- Misconceptions About Semantic Segmentation Annotation - Jan 6, 2022.
Semantic segmentation is a computer vision problem that entails putting related elements of an image into the same class. Read on to discover more, including the difficulties associated with annotation.
- Where NLP is heading - Nov 18, 2021.
Natural language processing research and applications are moving forward rapidly. Several trends have emerged on this progress, and point to a future of more exciting possibilities and interesting opportunities in the field.
- 10 AI Project Ideas in Computer Vision - Nov 16, 2021.
The field of computer vision has seen the development of very powerful applications leveraging machine learning. These projects will introduce you to these techniques and guide you to more advanced practice to gain a deeper appreciation for the sophistication now available.
- KDnuggets™ News 21:n40, Oct 20: The 20 Python Packages You Need For Machine Learning and Data Science; Ace Data Science Interviews with Portfolio Projects - Oct 20, 2021.
The 20 Python Packages You Need For Machine Learning and Data Science; How to Ace Data Science Interview by Working on Portfolio Projects; Deploying Your First Machine Learning API; Real Time Image Segmentation Using 5 Lines of Code; What is Clustering and How Does it Work?
- Real Time Image Segmentation Using 5 Lines of Code - Oct 18, 2021.
PixelLib Library is a library created to allow easy integration of object segmentation in images and videos using few lines of python code. PixelLib now provides support for PyTorch backend to perform faster, more accurate segmentation and extraction of objects in images and videos using PointRend segmentation architecture.
- How our Obsession with Algorithms Broke Computer Vision: And how Synthetic Computer Vision can fix it - Oct 15, 2021.
Deep Learning radically improved Machine Learning as a whole. The Data-Centric revolution is about to do the same. In this post, we’ll take a look at the pitfalls of mainstream Computer Vision (CV) and discuss why Synthetic Computer Vision (SCV) is the future.
- Computer Vision in Agriculture - Sep 27, 2021.
Deep learning isn’t just for placing ads or identifying cats anymore. Instead, a slew of young startups have started to incorporate the advances in computer vision made possible through larger and larger neural networks to real working robots in the fields.
- 3 Data Acquisition, Annotation, and Augmentation Tools - Aug 27, 2021.
Check out these 3 projects found around GitHub that can help with your data acquisition, annotation, and augmentation tasks.
- KDnuggets™ News 21:n32, Aug 25: Open Source Datasets for Computer Vision; Django’s 9 Most Common Applications - Aug 25, 2021.
Open Source Datasets for Computer Vision; Django’s 9 Most Common Applications; How to Select an Initial Model for your Data Science Problem; Automate Microsoft Excel and Word Using Python; Stack Overflow Survey Data Science Highlights
- Open Source Datasets for Computer Vision - Aug 18, 2021.
Access to high-quality, noise-free, large-scale datasets is crucial for training complex deep neural network models for computer vision applications. Many open-source datasets are developed for use in image classification, pose estimation, image captioning, autonomous driving, and object segmentation. These datasets must be paired with the appropriate hardware and benchmarking strategies to optimize performance.
- Machine Learning Skills – Update Yours This Summer - Jul 27, 2021.
The process of mastering new knowledge often requires multiple passes to ensure the information is deeply understood. If you already began your journey into machine learning and data science, then you are likely ready for a refresher on topics you previously covered. This eight-week self-learning path will help you recapture the foundations and prepare you for future success in applying these skills.
- Major changes: Where Analytics, Data Science, Machine Learning were applied in 2020/21 - Jun 18, 2021.
Our latest poll shows major change in where AI, Data Science, Machine Learning are being applied, with decline in interest in traditional fields like CRM/Consumer Analytics, and growth in applications to Computer Vision, COVID, Agriculture, and Education.
- The Essential Guide to Transformers, the Key to Modern SOTA AI - Jun 10, 2021.
You likely know Transformers from their recent spate of success stories in natural language processing, computer vision, and other areas of artificial intelligence, but are familiar with all of the X-formers? More importantly, do you know the differences, and why you might use one over another?
- 6 Mistakes To Avoid While Training Your Machine Learning Model - Apr 15, 2021.
While training the AI model, multi-stage activities are performed to utilize the training data in the best manner, so that outcomes are satisfying. So, here are the 6 common mistakes you need to understand to make sure your AI model is successful.
- Extraction of Objects In Images and Videos Using 5 Lines of Code - Mar 25, 2021.
PixelLib is a library created for easy integration of image and video segmentation in real life applications. Learn to use PixelLib to extract objects In images and videos with minimal code.
- A Beginner’s Guide to the CLIP Model - Mar 11, 2021.
CLIP is a bridge between computer vision and natural language processing. I'm here to break CLIP down for you in an accessible and fun read! In this post, I'll cover what CLIP is, how CLIP works, and why CLIP is cool.
- Evaluating Object Detection Models Using Mean Average Precision - Mar 3, 2021.
In this article we will see see how precision and recall are used to calculate the Mean Average Precision (mAP).
- Deep Learning-based Real-time Video Processing - Feb 17, 2021.
In this article, we explore how to build a pipeline and process real-time video with Deep Learning to apply this approach to business use cases overviewed in our research.
- KDnuggets™ News 21:n02, Jan 13: Best Python IDEs and Code Editors; 10 Underappreciated Python Packages for Machine Learning Practitioners - Jan 13, 2021.
Best Python IDEs and Code Editors You Should Know; 10 Underappreciated Python Packages for Machine Learning Practitioners; Top 10 Computer Vision Papers 2020; CatalyzeX: A must-have browser extension for machine learning engineers and researchers
- OpenAI Releases Two Transformer Models that Magically Link Language and Computer Vision - Jan 11, 2021.
OpenAI has released two new transformer architectures that combine image and language tasks in an fun and almost magical way. Read more about them here.
- Top 10 Computer Vision Papers 2020 - Jan 8, 2021.
The top 10 computer vision papers in 2020 with video demos, articles, code, and paper reference.
- Change the Background of Any Video with 5 Lines of Code - Dec 7, 2020.
Learn to blur, color, grayscale and create a virtual background for a video with PixelLib.
- Computer Vision at Scale With Dask And PyTorch - Nov 23, 2020.
A tutorial on conducting image classification inference using the Resnet50 deep learning model at scale with using GPU clusters on Saturn Cloud. The results were: 40x faster computer vision that made a 3+ hour PyTorch model run in just 5 minutes.
- Adversarial Examples in Deep Learning – A Primer - Nov 20, 2020.
Bigger compute has led to increasingly impressive deep learning computer vision model SOTA results. However most of these SOTA deep learning models are brought down to their knees when making predictions on adversarial images. Read on to find out more.
- Top Python Libraries for Deep Learning, Natural Language Processing & Computer Vision - Nov 16, 2020.
This article compiles the 30 top Python libraries for deep learning, natural language processing & computer vision, as best determined by KDnuggets staff.
- How to Acquire the Most Wanted Data Science Skills - Nov 13, 2020.
We recently surveyed KDnuggets readers to determine the "most wanted" data science skills. Since they seem to be those most in demand from practitioners, here is a collection of resources for getting started with this learning.
- Change the Background of Any Image with 5 Lines of Code - Nov 9, 2020.
Blur, color, grayscale and change the background of any image with a picture using PixelLib.
- KDnuggets™ News 20:n41, Oct 28: Difference Between Junior and Senior Data Scientists; Ain’t No Such a Thing as a Citizen Data Scientist - Oct 28, 2020.
The unspoken difference between junior and senior data scientists; Ain't No Such a Thing as a Citizen Data Scientist; How to become a Data Scientist: a step-by-step guide; Good-bye Big Data. Hello, Massive Data!; DeepMind Relies on this Old Statistical Method to Build Fair Machine Learning Models
- Roadmap to Computer Vision - Oct 26, 2020.
Read this introduction to the main steps which compose a computer vision system, starting from how images are pre-processed, features extracted and predictions are made.
- Deep Learning for Virtual Try On Clothes – Challenges and Opportunities - Oct 16, 2020.
Learn about the experiments by MobiDev for transferring 2D clothing items onto the image of a person. As part of their efforts to bring AR and AI technologies into virtual fitting room development, they review the deep learning algorithms and architecture under development and the current state of results.
- A Guide to Preparing OpenCV for Android - Oct 6, 2020.
This tutorial guides Android developers in preparing the popular library OpenCV for use. Using a step-by-step guide, the library will be imported into Android Studio and then can be used for performing any of the operations it supports, such as object detection, segmentation, tracking, and more.
- NIST $240K Challenge: Saving Lives, One Pixel at a Time - Sep 8, 2020.
Video analytics that could save lives and property are just out of reach. A new prize challenge, Enhancing Computer Vision for Public Safety, is designed to help develop a new line of research that will bring such tools closer to reality.
- Computer Vision Recipes: Best Practices and Examples - Sep 2, 2020.
This is an overview of a great computer vision resource from Microsoft, which demonstrates best practices and implementation guidelines for a variety of tasks and scenarios.
- Accelerated Computer Vision: A Free Course From Amazon - Aug 31, 2020.
Amazon's Machine Learning University is making its online courses available to the public, and this time we look at its Accelerated Computer Vision offering.
- 3D Human Pose Estimation Experiments and Analysis - Aug 17, 2020.
In this article, we explore how 3D human pose estimation works based on our research and experiments, which were part of the analysis of applying human pose estimation in AI fitness coach applications.
- Are Computer Vision Models Vulnerable to Weight Poisoning Attacks? - Aug 17, 2020.
A recent paper has explored the possibility of influencing the predictions of a freshly trained Natural Language Processing (NLP) model by tweaking the weights re-used in its training. his result is especially interesting if it proves to transfer also to the context of Computer Vision (CV) since there, the usage of pre-trained weights is widespread.
- Metrics to Use to Evaluate Deep Learning Object Detectors - Aug 6, 2020.
It's important to understand which metric should be used to evaluate trained object detectors and which one is more important. Is mAP alone enough to evaluate the objector models? Can the same metric be used to evaluate object detectors on validation set and test set?
- Scaling Computer Vision Models with Dataflow - Jul 31, 2020.
Scaling Machine Learning models is hard and expensive. We will shortly introduce the Google Cloud service Dataflow, and how it can be used to run predictions on millions of images in a serverless way.
- Auto Rotate Images Using Deep Learning - Jul 14, 2020.
Follow these 5 simple steps to auto rotate images and get the right angle in human photos using computer vision.
- KDnuggets™ News 20:n25, Jun 24: PyTorch Fundamentals You Should Know; Free Math Courses to Boost Your Data Science Skills - Jun 24, 2020.
A Classification Project in Machine Learning: a gentle step-by-step guide; Crop Disease Detection Using Machine Learning and Computer Vision; Bias in AI: A Primer; Machine Learning in Dask; How to Deal with Missing Values in Your Dataset
- Bias in AI: A Primer - Jun 23, 2020.
Those interested in studying AI bias, but who lack a starting point, would do well to check out this introductory set of slides and the accompanying talk on the subject from Google researcher Margaret Mitchell.
- 6 Easy Steps to Implement a Computer Vision Application Using Tensorflow.js - Jun 18, 2020.
In this article, we are going to see how we can implement computer vision applications using tensorflow.js models.
- Crop Disease Detection Using Machine Learning and Computer Vision - Jun 17, 2020.
Computer vision has tremendous promise for improving crop monitoring at scale. We present our learnings from building such models for detecting stem and wheat rust in crops.
- Fighting Coronavirus With AI: Improving Testing with Deep Learning and Computer Vision - Apr 22, 2020.
This post will cover how testing is done for the coronavirus, why it's important in battling the pandemic, and how deep learning tools for medical imaging can help us improve the quality of COVID-19 testing.
- Image Recognition For Building Your Perfect Store - Mar 3, 2020.
In this blog, we outline what a perfect store strategy is, and how to achieve it.
- Using AI to Identify Wildlife in Camera Trap Images from the Serengeti - Feb 17, 2020.
With recent developments in machine learning and computer vision, we acquired the tools to provide the biodiversity community with an ability to tap the potential of the knowledge generated automatically with systems triggered by a combination of heat and motion.
- Create Your Own Computer Vision Sandbox - Feb 5, 2020.
This post covers a wide array of computer vision tasks, from automated data collection to CNN model building.
- The Future of Machine Learning - Jan 17, 2020.
This summary overviews the keynote at TensorFlow World by Jeff Dean, Head of AI at Google, that considered the advancements of computer vision and language models and predicted the direction machine learning model building should follow for the future.
- How to Convert a Picture to Numbers - Jan 6, 2020.
Reducing images to numbers makes them amenable to computation. Let's take a look at the why and the how using Python and Numpy.
- How to Convert an RGB Image to Grayscale - Dec 18, 2019.
This post is about working with a mixture of color and grayscale images and needing to transform them into a uniform format - all grayscale. We'll be working in Python using the Pillow, Numpy, and Matplotlib packages.
- Pedestrian Detection Using Non Maximum Suppression Algorithm - Dec 17, 2019.
Read this overview of a complete pipeline for detecting pedestrians on the road.
- 10 Free Top Notch Machine Learning Courses - Dec 6, 2019.
Are you interested in studying machine learning over the holidays? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to improving your machine learning skills.
- Google Open Sources MobileNetV3 with New Ideas to Improve Mobile Computer Vision Models - Dec 2, 2019.
The latest release of MobileNets incorporates AutoML and other novel ideas in mobile deep learning.
- Practical Computer Vision Course with Real-Life Cases, Nov 18, Washington, DC - Nov 4, 2019.
This course, Practical Computer Vision Course with Real-Life Cases, Nov 18 in Washington, DC, will move you on the next step, providing you with practical means of solving business-specific tasks.Reserve your seat now.
- Research Guide for Video Frame Interpolation with Deep Learning - Oct 15, 2019.
In this research guide, we’ll look at deep learning papers aimed at synthesizing video frames within an existing video.
- A 2019 Guide to Human Pose Estimation - Aug 28, 2019.
Human pose estimation refers to the process of inferring poses in an image. Essentially, it entails predicting the positions of a person’s joints in an image or video. This problem is also sometimes referred to as the localization of human joints.
- Monash University: Research Fellow – Computer Vision [Melbourne, Australia] - Aug 9, 2019.
The position requires a passion for research, a proven research track record in computer vision, an ability to work independently as well as lead a team, and a willingness to work on inter-disciplinary research projects and seek external funding. The successful candidate will align with the group goal on building a world-class computer vision team.
- Introduction to Image Segmentation with K-Means clustering - Aug 9, 2019.
Image segmentation is the classification of an image into different groups. Many kinds of research have been done in the area of image segmentation using clustering. In this article, we will explore using the K-Means clustering algorithm to read an image and cluster different regions of the image.
- A 2019 Guide to Object Detection - Aug 1, 2019.
Object detection has been applied widely in video surveillance, self-driving cars, and object/people tracking. In this piece, we’ll look at the basics of object detection and review some of the most commonly-used algorithms and a few brand new approaches, as well.
- Computer Vision for Beginners: Part 1 - Jul 17, 2019.
Image processing is performing some operations on images to get an intended manipulation. Think about what we do when we start a new data analysis. We do some data preprocessing and feature engineering. It’s the same with image processing.
- Build your own AutoML computer vision pipeline, July 16 webinar - Jul 2, 2019.
This webinar will present a step-by-step use case so you can build your own AutoML computer vision pipelines, and will go through the essentials for research, deployment and training using Keras, PyTorch and TensorFlow.
- An Overview of Human Pose Estimation with Deep Learning - Jun 28, 2019.
Human Pose Estimation is one of the main research areas in computer vision. The reason for its importance is the abundance of applications that can benefit from such a technology. Here's an introduction to the different techniques used in Human Pose Estimation based on Deep Learning.
- NLP and Computer Vision Integrated - Jun 5, 2019.
Computer vision and NLP developed as separate fields, and researchers are now combining these tasks to solve long-standing problems across multiple disciplines.
- Fixing a Major Weakness in Machine Learning of Images with Hinton’s Capsule Networks - May 22, 2019.
We explore Geoffrey Hinton's capsule networks to deal with rotational variance in images.
- KDnuggets™ News 19:n20, May 22: 7 Steps to Mastering SQL for Data Science; How to build Math Programming Skills - May 22, 2019.
Also An overview of Pycharm for Data Scientists; How to build a Computer Vision model - key approaches and datasets; k-means clustering tutorial; 60+ useful graph visualization libraries; The Data Fabric for Machine Learning.
- Building a Computer Vision Model: Approaches and datasets - May 20, 2019.
How can we build a computer vision model using CNNs? What are existing datasets? And what are approaches to train the model? This article provides an answer to these essential questions when trying to understand the most important concepts of computer vision.
- Think Like an Amateur, Do As an Expert: Lessons from a Career in Computer Vision - May 17, 2019.
Dr. Takeo Kanade shared his life lessons from an illustrious 50-year career in Computer Vision at last year's Embedded Vision Summit. You have a chance to attend the 2019 Embedded Vision Summit, from May 20-23, in the Santa Clara Convention Center, Santa Clara CA.
- Predict Age and Gender Using Convolutional Neural Network and OpenCV - Apr 4, 2019.
Age and gender estimation from a single face image are important tasks in intelligent applications. As such, let's build a simple age and gender detection model in this detailed article.
- Pedestrian Detection in Aerial Images Using RetinaNet - Mar 26, 2019.
Object Detection in Aerial Images is a challenging and interesting problem. By using Keras to train a RetinaNet model for object detection in aerial images, we can use it to extract valuable information.
- Object Detection with Luminoth - Mar 13, 2019.
In this article you will learn about Luminoth, an open source computer vision library which sits atop Sonnet and TensorFlow and provides object detection for images and video.
- KDnuggets™ News 19:n10, Mar 6: What no one will tell you about data science job applications; The rise of ML Engineering - Mar 6, 2019.
Also most impactful AI trends of 2018: The rise of ML Engineering; How to do Everything in Computer Vision; GANs Need Some Attention, Too; OpenAI GPT-2.
- Comparing MobileNet Models in TensorFlow - Mar 1, 2019.
MobileNets are a family of mobile-first computer vision models for TensorFlow, designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application.
- How to do Everything in Computer Vision - Feb 27, 2019.
The many standard tasks in computer vision all require special consideration: classification, detection, segmentation, pose estimation, enhancement and restoration, and action recognition. Let me show you how to do everything in Computer Vision with Deep Learning!
- Building an image search service from scratch - Jan 30, 2019.
By the end of this post, you should be able to build a quick semantic search model from scratch, no matter the size of your dataset.
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- Implementing ResNet with MXNET Gluon and Comet.ml for Image Classification - Dec 14, 2018.
Whether MXNet is an entirely new framework for you or you have used the MXNet backend while training your Keras models, this tutorial illustrates how to build an image recognition model with an MXNet resnet_v1 model.
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- Solve any Image Classification Problem Quickly and Easily - Dec 13, 2018.
This article teaches you how to use transfer learning to solve image classification problems. A practical example using Keras and its pre-trained models is given for demonstration purposes.
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- Latest Trends in Computer Vision Technology and Applications - Nov 7, 2018.
We investigate the advancements in deep learning, the rise of edge computing, object recognition with point cloud, VR and AR enhanced merged reality, semantic instance segmentation and more.
- Building Surveillance System Using USB Camera and Wireless-Connected Raspberry Pi - Nov 6, 2018.
Read this post to learn how to build a surveillance system using a USB camera plugged into Raspberry Pi (RPi) which is connected a PC using its wireless interface.
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- Basic Image Data Analysis Using Python – Part 4 - Oct 5, 2018.
Accessing the internal component of digital images using Python packages helps the user understand its properties, as well as its nature.
- KDnuggets™ News 18:n37, Oct 3: Mathematics of Machine Learning; Effective Transfer Learning for NLP; Path Analysis with R - Oct 3, 2018.
Also: Introducing VisualData: A Search Engine for Computer Vision Datasets; Raspberry Pi IoT Projects for Fun and Profit; Recent Advances for a Better Understanding of Deep Learning; Basic Image Data Analysis Using Python - Part 3; Introduction to Deep Learning
- Basic Image Data Analysis Using Python – Part 3 - Sep 28, 2018.
Accessing the internal component of digital images using Python packages becomes more convenient to help understand its properties, as well as nature.
- Introducing VisualData: A Search Engine for Computer Vision Datasets - Sep 26, 2018.
Instead of building your own dataset, there already exists a rich collection of computer vision datasets contributed by academic researchers, hobbyists and companies.
- Basic Image Processing in Python, Part 2 - Jul 17, 2018.
We explain how to easily access and manipulate the internal components of digital images using Python and give examples from satellite image processing.
- Basic Image Data Analysis Using Numpy and OpenCV – Part 1 - Jul 10, 2018.
Accessing the internal component of digital images using Python packages becomes more convenient to understand its properties as well as nature.
- DIY Deep Learning Projects - Jun 8, 2018.
Inspired by the great work of Akshay Bahadur in this article you will see some projects applying Computer Vision and Deep Learning, with implementations and details so you can reproduce them on your computer.
- How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1 - May 17, 2018.
The best way to go about learning object detection is to implement the algorithms by yourself, from scratch. This is exactly what we'll do in this tutorial.
- KDnuggets™ News 18:n20, May 16: PyTorch Tensor Basics; Data Science in Finance; Executive Guide to Data Science - May 16, 2018.
PyTorch Tensor Basics; Top 7 Data Science Use Cases in Finance; The Executive Guide to Data Science and Machine Learning; Data Augmentation: How to use Deep Learning when you have Limited Data
- NVIDIA: Director – Computer Vision/Deep Learning for Imaging Software - Jan 4, 2018.
Nvidia is seeking an experienced engineering leader to direct our Deep Learning and Computer Vision efforts in Imaging software.
- Top KDnuggets tweets, Dec 20-26: Harvard CS109 #DataScience Course Resources; Computer Vision by Andrew Ng: Lessons Learned - Dec 27, 2017.
Also: Ten years in, nobody has come up with a use for #blockchain - here is what happened; Can I Become a #DataScientist: Research into 1,001 #DataScience Profiles.
- Computer Vision by Andrew Ng - 11 Lessons Learned - Dec 22, 2017.
I recently completed Andrew Ng’s computer vision course on Coursera. In this article, I will discuss 11 key lessons that I learned in the course.
- Real World Deep Learning: Neural Networks for Smart Crops - Nov 7, 2017.
The advances in image classification, object detection, and semantic segmentation using deep Convolutional Neural Networks, which spawned the availability of open source tools such as Caffe and TensorFlow (to name a couple) to easily manipulate neural network graphs... made a very strong case in favor of CNNs for our classifier.
- Top KDnuggets tweets, Oct 18-24: Chihuahua or muffin? The #DataScience Project Playbook - Oct 25, 2017.
Chihuahua or muffin? My search for the best computer vision API; Could #AI Be the Future of #FakeNews and Product Reviews? 7 Types of Artificial #NeuralNetworks for NLP.
- Key Trends and Takeaways from RE•WORK Deep Learning Summit Montreal – Part 1: Computer Vision - Oct 16, 2017.
Read up on what you missed from the RE•WORK Deep Learning Summit Montreal, held October 10 & 11, including talks from Aaron Courville, Ira Kemelmacher-Shlizerman, Roland Memisevic, and Raquel Urtasun.
- How SnotBots, Surveys and NASA are saving our oceans - Sep 14, 2017.
There are many projects using computer vision systems, machine learning and large data sets to hopefully make a difference to our oceans and gain the knowledge to have a real impact on future sustainability.
- Object Detection: An Overview in the Age of Deep Learning - Sep 13, 2017.
Like many other computer vision problems, there still isn’t an obvious or even “best” way to approach the problem of object recognition, meaning there’s still much room for improvement.
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- Road Lane Line Detection using Computer Vision models - Jul 19, 2017.
A tutorial on how to implement a computer vision data pipeline for road lane detection used by self-driving cars.
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- Sturfee: Deep Learning Engineer - Jul 10, 2017.
You will be working on some amazing vision technologies for satellite and street view fusion that will become the cornerstone of future digital applications.
- Top 10 Machine Learning Videos on YouTube, updated - May 3, 2017.
The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.
- Top KDnuggets tweets, Feb 15-21: curated list of top #DeepLearning papers; Hill for the #DataScientist: An xkcd Story - Feb 22, 2017.
Sir Austin Bradford Hill for the #DataScientist: An xkcd Story; Attacking #machinelearning with adversarial examples; Hans Rosling: An Appreciation - Great Data Scientist, Great Human #RIP; The Most Popular Language For #MachineLearning and #DataScience Is ...
- FeatureX: Software Engineer - Feb 10, 2017.
Seeking a software engineer, responsible for the design and development of machine learning and computer vision platforms and data systems.
- FeatureX: Computer Vision Research Scientist - Feb 10, 2017.
Seeking computer vision research scientists who can apply and develop computer vision algorithms to extract information from high-resolution satellite images and other remote sensing data.
- Introduction to Trainspotting: Computer Vision, Caltrain, and Predictive Analytics - Nov 1, 2016.
We previously analyzed delays using Caltrain’s real-time API to improve arrival predictions, and we have modeled the sounds of passing trains to tell them apart. In this post we’ll start looking at the nuts and bolts of making our Caltrain work possible.
- Predicting Future Human Behavior with Deep Learning - Sep 30, 2016.
Carl Vondrick, MIT researcher, who studies computer vision and machine learning, discusses how to use Big Data with minimal annotations and applications to predictive vision and scene understanding.
- 7 Steps to Understanding Computer Vision - Aug 9, 2016.
A starting point for Computer Vision and how to get going deeper. Dive into this post for some overview of the right resources and a little bit of advice.
- Trulia (Zillow Group): Data Scientist – Computer Vision & Deep Learning - Aug 7, 2016.
Become one of the founding members of computer vision/deep learning group at Trulia and develop solutions that would be used by millions of users across Zillow Group.
- 5 More Machine Learning Projects You Can No Longer Overlook - Jun 28, 2016.
There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects.
- Microsoft is Becoming M(ai)crosoft - Apr 25, 2016.
This post is an overview and discussion of Microsoft's increasing investment in, and approach to, artificial intelligence, and how their philosophy differs from their competitors.
- Top /r/MachineLearning Posts, March: Hugs, Deep Learning Navigation, 3D Face Capture, AlphaGo! - Apr 4, 2016.
What's huggable, adversarial images for deep learning, overview of real-time 3D face capture and reenactment, deep learning quadcopter navigation, and a whole lot of AlphaGo!
- The Top A.I. Breakthroughs of 2015 - Feb 2, 2016.
Learn about the biggest developments of 2015 in the field of Artificial Intelligence.
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- 20+ hottest research papers on Computer Vision, Machine Learning - Jan 15, 2016.
December's ICCV 2015 conference in Santiago, Chile has come and gone, but that's no reason not to know about its top papers. Get an update on which computer vision papers and researchers won awards.
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- Top 5 Deep Learning Resources, January - Jan 7, 2016.
There is an increasing volume of deep learning research, articles, blog posts, and news constantly emerging. Our Deep Learning Reading List aims to make this information easier to digest.
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- Top 10 Machine Learning Videos on YouTube - Jun 23, 2015.
The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.
- Inside Deep Learning: Computer Vision With Convolutional Neural Networks - Apr 9, 2015.
Deep Learning-powered image recognition is now performing better than human vision on many tasks. We examine how human and computer vision extracts features from raw pixels, and explain how deep convolutional neural networks work so well.
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