Tag: Deep Learning

Feed: Top Blog Posts. Author: Andrea Manero-Bastin. This article was written by Joyce Xu. With the rise of autonomous vehicles, smart video surveillance, facial detection and various people counting applications, fast and accurate object detection systems are rising in demand. These systems involve not only recognizing and classifying every object in an image, but localizing each one by drawing the appropriate bounding box around it. This makes object detection a significantly harder task than its traditional computer vision predecessor, image classification. Fortunately, however, the most successful approaches to object detection are currently extensions of image classification models. A few months ... Read More

Feed: Top Blog Posts. Author: Andrea Manero-Bastin. This article was written by Joyce Xu. With the rise of autonomous vehicles, smart video surveillance, facial detection and various people counting applications, fast and accurate object detection systems are rising in demand. These systems involve not only recognizing and classifying every object in an image, but localizing each one by drawing the appropriate bounding box around it. This makes object detection a significantly harder task than its traditional computer vision predecessor, image classification. Fortunately, however, the most successful approaches to object detection are currently extensions of image classification models. A few months ... Read More

Feed: Top Blog Posts. Author: Andrea Manero-Bastin. This article was written by Alberto Roldan. I was reading two articles this week on MIT Technology Review about the difficulties of explaining the decision-making of advanced algorithms that uses AI. This explanation is fundamental as our life's become more intertwined in ways that sometimes we do not even realized. From self-driving cars, who's approved for a loan, and personalized medicine the issue of a methodology to explain the outputs of AI is becoming to the forefront due to liability issues. After 20 years implementing advanced algorithms filed and 12 years in the legal profession ... Read More

Feed: Top Blog Posts. Author: Andrea Manero-Bastin. This article was written by Alberto Roldan. I was reading two articles this week on MIT Technology Review about the difficulties of explaining the decision-making of advanced algorithms that uses AI. This explanation is fundamental as our life's become more intertwined in ways that sometimes we do not even realized. From self-driving cars, who's approved for a loan, and personalized medicine the issue of a methodology to explain the outputs of AI is becoming to the forefront due to liability issues. After 20 years implementing advanced algorithms filed and 12 years in the legal profession ... Read More

Feed: Top Blog Posts. Author: Andrea Manero-Bastin. This article was written by Joyce Xu. With the rise of autonomous vehicles, smart video surveillance, facial detection and various people counting applications, fast and accurate object detection systems are rising in demand. These systems involve not only recognizing and classifying every object in an image, but localizing each one by drawing the appropriate bounding box around it. This makes object detection a significantly harder task than its traditional computer vision predecessor, image classification. Fortunately, however, the most successful approaches to object detection are currently extensions of image classification models. A few months ... Read More

Feed: Top Blog Posts. Author: Varun Bhagat. Source-Pixabay Raise your hand, if you are stuck in confusion between Artificial Intelligence, Machine Learning and Deep Learning. Are you one of these? If yes, then you have clicked the right page. Here, you will get your answers. Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are the buzzing technologies of this digital age. These three terms not only have different spellings but also refer to other things.  However, we use these terms interchangeably; the truth is they are different but a part of the same branch, i.e. computer science. Take ... Read More

Feed: Top Blog Posts. Author: ajit jaokar. AI is a complex subject and hard to learn Often, in the early stages, people make mistake such as  a)  They try to learn everything b)  They do not know in which order to learn  c)  They go to deep into one subtopic initially Hence, I created a minimum viable learning framework for self learning AI (machine learning and deep learning) Because its concise and its minimal, it does not include topics like GANs. Reinforcement learning etc. tt also does not cover Bayesian approaches n detail However, this syllabus should get you to about ... Read More

Feed: Top Blog Posts. Author: Andrea Manero-Bastin. This article was written by Motoki Wu. Full title: How to construct prediction intervals for deep learning models using Edward and TensorFlow. Edward and Tensorflow. Source: here (Duke University) The difference between statistical modeling and machine learning gets blurry by the day. They both learn from data and predict an outcome. The main distinction seems to come from the existence of uncertainty estimates. Uncertainty estimates allow hypothesis testing, though usually at the expense of scalability.           Machine Learning = Statistical Modeling - Uncertainty + Data Ideally, we mesh the best of ... Read More

Feed: Top Blog Posts. Author: Varun Bhagat. Source-Pixabay Raise your hand, if you are stuck in confusion between Artificial Intelligence, Machine Learning and Deep Learning. Are you one of these? If yes, then you have clicked the right page. Here, you will get your answers. Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are the buzzing technologies of this digital age. These three terms not only have different spellings but also refer to other things.  However, we use these terms interchangeably; the truth is they are different but a part of the same branch, i.e. computer science. Take ... Read More

Feed: Top Blog Posts. Author: ajit jaokar. AI is a complex subject and hard to learn Often, in the early stages, people make mistake such as  a)  They try to learn everything b)  They do not know in which order to learn  c)  They go to deep into one subtopic initially Hence, I created a minimum viable learning framework for self learning AI (machine learning and deep learning) Because its concise and its minimal, it does not include topics like GANs. Reinforcement learning etc. tt also does not cover Bayesian approaches n detail However, this syllabus should get you to about ... Read More