How to do everything in Computer Vision

Want to do Computer Vision? Deep Learning is the way to go these days. Large scale datasets plus the representational power of deep Convolutional Neural Networks (CNNs) make for super accurate and robust models. Only one challenge still remains: how to design your model. With a field as broad and complex as computer vision, the solution isn’t always clear. The many standard tasks in computer vision all require special consideration: classification, detection, segmentation, pose estimation, enhancement and restoration, and action recognition. Although the state-of-the-art networks used for each of them exhibit common patterns, they’ll all still need their own unique design twist. So how can we build models for all of those different tasks? Let me show you how to do everything in Computer Vision with Deep Learning!

Acquiring Labeled Data to Train Your Models at Low Costs

An untrained statistical model is like a Ferrari that simply will not run. In other words, it is simply not of much use. Supervised Learning is based on the availability of high quality labeled data. Labeled data is the ingredient that will make your Ferrari ( statistical model) roar. To put it in technical terms, labeling your training data gives your model the ability to correctly predict, classify and otherwise analyze data to generate meaningful output. Rule of thumb, it’s best not to develop a model if you haven’t figured out how to first acquire and then, more importantly, label(‘tag’ or ‘annotate’) a suitable training data set. Labeling is a tedious and time-consuming affair, isn’t it? Check out these ingenious methods that you can use to get your data set labeled without breaking the bank.

Using Order By Keyword in SQL

In this tutorial, you will learn how to use the order by keyword in SQL.

Training Your First Classifier with Spark and Scala

Many people begin their machine learning journey using Python and Sklearn. If you want to work with big data you have to use Apache Spark. It is possible to work with Spark in Python using Pyspark. However, since Spark is written in Scala, you will see much better performance by using Scala. There are numerous tutorials out there about how to get up and running with Spark on your computer so I won’t go into that. I will only suggest that two ways to get started quickly are to use a docker image, or the community version of Databricks. Let’s get started!

Surprising Findings in Document Classification

Document Classification: The task of assigning labels to large bodies of text. In this case the task is to classify BBC news articles to one of five different labels, such as sport or tech. The data set used wasn’t ideally suited for deep learning, having only low thousands of examples, but this is far from an unrealistic case outside large firms.

Reinforcement Learning Tutorial Part 3: Basic Deep Q-Learning

In this third part, we will move our Q-learning approach from a Q-table to a deep neural net. With Q-table, your memory requirement is an array of states x actions. For the state-space of 5 and action-space of 2, the total memory consumption is 2 x 5=10. But just the state-space of chess is around 10¹²?, which means this strict spreadsheet approach will not scale to the real world. Luckily you can steal a trick from the world of media compression: Trade some accuracy for memory.

Speaker Diarization with Kaldi

With the rise of voice biometrics and speech recognition systems, the ability to process audio of multiple speakers is crucial. This article is a basic tutorial for that process with Kaldi X-Vectors, a state-of-the-art technique. In most real-world scenarios speech does not come in well defined audio segments with only one speaker. In most of the conversations that our algorithms will need to work with, people will interrupt each other and cutting the audio between sentences won’t be a trivial task. In addition to that, in many applications we will want to identify multiple speakers in a conversation, for example when writing a protocol of a meeting. For such occasions, identifying the different speakers and connect different sentences under the same speaker is a critical task. Speaker Diarization is the solution for those problems. With this process we can divide an input audio into segments according to the speaker’s identity. It can be described as the question ‘who spoke when?’ in an audio segment.

How to make your model awesome with Optuna

Hyperparameter optimization is one of the crucial steps in training Machine Learning models. With many parameters to optimize, long training time and multiple folds to limit information leak, it may be a cumbersome endeavor. There are a few methods of dealing with the issue: grid search, random search, and Bayesian methods. Optuna is an implementation of the latter one.

One neural network, many uses

It’s common knowledge that neural networks are really good at one narrow task, but they fail at handling multiple tasks. This is unlike the human brain which is able to use the same concepts at amazingly diverse tasks. For example, if you have never seen a fractal before and I show you one right now.

Intro to Multiple Inheritance & super()

A Pythonista’s introductory guide to multiple inheritance, the super() function, & how to navigate the diamond problem.

TensorFlow.js: machine learning for the web and beyond

If machine learning and ML models are to pervade all of our applications and systems, then they’d better go to where the applications are rather than the other way round. Increasingly, that means JavaScript – both in the browser and on the server. TensorFlow.js brings TensorFlow and Keras to the the JavaScript ecosystem, supporting both Node.js and browser-based applications. As well as programmer accessibility and ease of integration, running on-device means that in many cases user data never has to leave the device.

Adversarial Training: Creating Real Pictures of Fake People With Machine Learning

GANs use a clever training method to do this. They are made of two competing neural networks. A generator network, and a discriminator network. The generator comes up with pictures, and the discriminator is then given real training images (in this case a bunch of faces) as well as the images generated by the generator and tries to determine which ones are real and which ones were made by the generator.

GKS and Data Visualization In Data Science

The Graphical Kernel System (GKS) is a document produced by the International Standards Organization (ISO) which defines a common interface to interactive computer graphics for application programs. GKS has been designed by a group of experts representing the national standards institutions of most major industrialized countries. The full standard provides functional specifications for some 200 subroutines which perform graphics input and output in a device independent way. Application programs can thus move freely between different graphics devices and different host computers. For the first time graphics programs have become genuinely portable.

How To Use Artificial Intelligence In Blockchain Technology

Artificial intelligence has been fascinating to the human imagination since the term was first used by the first science fiction writers. The roots of the concept of ‘artificial intelligence’ must be sought deep in the ancient world, where folklore, legends and myths in almost every culture spoke of artificially created creatures endowed with supernatural intelligence, consciousness or other human qualities. The only factor uniting the myths of the whole world is that artificial intelligence was always created by a man so passionate about his work that his brainchild went beyond the boundaries of matter and turned into life, sometimes surpassing man.

The Role of Artificial Intelligence in Manufacturing: 15 High Impact AI Use Cases

Use Case 1: Real-Time Alert of Wear, Tear, Fault, or Breakdown – Warning signals of potential breakdown by AI, it could even look ahead for fatigue
Use Case 2: Lifetime Prediction: Using AI to accurately predict Time to Live for Assets like Machinery improving overall life of machinery and assets
Use Case 3: AI to enable more informed asset maintenance schedule triggering a focused repair and MRO schedule optimizing overall effort, cost, and quality across assets.
Use Case 4: Enhanced effectiveness of robots in form of powerful software to enable robots to take on complex tasks. Not just complexity but also the versatility of tasks enhanced by AI
Use Case 5: Role of AI in better human-robot interaction to enable more effective utilization of robots is key. Cobots are emerging as potential enablers in this area.
Use Case 6: Real-time tracking of supply vehicles helps in better utilization of logistics fleet thereby optimizing overall production schedule
Use case 7: Better data-driven AI-based approach to analyzing inventory and thereby using it to lower inventory costs can be a great cost saver for manufacturers.
Use case 8: Shipping and Delivery Lead Time can not only be accurately predicted, but it is also optimized via application of AI algorithms
Use Case 9. Use of AI-based generative design is being used by large design houses like auto manufacturers. airplane manufacturers etc enabling creative machine or part or asset designs not limited by human designers.
Use Case 10. Quality process improvement. AI can enable understand limitations, shortcomings, or deficiencies of current as manufacturing quality processes and using AI applied on quality data several improvement opportunities can be harnessed.
Use Case 11: Using complex AI like computer vision to explore defects in produced items can be a great way to ensure product quality.
Use Case 12: Idea of such a digital twin is to understand and simulate how the process flows occur and identify what if scenarios via AI. AI thus enables the realization of potential implications of the process
Use Case 13: Exception Management: In conventional workflows, exceptions are usually routed to humans to take care of the same. In an AI wired process such processes could be automated and straight through actions could be taken by programs rather than humans
Use Case 14: Testing of design and manufacturing feasibility of items can be carried out intelligent simulations.
Use Case 15: Understanding customers closely and designing, manufacturing and testing products with a high level of customization. This leads to change of models of design and manufacturing also to include flexible ways of catering to all diverse products. Example of BTO models falls in this.

Rapidly Build and Run Apache Spark Applications in the Cloud with StreamAnalytix on AWS Marketplace

StreamAnalytix is an Apache Spark based big data analytics and machine learning platform. It offers an intuitive visual development environment to rapidly build and operationalize batch + streaming applications, across industries, data formats, and use cases.

Comparing MobileNet Models in TensorFlow

In recent years, neural networks and deep learning have sparked tremendous progress in the field of natural language processing (NLP) and computer vision. While many of the face, object, landmark, logo, and text recognition and detection technologies are provided for Internet-connected devices, we believe that the ever-increasing computational power of mobile devices can enable the delivery of these technologies into the hands of users anytime, anywhere, regardless of Internet connection.