that define a separation between foreground and background. While recent computer vision techniques can produce high-quality mattes for natural images and videos, allowing real-world applications such as generating synthetic depth-of-field , editing and synthesising images , or removing backgrounds from images , one fundamental piece is missing: the various scene effects that the subject may generate, like shadows, reflections, or smoke, are typically overlooked. In “ Omnimatte: Associating Objects and Their Effects in Video ”, presented at CVPR 2021 , we describe a new approach to matte generation that leverages layered neural rendering to separate a video into layers called omnimattes that include not only the subjects but also all of the effects related to them in the scene. Whereas a typical state-of-th...

Original Source Here Machine learning (ML) is highly iterative and complex in nature, and requires data scientists to explore multiple ways in which a business problem can be solved. Data scientists have to use tools that support interactive experimentation so you can run code, review its outputs, and annotate it, which makes it easy to work and collaborate with other teammates. Amazon SageMaker Studio is the first fully integrated development environment (IDE) for ML. It provides a single, web-based visual interface where you can perform all ML development steps required to build, train, tune, debug, deploy, and monitor models. It gives data scientists all the tools you need to take ML models from experimentation to production without leaving the IDE. Studio notebooks are one-click Jupyter notebooks that can be spun up quickly. The underlying compute resources are fully elastic, so you can easily dial up or down the available resources, and the changes take place automatically i...

Original Source Here 27/08/2021 Continue reading on Medium » AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.io/2021/08/31/other-optimization-method-for-neural-network-and-start-to-learn-tableau/

Original Source Here Context Continue reading on AITS Journal » AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.io/2021/08/31/water-potability-test-using-deep-neural-networks/

Original Source Here TABLE OF CONTENTS Continue reading on Medium » AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.io/2021/08/31/rain-prediction-ann/