#machine learning

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Machine Learning is a subfield of Artificial Intelligence that enables computers to learn from data without being explicitly programmed. It involves the use of algorithms and statistical models to identify patterns in large datasets and make predictions or decisions based on them. In JavaScript, Machine Learning can be used for tasks such as natural language processing, image recognition, and predictive analytics.
Leveraging LLMs to Build Intuitive AI Experiences With JavaScript
JSNation 2024JSNation 2024
108 min
Leveraging LLMs to Build Intuitive AI Experiences With JavaScript
Featured Workshop
Roy Derks
Shivay Lamba
2 authors
Today every developer is using LLMs in different forms and shapes, from ChatGPT to code assistants like GitHub CoPilot. Following this, lots of products have introduced embedded AI capabilities, and in this workshop we will make LLMs understandable for web developers. And we'll get into coding your own AI-driven application. No prior experience in working with LLMs or machine learning is needed. Instead, we'll use web technologies such as JavaScript, React which you already know and love while also learning about some new libraries like OpenAI, Transformers.js
Charlie Gerard's Career Advice: Be intentional about how you spend your time and effort
6 min
Charlie Gerard's Career Advice: Be intentional about how you spend your time and effort
Featured Article
Charlie Gerard
Jan Tomes
2 authors
When it comes to career, Charlie has one trick: to focus. But that doesn’t mean that you shouldn’t try different things — currently a senior front-end developer at Netlify, she is also a sought-after speaker, mentor, and a machine learning trailblazer of the JavaScript universe. "Experiment with things, but build expertise in a specific area," she advises.

What led you to software engineering?My background is in digital marketing, so I started my career as a project manager in advertising agencies. After a couple of years of doing that, I realized that I wasn't learning and growing as much as I wanted to. I was interested in learning more about building websites, so I quit my job and signed up for an intensive coding boot camp called General Assembly. I absolutely loved it and started my career in tech from there.
 What is the most impactful thing you ever did to boost your career?I think it might be public speaking. Going on stage to share knowledge about things I learned while building my side projects gave me the opportunity to meet a lot of people in the industry, learn a ton from watching other people's talks and, for lack of better words, build a personal brand.
 What would be your three tips for engineers to level up their career?Practice your communication skills. I can't stress enough how important it is to be able to explain things in a way anyone can understand, but also communicate in a way that's inclusive and creates an environment where team members feel safe and welcome to contribute ideas, ask questions, and give feedback. In addition, build some expertise in a specific area. I'm a huge fan of learning and experimenting with lots of technologies but as you grow in your career, there comes a time where you need to pick an area to focus on to build more profound knowledge. This could be in a specific language like JavaScript or Python or in a practice like accessibility or web performance. It doesn't mean you shouldn't keep in touch with anything else that's going on in the industry, but it means that you focus on an area you want to have more expertise in. If you could be the "go-to" person for something, what would you want it to be? 
 And lastly, be intentional about how you spend your time and effort. Saying yes to everything isn't always helpful if it doesn't serve your goals. No matter the job, there are always projects and tasks that will help you reach your goals and some that won't. If you can, try to focus on the tasks that will grow the skills you want to grow or help you get the next job you'd like to have.
 What are you working on right now?Recently I've taken a pretty big break from side projects, but the next one I'd like to work on is a prototype of a tool that would allow hands-free coding using gaze detection. 
 Do you have some rituals that keep you focused and goal-oriented?Usually, when I come up with a side project idea I'm really excited about, that excitement is enough to keep me motivated. That's why I tend to avoid spending time on things I'm not genuinely interested in. Otherwise, breaking down projects into smaller chunks allows me to fit them better in my schedule. I make sure to take enough breaks, so I maintain a certain level of energy and motivation to finish what I have in mind.
 You wrote a book called Practical Machine Learning in JavaScript. What got you so excited about the connection between JavaScript and ML?The release of TensorFlow.js opened up the world of ML to frontend devs, and this is what really got me excited. I had machine learning on my list of things I wanted to learn for a few years, but I didn't start looking into it before because I knew I'd have to learn another language as well, like Python, for example. As soon as I realized it was now available in JS, that removed a big barrier and made it a lot more approachable. Considering that you can use JavaScript to build lots of different applications, including augmented reality, virtual reality, and IoT, and combine them with machine learning as well as some fun web APIs felt super exciting to me.


Where do you see the fields going together in the future, near or far? I'd love to see more AI-powered web applications in the future, especially as machine learning models get smaller and more performant. However, it seems like the adoption of ML in JS is still rather low. Considering the amount of content we post online, there could be great opportunities to build tools that assist you in writing blog posts or that can automatically edit podcasts and videos. There are lots of tasks we do that feel cumbersome that could be made a bit easier with the help of machine learning.
 You are a frequent conference speaker. You have your own blog and even a newsletter. What made you start with content creation?I realized that I love learning new things because I love teaching. I think that if I kept what I know to myself, it would be pretty boring. If I'm excited about something, I want to share the knowledge I gained, and I'd like other people to feel the same excitement I feel. That's definitely what motivated me to start creating content.
 How has content affected your career?I don't track any metrics on my blog or likes and follows on Twitter, so I don't know what created different opportunities. Creating content to share something you built improves the chances of people stumbling upon it and learning more about you and what you like to do, but this is not something that's guaranteed. I think over time, I accumulated enough projects, blog posts, and conference talks that some conferences now invite me, so I don't always apply anymore. I sometimes get invited on podcasts and asked if I want to create video content and things like that. Having a backlog of content helps people better understand who you are and quickly decide if you're the right person for an opportunity.What pieces of your work are you most proud of?It is probably that I've managed to develop a mindset where I set myself hard challenges on my side project, and I'm not scared to fail and push the boundaries of what I think is possible. I don't prefer a particular project, it's more around the creative thinking I've developed over the years that I believe has become a big strength of mine.***Follow Charlie on Twitter
TensorFlow.js 101: ML in the Browser and Beyond
ML conf EU 2020ML conf EU 2020
41 min
TensorFlow.js 101: ML in the Browser and Beyond
TensorFlow.js enables machine learning in the browser and beyond, with features like face mesh, body segmentation, and pose estimation. It offers JavaScript prototyping and transfer learning capabilities, as well as the ability to recognize custom objects using the Image Project feature. TensorFlow.js can be used with Cloud AutoML for training custom vision models and provides performance benefits in both JavaScript and Python development. It offers interactivity, reach, scale, and performance, and encourages community engagement and collaboration between the JavaScript and machine learning communities.
Let AI Be Your Docs
JSNation 2024JSNation 2024
69 min
Let AI Be Your Docs
Workshop
Jesse Hall
Jesse Hall
Join our dynamic workshop to craft an AI-powered documentation portal. Learn to integrate OpenAI's ChatGPT with Next.js 14, Tailwind CSS, and cutting-edge tech to deliver instant code solutions and summaries. This hands-on session will equip you with the knowledge to revolutionize how users interact with documentation, turning tedious searches into efficient, intelligent discovery.
Key Takeaways:
- Practical experience in creating an AI-driven documentation site.- Understanding the integration of AI into user experiences.- Hands-on skills with the latest web development technologies.- Strategies for deploying and maintaining intelligent documentation resources.
Table of contents:- Introduction to AI in Documentation- Setting Up the Environment- Building the Documentation Structure- Integrating ChatGPT for Interactive Docs
Forget Polygons – Gaussian Splats, the New Approach to Photorealistic 3D Graphics
JSNation 2024JSNation 2024
5 min
Forget Polygons – Gaussian Splats, the New Approach to Photorealistic 3D Graphics
Today, I'll be talking about GspotJS and Gaussian Splatting, a revolutionary graphics pipeline that can render high-fidelity scenes at 144 FPS. Gaussian Splatting is a technique that converts data directly into an image using Gaussians. GspotJS is a lightweight JavaScript library for Gaussian Splat rendering, with features like 4D rendering. The library aims to provide a simple and speedy way to view Splats on the web, while more advanced applications can use Mackellog Gaussian Splats 3D. Both Gaussian Splatting and gSplotJS are open-source.
Come On Barbie, Let’s Go Party: Using AI for Music Mixing
JSNation 2024JSNation 2024
27 min
Come On Barbie, Let’s Go Party: Using AI for Music Mixing
Today, we explore DJ mixing and how deep learning revolutionizes the art by discussing sound processing, extracting features, and using machine learning. Deep learning allows for efficient extraction of audio features and high-resolution track separation. Neural networks can achieve source separation by converting audio to spectrograms and applying convolutional and recurrent neural networks. This has immediate impact on industries such as karaoke and music transcription.
Coffee Chat With Documentation, Are You Ready?
JSNation 2024JSNation 2024
34 min
Coffee Chat With Documentation, Are You Ready?
Maya Chavin, a senior software engineer at Microsoft, discusses generative AI and the core model for LM. The flow of a document Q&A service and the importance of prompts in enhancing it are explored. The injection and querying phases of document Q&A are explained, emphasizing the need for efficient storage, indexing, and computing relevant prompts. The talk also covers the use of embedding models, optimization strategies, and the challenges of testing and validating AI results. Creative uses of LLMs and the impact of AI on job security are mentioned.
Can LLMs Learn? Let’s Customize an LLM to Chat With Your Own Data
C3 Dev Festival 2024C3 Dev Festival 2024
48 min
Can LLMs Learn? Let’s Customize an LLM to Chat With Your Own Data
WorkshopFree
Andreia Ocanoaia
Andreia Ocanoaia
Feeling the limitations of LLMs? They can be creative, but sometimes lack accuracy or rely on outdated information. In this workshop, we’ll break down the process of building and easily deploying a Retrieval-Augmented Generation system. This approach enables you to leverage the power of LLMs with the added benefit of factual accuracy and up-to-date information.
Build Intelligence at the Edge - Machine Learning with React Native
React Day Berlin 2023React Day Berlin 2023
13 min
Build Intelligence at the Edge - Machine Learning with React Native
Watch video: Build Intelligence at the Edge - Machine Learning with React Native
The Talk is about building intelligence at the edge with machine learning and React Native. It covers machine learning concepts, building ML models with React, challenges, best practices, and resources.
The Future is Today: Leveraging AI in Software Testing
TestJS Summit 2023TestJS Summit 2023
25 min
The Future is Today: Leveraging AI in Software Testing
This Talk discusses integrating machine learning into software testing, exploring its use in different stages of the testing lifecycle. It highlights the importance of training data and hidden patterns in machine learning. The Talk also covers generating relevant code for test automation using machine learning, as well as the observation and outlier detection capabilities of machine learning algorithms. It emphasizes the use of machine learning in maintenance, bug management, and classifying bugs based on severity levels. The Talk concludes with the results of classification and bug management, including the use of clustering.
Machine Learning in Game Development
JS GameDev Summit 2023JS GameDev Summit 2023
18 min
Machine Learning in Game Development
Today's Talk explores cheating in video games and the role of machine learning in detecting and preventing it. Trust and fairness are crucial in gaming, as players invest time and emotion into virtual worlds. Traditional rule-based models assess player actions, while machine learning can detect complex and evolving cheating methods. Training models and organizing data are key challenges in utilizing machine learning for cheating detection. The future lies in collaborative security systems that combine rule-based models with machine learning to protect against cheating.
useMachineLearning… and Have Fun with It!
React Summit 2023React Summit 2023
9 min
useMachineLearning… and Have Fun with It!
Watch video: useMachineLearning… and Have Fun with It!
Nico, a freelance frontend developer and part of the Google Developer Experts program, provides an introduction to machine learning in the browser. He explains how machine learning differs from traditional algorithms and highlights the use of TensorFlow.js for implementing machine learning in the browser. The talk also covers the use of different backends, such as WebGL, and the conversion of audio into spectrograms for model comparison. Nico mentions the use of overlay for improved detection accuracy and the availability of speech command detection and custom model training with TensorFlow. Overall, the talk emphasizes the benefits of using and training machine learning models directly on the device.
Giving Superpowers to Your React Apps with Machine Learning
React Summit 2023React Summit 2023
11 min
Giving Superpowers to Your React Apps with Machine Learning
Watch video: Giving Superpowers to Your React Apps with Machine Learning
Welcome to my lightning talk at React Summit 2023 where I discuss integrating machine learning capabilities in React apps using JavaScript libraries like TensorFlow.js and ONNX.js. These libraries allow for better privacy, lower cost, and lower latency by leveraging system hardware. Examples include using TensorFlow.js and CocoaSSIST to classify images and Ermine.ai for live audio transcription. React developers can now integrate machine learning without needing extensive knowledge of Python or other frameworks.
Scaling Distributed Machine Learning, to the Edge & Back
JSNation 2023JSNation 2023
21 min
Scaling Distributed Machine Learning, to the Edge & Back
This talk explores JavaScript's role in distributed machine learning at scale, discussing the lack of tooling and the accessibility of machine learning deployments. It also covers cloud-based machine learning architecture, machine learning at the edge, and the use of HarperDB for simplified machine learning deployment. The concept of iterative AI and model training is also discussed.
Observability with diagnostics_channel and AsyncLocalStorage
Node Congress 2023Node Congress 2023
21 min
Observability with diagnostics_channel and AsyncLocalStorage
Observability with Diagnostics Channel and async local storage allows for high-performance event tracking and propagation of values through calls, callbacks, and promise continuations. Tracing involves five events and separate channels for each event, capturing errors and return values. The span object in async local storage stores data about the current execution and is reported to the tracer when the end is triggered.
Machine Learning based Unit Tesing in JavaScript
TestJS Summit 2022TestJS Summit 2022
22 min
Machine Learning based Unit Tesing in JavaScript
This talk explores machine learning-based unit testing in JavaScript and TypeScript, focusing on tools like the Pony Code VS Code extension and GitHub Copilot. The Pony Code tool provides a graphical user interface for generating and managing unit test cases. GitHub Copilot acts as an intelligent code auto-completion tool, understanding the context of the code and suggesting unit test cases. These tools aim to improve code coverage and achieve 100% coverage.
GPU Accelerating Node.js Web Services and Visualization with RAPIDS
JSNation 2022JSNation 2022
26 min
GPU Accelerating Node.js Web Services and Visualization with RAPIDS
Welcome to GPU Accelerating Node.js Web Services and Visualization with Rapids. Rapids aims to bring high-performance data science capabilities to Node.js, providing a streamlined API to the Rapids platform without the need to learn a new language or environment. GPU acceleration in Node.js enables performance optimization and memory access without changing existing code. The demos showcase the power and speed of GPUs and rapids in ETL data processing, graph visualization, and point cloud interaction. Future plans include expanding the library, improving developer UX, and exploring native Windows support.
Predictive Testing in JavaScript with Machine Learning
TestJS Summit 2021TestJS Summit 2021
18 min
Predictive Testing in JavaScript with Machine Learning
This Talk explores the benefits of introducing machine learning to software testing, including automating test case generation and achieving close to 100% code coverage. AI is being used to automate test generation, improve regression testing, and make predictions in automation testing. Machine learning enables predictive testing by selecting tests that are more likely to uncover issues in code changes. AI-based tools are being used to generate automated tests, improve code coverage, and intelligently select tests. Companies are relying on dedicated testers and using historical code changes and test cases to generate specific test cases for relevant code changes.
Using MediaPipe to Create Cross Platform Machine Learning Applications with React
React Advanced Conference 2021React Advanced Conference 2021
21 min
Using MediaPipe to Create Cross Platform Machine Learning Applications with React
Top Content
MediaPipe is a cross-platform framework that helps build perception pipelines using machine learning models. It offers ready-to-use solutions for various applications, such as selfie segmentation, face mesh, object detection, hand tracking, and more. MediaPipe can be integrated with React using NPM modules provided by the MediaPipe team. The demonstration showcases the implementation of face mesh and selfie segmentation solutions. MediaPipe enables the creation of amazing applications without needing to understand the underlying computer vision or machine learning processes.
ML on the Edge
React Summit Remote Edition 2020React Summit Remote Edition 2020
7 min
ML on the Edge
This Talk discusses machine learning on the edge and its benefits for mobile applications. ML on the edge utilizes the computing power of mobile devices for secure, real-time processing and offline capabilities. ML Kit, Google's SDK, provides easy integration of ML solutions in mobile apps without extensive ML expertise. The Talk covers the setup of Firebase and ML Kit integration in React Native projects, showcasing the possibilities of applying filters and generating avatars with ML on the edge.
Introduction to Machine Learning on the Cloud
ML conf EU 2020ML conf EU 2020
146 min
Introduction to Machine Learning on the Cloud
Workshop
Dmitry Soshnikov
Dmitry Soshnikov
This workshop will be both a gentle introduction to Machine Learning, and a practical exercise of using the cloud to train simple and not-so-simple machine learning models. We will start with using Automatic ML to train the model to predict survival on Titanic, and then move to more complex machine learning tasks such as hyperparameter optimization and scheduling series of experiments on the compute cluster. Finally, I will show how Azure Machine Learning can be used to generate artificial paintings using Generative Adversarial Networks, and how to train language question-answering model on COVID papers to answer COVID-related questions.
Hands on with TensorFlow.js
ML conf EU 2020ML conf EU 2020
160 min
Hands on with TensorFlow.js
Workshop
Jason Mayes
Jason Mayes
Come check out our workshop which will walk you through 3 common journeys when using TensorFlow.js. We will start with demonstrating how to use one of our pre-made models - super easy to use JS classes to get you working with ML fast. We will then look into how to retrain one of these models in minutes using in browser transfer learning via Teachable Machine and how that can be then used on your own custom website, and finally end with a hello world of writing your own model code from scratch to make a simple linear regression to predict fictional house prices based on their square footage.
The Hitchhiker's Guide to the Machine Learning Engineering Galaxy
ML conf EU 2020ML conf EU 2020
112 min
The Hitchhiker's Guide to the Machine Learning Engineering Galaxy
Workshop
Alyona Galyeva
Alyona Galyeva
Are you a Software Engineer who got tasked to deploy a machine learning or deep learning model for the first time in your life? Are you wondering what steps to take and how AI-powered software is different from traditional software? Then it is the right workshop to attend.
The internet offers thousands of articles and free of charge courses, showing how it is easy to train and deploy a simple AI model. At the same time in reality it is difficult to integrate a real model into the current infrastructure, debug, test, deploy, and monitor it properly. In this workshop, I will guide you through this process sharing tips, tricks, and favorite open source tools that will make your life much easier. So, at the end of the workshop, you will know where to start your deployment journey, what tools to use, and what questions to ask.
Dabl: Automatic Machine Learning with a Human in the Loop
ML conf EU 2020ML conf EU 2020
35 min
Dabl: Automatic Machine Learning with a Human in the Loop
This talk introduces Dabble, a library that allows data scientists to iterate quickly and incorporate human input into the machine learning process. Dabble provides tools for each step of the machine learning workflow, including problem statement, data cleaning, visualization, model building, and model interpretation. It uses mosaic plots and pair plots to analyze categorical and continuous features. Dabble also implements a portfolio-based automatic machine learning approach using successive halving to find the best model. The future goals of Dabble include supporting more feature types, improving the portfolio, and building explainable models.
Never Have an Unmaintainable Jupyter Notebook Again!
ML conf EU 2020ML conf EU 2020
26 min
Never Have an Unmaintainable Jupyter Notebook Again!
Jupyter Notebooks are important for data science, but maintaining them can be challenging. Visualizing data sets and using code quality tools like NBQA can help address these challenges. Tools like nbdime and Precommit can assist with version control and future code quality. Configuring NBQA and other code quality tools can be done in the PyProject.toml file. NBQA has been integrated into various projects' continuous integration workflows. Moving code from notebooks to Python packages should be considered based on the need for reproducibility and self-contained solutions.
Power of Transfer Learning in NLP: Build a Text Classification Model Using BERT
ML conf EU 2020ML conf EU 2020
35 min
Power of Transfer Learning in NLP: Build a Text Classification Model Using BERT
Transfer learning is a technique used when there is a scarcity of labeled data, where a pre-trained model is repurposed for a new task. BERT is a bidirectional model trained on plain text that considers the context of tokens during training. Understanding the baseline NLP modeling and addressing challenges like context-based words and spelling errors are crucial. BERT has applications in multiple problem-solving scenarios, but may not perform well in strict classification labels or conversational AI. Training BERT involves next sentence prediction and mass language modeling to handle contextual understanding and coherent mapping.
Can You Sing with All the Voices of the Features?
ML conf EU 2020ML conf EU 2020
8 min
Can You Sing with All the Voices of the Features?
This Talk discusses the role of repetition in songwriting and how it has become more prevalent over the years. The use of string metrics, such as the Levenstein distance, allows for the analysis of similarity between segments of songs. A similarity threshold of 70% is used to determine if segments are considered similar. Overall, the Talk explores the importance of repetition in creating successful songs and the use of analytical tools to measure similarity.
Browser Session Analytics: The Key to Fraud Detection
ML conf EU 2020ML conf EU 2020
7 min
Browser Session Analytics: The Key to Fraud Detection
Blue Tab Solutions specializes in advanced analytics and big data, and recently improved financial fraud detection using Spark and the CRISPM methodology. They discovered insights like the correlation between fraudulent sessions and the mobile cast page accessed from the web application. The models created using decision trees, random forest classifiers, and gradient boosting classifiers were validated using the area under the ROC curve. The GVT classifier yielded the best result with a score of 0.94. Regular training is necessary for accurate models, and the next steps involve real-time action when fraud is detected.
Machine Learning on the Edge Using TensorFlow Lite
ML conf EU 2020ML conf EU 2020
8 min
Machine Learning on the Edge Using TensorFlow Lite
Håkan Silvernagel introduces TensorFlow Lite, an open-source deep learning framework for deploying machine learning models on mobile and IoT devices. He highlights the benefits of using TensorFlow Lite, such as reduced latency, increased privacy, and improved connectivity. The Talk includes a demonstration of object recognition capabilities and a real-world example of using TensorFlow Lite to detect a disease affecting farmers in Tanzania. References to official TensorFlow documentation, Google IO conference, and TensorFlow courses on Coursera are provided.
DeepPavlov Agent: Open-source Framework for Multiskill Conversational AI
ML conf EU 2020ML conf EU 2020
27 min
DeepPavlov Agent: Open-source Framework for Multiskill Conversational AI
The Pavlov Agent is an open source framework for multi-skill conversational AI, addressing the need for specific skills in different domains. The microservice architecture allows for scalability and skill reuse. The Deep Pavlov Library enables the creation of NLP pipelines for different skills. The Deep Pavlov Dream serves as a repository for skills and templates, while the Deployment Agent orchestrates all components for a seamless conversational experience. DeepLove.AI offers more flexibility and customization compared to Microsoft's LUIS service.
Boost Productivity with Keras Ecosystem
ML conf EU 2020ML conf EU 2020
30 min
Boost Productivity with Keras Ecosystem
This Talk introduces the TensorFlow in Keras ecosystem and highlights its features, including tensor manipulations, automatic differentiation, and deployment. It also discusses the workflow and automation of hyperparameter tuning with Keras Tuner and AutoKeras. The Talk emphasizes the simplicity and productivity of using AutoKeras, which supports various tasks and advanced scenarios. It also mentions the challenges beginners face and provides resources for learning. Lastly, it touches on the use of TensorFlow and Keras in the research domain and the customization options in AutoKeras, including time series forecasting.
Broadening AI Adoption with AutoML
ML conf EU 2020ML conf EU 2020
9 min
Broadening AI Adoption with AutoML
AutomL simplifies the complexity of building machine learning models, allowing engineers to focus on the hard problems and applications. It enables the solving of problems that wouldn't be feasible otherwise. The three-step AutomL approach by MathWorks includes wavelet scattering for feature extraction. AutoML also enables feature selection and model optimization for memory and power-limited embedded systems. MATLAB can translate to low-level code for deployment.
Processing Robot Data at Scale with R and Kubernetes
ML conf EU 2020ML conf EU 2020
8 min
Processing Robot Data at Scale with R and Kubernetes
The Talk discusses the challenges of managing and analyzing the increasing volume of data gathered from robots. It highlights the importance of data extraction and feature engineering in analyzing what happens before a failure. The use of Kubernetes and Packyderm for data management and automatic updates in the pipeline is mentioned. The parallelization of R scripts and the scalability of large clusters for data collection and processing are emphasized. The Talk also mentions the use of AI at the robot fleet level for unlocking new opportunities.
Deep Transfer Learning for Computer Vision
ML conf EU 2020ML conf EU 2020
8 min
Deep Transfer Learning for Computer Vision
Dipanjan Sarkar
Sachin Dangayach
2 authors
Today's Talk focuses on deep transfer learning for Computer Vision in the semiconductor manufacturing industry, specifically defect classification. The speakers discuss using a hybrid classification system with pre-trained models and image augmentation for accurate defect detection. They also explore the use of unsupervised learning, leveraging clustering algorithms and pre-trained models like ResNet-50, for defect analysis without prior knowledge. The process is reproducible, user-friendly, and provides accurate cluster results, with potential for future supervised learning applications.
Teaching ML and AI to Coders
ML conf EU 2020ML conf EU 2020
34 min
Teaching ML and AI to Coders
The Talk discusses the current state of AI and the challenges faced in educating developers. Google's mission is to train 10 percent of the world's developers in machine learning and AI. They have developed specializations and training initiatives to make AI easy and accessible. The impact of AI education includes rigorous certification exams and partnerships with universities. The Talk also highlights the growth trends in the tech industry and the importance of AI skills. TensorFlow is recommended for its deployment capabilities, and practice is emphasized for building a career in machine learning.
How to Machine Learn-ify any Product
ML conf EU 2020ML conf EU 2020
33 min
How to Machine Learn-ify any Product
In this Talk, an ML engineer from Facebook shares insights on when to use ML and a successful use case from Facebook. The speaker discusses the process of using ML for Facebook Portal calls, including data collection and model selection. The importance of precision and recall in ML models is emphasized, as well as the need for online evaluation and active learning. The Talk also touches on the challenges of data protection and label delay in ML model development.
The Evolution Revolution
ML conf EU 2020ML conf EU 2020
31 min
The Evolution Revolution
The Talk discusses the challenges of implementing software solutions and the need for abstractions. It emphasizes the importance of innovation and implementing once to avoid complexity. The use of Brain.js in machine learning research and its practical applications are highlighted. The talk also mentions the benefits of using JavaScript and GPU.js for graphics processing. Overall, the Talk encourages simplicity, efficiency, and collaboration in software development.
Computer Vision Using OpenCV
ML conf EU 2020ML conf EU 2020
32 min
Computer Vision Using OpenCV
Today's Talk explores image processing, computer vision, and their combination with machine learning. Image processing involves manipulating images, while computer vision extracts valuable information from images. Histograms are crucial in image processing as they represent the distribution of brightness values. Various image processing techniques can be used, such as thresholding and convolution. Computer vision techniques focus on extracting important features for object recognition and can be hand-tailored. Audio processing is not the focus of OpenCV, but TensorFlow libraries may be more suitable. Understanding the algorithms behind the code is important for robustness and effective debugging. Computer vision has applications in healthcare for cancer recognition and in agriculture for plant health monitoring.
An Introduction to Transfer Learning in NLP and HuggingFace
ML conf EU 2020ML conf EU 2020
32 min
An Introduction to Transfer Learning in NLP and HuggingFace
Transfer learning in NLP allows for better performance with minimal data. BERT is commonly used for sequential transfer learning. Models like BERT can be adapted for downstream tasks such as text classification. Handling different types of inputs in NLP involves concatenating or duplicating the model. Hugging Face aims to tackle challenges in NLP through knowledge sharing and open sourcing code and libraries.
Machine Learning in Node.js using Tensorflow.js
Node Congress 2021Node Congress 2021
8 min
Machine Learning in Node.js using Tensorflow.js
The Talk introduces TensorFlow.js in Node.js for machine learning, highlighting its open-source nature and easy integration with JavaScript. It emphasizes the benefits of using Node.js, such as the ability to write machine learning models directly in JavaScript, access to the NPM ecosystem, and improved performance. The different packages available for utilizing TensorFlow.js in Node.js, including CPU, GPU, and vanilla packages, are discussed. The importance of setting up Node.js bindings to avoid blocking the main thread is mentioned, along with the availability of APIs like dfNode and TensorBoard.
Games Are Smarter Than Us
React Summit 2020React Summit 2020
26 min
Games Are Smarter Than Us
Today's Talk explores game development using JavaScript, including building games in the browser, using game engines, and utilizing the BitMellow framework. It also delves into the concept of using AI to make computers play games, discussing reinforcement learning and implementing it in games like Flappy Bird. The Talk highlights the process of teaching the agent to learn, modifying rewards to improve performance, and the journey of game development from initial stages to advanced AI integration.
TensorFlow.JS 101: ML in the Browser and Beyond
JSNation Live 2021JSNation Live 2021
39 min
TensorFlow.JS 101: ML in the Browser and Beyond
JavaScript with TensorFlow.js allows for machine learning in various environments, enabling the creation of applications like augmented reality and sentiment analysis. TensorFlow.js offers pre-trained models for object detection, body segmentation, and face landmark detection. It also allows for 3D rendering and the combination of machine learning with WebGL. The integration of WebRTC and WebXR enables teleportation and enhanced communication. TensorFlow.js supports transfer learning through Teachable Machine and Cloud AutoML, and provides flexibility and performance benefits in the browser and Node.js environments.