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How to Build An Image Classifier in Few Lines of Code with Flash

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Image classification is a task where we want to predict which class belongs to an image. This task is difficult because of the image representation. If we flatten the image, it will create a long one-dimensional vector. Also, that representation will lose the neighbor information. Therefore, we need deep learning for extracting features and predict the result.


Open Source Projects for Machine Learning Enthusiasts

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Open source refers to something people can modify and share because they are accessible to everyone. You can use the work in new ways, integrate it into a larger project, or find a new work based on the original. Open source promotes the free exchange of ideas within a community to build creative and technological innovations or ideas. It helps you to write cleaner code. That can be of any choice.


Offline Data Augmentation for multiple images in Python

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Can you increase the number of images in any dataset? Machine learning, Deep learning, Artificial intelligence all require large amounts of data. However, data is not always available in every case. The programmer needs to work with the small amount of data available. Hence the use of data augmentation came into the picture.


The Modern Mathematics of Deep Learning

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We describe the new field of mathematical analysis of deep learning. This field emerged around a list of research questions that were not answered within the classical framework of learning theory. These questions concern: the outstanding generalization power of overparametrized neural networks, the role of depth in deep architectures, the apparent absence of the curse of dimensionality, the surprisingly successful optimization performance despite the non-convexity of the problem, understanding what features are learned, why deep architectures perform exceptionally well in physical problems, and which fine aspects of an architecture affect the behavior of a learning task in which way. We present an overview of modern approaches that yield partial answers to these questions. For selected approaches, we describe the main ideas in more detail.


Advanced AI eBooks Bundle by Morgan Claypool

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If the potential and possibility of artificial intelligence has always fascinated you, get ready for the perfect bundle to fill the next few weeks with! Humble Bundle teamed up with Morgan & Claypool to bring you insights into AI and its applications into autonomous vehicles, conversational systems, and more! Pick up this bundle and you'll enjoy discovering eBooks like Why AI/Data Science Projects Fail: How to Avoid Project Pitfalls, Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale Production, and Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots. Your purchase of this bundle helps support a charity of your choice. This bundle launched on June 14 at 11:00 am PST and lasts through July 05, 2021.


A Short Discussion on Bias in Machine Learning

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In the last decade, advances in data science and engineering have made possible the development of various data products across industry. Problems that not so long ago were treated as very difficult for machines to tackle are now solved (to some extent) and available at large scale capacities. These include many perceptual-like tasks in computer vision, speech recognition, and natural language processing (NLP). Nowadays, we can contract large-scale deep learning-based vision systems that can recognize and verify faces on images and videos. In the same way, we can take advantage of large-scaled language models to build conversational bots, analyze large bodies of text to find common patterns, or use translation systems that can work on nearly any modern language.


Detection of ripe flowers of the Alstroemeria genus Morado

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The authors of this blog are Stan Zwinkels & Ted de Vries Lentsch. This blog aims to present our attempt to create a detection algorithm for detecting ripe flowers of the Alstroemeria genus Morado. Throughout this blog, we explain our process to create a dataset and detection model that achieves an F1 score of more than 0.75. This blog is part of the course Seminar Computer Vision By Deep Learning (CS4245) 2021 from the Delft University of Technology. Creating the dataset has been carried out in collaboration with the company Hoogenboom Alstroemeria.


Everything You Need to Know About Google BERT

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We have published 7k books, videos, articles, and tutorials. If you've been following developments in deep learning and natural language processing (NLP) over the past few years then you've probably heard of something called BERT; and if you haven't, just know that techniques owing something to BERT will likely play an increasing part in all our digital lives. BERT is a state-of-the-art embedding model published by Google, and it represents a breakthrough in the field of NLP by providing excellent results on many NLP tasks, including question answering, text generation, sentence classification, and more. Here we are going to look at what BERT is and see what is distinctive about it, by looking in a relatively high-level way (eschewing the underlying linear algebra) at the internal workings of the BERT model. By the end you should have if not a detailed understanding then at least a strong sense of what underpins this modern approach to NLP and other methods like it.


Vision Guided Robotics & Artificial Intelligence: An Explanation for the Non-Technical

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The automation industry is experiencing an explosion of growth and technology capability. To explain complex technology, we use terms such as "artificial intelligence" to convey the idea that solutions are more capable and advanced than ever before. If you are an investor, business leader, or technology user who seeks to understand the technologies you are investing in, this article is for you. What follows is an explanation of vision-guided robotics and deep-learning algorithms. That's right, the article is titled "artificial intelligence" and yet by the end of the first paragraph, we've already switched to deep-learning algorithms!


Recreating an ML Master's degree with Online Courses

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A Bachelor's study usually takes six semesters; a Master's study takes four. But this is only an outline. I've witnessed people doing their BA in three semesters and some taking nine semesters. Sometimes there are so many exciting courses that you voluntarily stay longer to learn it all. Therefore, I've loosely structured the recreated curriculum into four semesters.