#machine learning

Discover how to embrace machine learning in JavaScript using TensorFlow.js in the browser and beyond in this speedy talk. Get inspired through a whole bunch of creative prototypes that push the boundaries of what is possible in the modern web browser (things have come a long way) and then take your own first steps with machine learning in minutes. By the end of the talk everyone will understand how to recognize an object of their choice which could then be used in any creative way you can imagine. Familiarity with JavaScript is assumed, but no background in machine learning is required. Come take your first steps with TensorFlow.js!

Have you ever wondered if we can really build machine learning models in React, rather than in the mainstream languages like Python or R? Afterall, React is the most used language by the developers, according to a 2019 survey by Stack Overflow. Well, this sounds like a crazy idea, because React is not designed for high performance computing and neural networks are compute-intensive! But, wait a minute - we have libraries such as Onnx.js, Tensorflow.js to our rescue! In this talk, I’ll be delving deeper into the process of building and deploying machine learning applications using React.

In this talk, we will discuss leveraging Machine Learning practices in Software Testing with several practical examples and a case study that I used in my project to do Bug Triage. Let's embrace the future together!

In this talk, I will share my experiences with using machine learning to identify cheaters in multiplayer games. We will also discuss the potential of machine learning in game development.

Machine learning is seen by many as the next step in artificial intelligence towards a new stage of human evolution. And thus helps us find new approaches to solving real-world problems. Phew... That sounds complex… And how is that supposed to be fun? Well, in addition to the big issues of our time, it is ultimately just another tool that we can play with. While it is important to first understand the core concepts of machine learning, we can quickly go way beyond that. Get ready for some unexpected examples of how to get started with machine learning in your React application!

Have you ever questioned whether Javascript is a viable alternative to Python or R for creating machine learning models? After all, a 2019 survey by Stack Overflow found that Javascript is the language that developers use the most. Given that machine learning models like neural networks require a lot of computational power and that javascript was not intended for high-speed computing, this approach seems unreasonable. But hold on, this not entirely true, as javascript libraries like Onnx.js and Tensorflow.js are here to save the day! I'll be going into further detail on how to create intuitive and innovative machine learning applications with React in this talk.

This talk will cover why and how organizations are distributing data storage and machine learning to the edge. By pushing machine learning to the edge, we can geographically distribute learning so that the models will actually learn different things relevant to specific locations. By delivering both edge database and compute in a single platform, more people can transition to a distributed architecture. The performance gains from this new architecture cements the value that mobile edge computing brings.

Modern tracing products work by combining diagnostics_channel with AsyncLocalStorage. Let's build a tracer together to see how it works and what you can do to make your apps more observable.

The talk covers the current scenario of writing test cases in JavaScript and the problems associated with the time and resources spent by companies to write the test cases and the lack of automation in this area.Then the talk will cover how AI and machine learning is being leveraged by tools such as Github Copilot and Ponicode to autogenerate test cases thus simplifying the software testing process.

The expansion of data size and complexity, broader adoption of ML, as well as the high expectations put on modern web apps all demand increasing compute power. Learn how the RAPIDS data science libraries can be used beyond notebooks, with GPU accelerated Node.js web services. From ETL to server side rendered streaming visualizations, the experimental Node RAPIDS project is developing a broad set of modules able to run across local desktops and multi-GPU cloud instances.

This talk will cover how we can apply machine learning to software testing including in Javascript, to help reduce the number of tests that need to be run.We can use predictive machine learning model to make an informed decision to rule out tests that are extremely unlikely to uncover an issue. This opens up a new, more efficient way of selecting tests.

This talk gives an introduction about MediaPipe which is an open source Machine Learning Solutions that allows running machine learning models on low-powered devices and helps integrate the models with mobile applications. It gives these creative professionals a lot of dynamic tools and utilizes Machine learning in a really easy way to create powerful and intuitive applications without having much / no knowledge of machine learning beforehand. So we can see how MediaPipe can be integrated with React. Giving easy access to include machine learning use cases to build web applications with React.

The world is filled with billions of small, connected, intelligent and compute-efficient smart-phones. What if we can tap into this power and do more on the edge? It turns out, ML fits perfectly here. Let us explore the MLKit library to bake in intelligence into react-native applications. 

Data visualisation is a fundamental part of Data Science. The talk will start with a practical demonstration (using pandas, scikit-learn, and matplotlib) of how relying on summary statistics and predictions alone can leave you blind to the true nature of your datasets. I will make the point that visualisations are crucial in every step of the Data Science process and therefore that Jupyter Notebooks definitely do belong in Data Science. We will then look at how maintainability is a real challenge for Jupyter Notebooks, especially when trying to keep them under version control with git. Although there exists a plethora of code quality tools for Python scripts (flake8, black, mypy, etc.), most of them don't work on Jupyter Notebooks. To this end I will present nbQA, which allows any standard Python code quality tool to be run on a Jupyter Notebook. Finally, I will demonstrate how to use it within a workflow which lets practitioners keep the interactivity of their Jupyter Notebooks without having to sacrifice their maintainability.

The domain of Natural Language Processing have seen a tremendous amount of research and innovation in the past couple of years to tackle the problem of implementing high quality machine learning and AI solutions using natural text. Text Classification is one such area that is extremely important in all sectors like finance, media, product development, etc. Building up a text classification system from scratch for every use case can be challenging in terms of cost as well as resources, considering there is a good amount of dataset to begin training with.
Here comes the concept of transfer learning. Using some of the models that has been pre-trained on terates of data and fine-tuning it based on the problem at hand is the new way to efficiently implement machine learning solutions without spending months on data cleaning pipeline.
This talk with highlight ways of implementing the newly launched BERT and fine tuning the base model to build an efficient text classifying model. Basic understanding of python is desirable.

DeepPavlov Agent is a framework designed to facilitate the development of scalable and production-ready multi-skill virtual assistants, complex dialogue systems, and chatbots. Key features of DeepPavlov Agent include (1) scalability and reliability in the high load environment due to micro-service architecture; (2) ease of adding and orchestrating conversational skills; (3) shared dialogue state memory and NLP annotations accessible to all skills.
DeepPavlov DREAM is a socialbot platform with a modular design with the main components such as annotators, skills and selectors run as independent services. These components are configured and deployed using Docker containers. It allows developers to focus on application development instead of focusing on the intrinsic details of the manual low-level infrastructure configuration.

TensorFlow has built a solid foundation for various machine learning applications, on top of which the Keras ecosystem can really boost the productivity of the developers in building machine learning solutions. Keras has a simple and arbitrarily flexible API for building and training models. However, we still need a lot of manual work to tune the hyperparameters. Fortunately, with Keras Tuner, we can automate the hyperparameter tuning process with minor modifications to the code for building and training the models. To further boost the productivity, we introduce AutoKeras, which fully automates the model building, training, and hyperparameter tuning process. It dramatically reduces the amount of prior knowledge needed of using machine learning for some common tasks. All you need is to define the task and to provide the training data.

Adoption of AI has been slowed the challenges involved in obtaining performant models, which require significant expertise and effort, and the limited number of practitioners with machine learning expertise. Automated machine learning (AutoML) eliminates the routine steps in the machine learning workflow, thus empowering domain experts without machine learning background to build good initial models, and allowing experienced practitioners to focus additional manual model optimization. This talk describes the extent of automation available for the various steps and demonstrates AutoML with a classifier for human activities based on accelerometer sensor data.

Chip and equipment manufacturing and tracking is a tough task given the strict adherence to quality standards and processes like six-sigma control checks. In this session, we will be looking at key real-world problems from Semiconductor & Manufacturing, and possible methodologies where we leveraged a combination of traditional computer vision techniques and coupled it with the power of deep transfer learning and machine \ deep learning. We will be covering two main use-cases from the Industry:
Automatic Defect detection at NanoscaleDefect Clustering at Nanoscale

Often it's thought that to be able to succeed with Machine Learning and Deep Learning, as an onramp to Artificial Intelligence, that you need a deep background in mathematics and calculus, as well as some form of PhD. But you don't. With modern APIs like TensorFlow, much of the complexity is abstracted away in pre-built libraries, so you can focus on learning. In this session, Laurence Moroney, from Google, will explain how he has used this to create courses with hundreds of thousands of students, and from there, how a certificate program was created.

This talk will be a walkthrough of utilizing machine learning to replace a rule based system for consumers. We will discuss when is it okay to use ML, how to build these models with intelligent data, evaluate these offline and finally how to validate this evaluation to land these models in production systems. Furthermore, we will illustrate various self-learning/interactive-learning strategies that can be used for production systems to automate how models teach themselves to become better.

Elegant and graceful mathematics make a cool textbook cover, but the inside of those same books are usually dry cold engineering. It's important to mix the theory of innovation with the excitement of practicality, and through the composition of these elements we find innovation. In this talk, I'll show you from an engineering perspective how to explore, balance, and ultimately bottle machined success.

As an AI scientist and a developer, I have been engaged with AI-applications for many years especially focusing on object detection and recognition purposes. I love thinking that we can get creative in designing neural networks. We can train them supervised, unsupervised, semi or self-supervised, and this gives possibilities to mimic the human brain in a narrow domain. However, in vision applications, there are still things where AI is lacking and will be lacking without computer vision knowledge. Computer vision has been solving detection and recognition problems for many years. However, in the last decade, it seems like AI is seen as a replacement of computer vision. AI can find the optimal model for a specific type of data set and it might achieve generalization better. AI can be designed in a way that it can learn life-long which also brings possibilities of creating models which serve better when they are used longer. However, an AI vision system will be lacking capabilities without computer vision knowledge. First of all, it will require a very big data set to train the model what can be expensive or even not possible. On the other hand, computer vision systems can be modeled only using a hand-drawn template image. Training AI models also requires GPUs. Nevertheless, I do not want to encourage everyone to train AI models for solving any simple problem which could be solved easily computer vision. Last but not least, knowing computer vision, machine learning and especially feature engineering methods helps to design hybrid models that might be more robust to adversarial attacks or changing conditions.
In this lecture, I will briefly introduce how computer vision (especially using the OpenCV library) and machine learning can be used for creating detection and recognition models. Some experience with python, jupyter notebook and some machine learning background would be useful to get more benefits from this lecture.

In this talk I'll start introducing the recent breakthroughs in NLP that resulted from the combination of Transfer Learning schemes and Transformer architectures. The second part of the talk will be dedicated to an introduction of the open-source tools released HuggingFace, in particular our Transformers, Tokenizers and Datasets libraries and our models.

Write and deploy machine learning models easily in Nodejs using Tensorflow.js.

JS awsomeness beyond webpages. First we'll write a cool 2D game (in Javascript) - and then - write AI code (in Javascript!) that will be able to win this game for us. Oh, what a time to be alive!

Discover how to embrace machine learning in JavaScript using TensorFlow.js in the browser and beyond in this speedy talk. Get inspired through a whole bunch of creative prototypes that push the boundaries of what is possible in the modern web browser (things have come a long way) and then take your own first steps with machine learning in minutes. By the end of the talk everyone will understand how to recognize an object of their choice which could then be used in any creative way you can imagine. Familiarity with JavaScript is assumed, but no background in machine learning is required. Come take your first steps with TensorFlow.js!