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Charles Ivan Niswander II
By the end of this project, you will learn how to code a smart webcam to detect people and other everyday objects using a pre-trained COCO-SSD image recognition model with Tensorflow.js. Based on an older library called deeplearn.js, Tensorflow.js is a deep...
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By the end of this project, you will learn how to code a smart webcam to detect people and other everyday objects using a pre-trained COCO-SSD image recognition model with Tensorflow.js. Based on an older library called deeplearn.js, Tensorflow.js is a deep learning library that leverages Tensorflow to create, train and run inference on artificial neural network models directly in a web browser, utilizing the client's GPU/CPU resources (accelerated using WebGL). Tensorflow.js brings Tensorflow to the web! JavaScript/Typescript experience is heavily recommended. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops usable skills in image recognition models, TensorFlow.js, and Web browser coding for models
Emphasizes practical application of image recognition technology in web development
Introduces students to a well-established deep learning library with a focus on TensorFlow.js
Requires JavaScript/TypeScript experience and recommends learners in the North America region, which may limit accessibility

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Reviews summary

Tensorflow.js course

TensorFlow.js course receives mixed reviews. One reviewer found the course to be thorough and informative, while two others found the content lacking and not worth the price.
Thorough course
"Thorough and informative"
Very basic
"The content was very lacking...Very basic."
Not worth the price
"Not recommended.$9.99 is way more expensive for a session which is uninformative"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Getting Started with Tensorflow.js with these activities:
Review JavaScript and Typescript Fundamentals
Ensure you have a solid understanding of JavaScript and Typescript, as these are the programming languages used in this course.
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  • Revisit the basics of JavaScript syntax, including data types, variables, operators, and control flow.
  • Review object-oriented programming concepts in JavaScript, such as classes, inheritance, and polymorphism.
  • Familiarize yourself with the basics of Typescript, including its type system and how it enhances JavaScript.
Resource Collection: Tensorflow.js Learning Materials
Gather and organize a comprehensive collection of resources for further learning and reference on Tensorflow.js.
Browse courses on TensorFlow.js
Show steps
  • Conduct an online search for tutorials, documentation, and other relevant materials on Tensorflow.js.
  • Curate a list of high-quality resources that cover different aspects of Tensorflow.js.
  • Organize the resources into a structured format, such as a website, blog post, or online document.
  • Share your resource collection with other learners in the course or the broader Tensorflow.js community.
Review JavaScript/TypeScript
Reinforce your JavaScript/Typescript knowledge to prepare for the course.
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  • Identify the basics of JavaScript/TypeScript syntax and data types.
  • Review functions, control flow, and object-oriented programming concepts.
14 other activities
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Complete TensorFlow.js tutorials
Reinforce your understanding of TensorFlow.js through practical examples and exercises.
Browse courses on TensorFlow.js
Show steps
  • Follow the TensorFlow.js tutorials on image classification
  • Practice building simple machine learning models using TensorFlow.js
  • Experiment with different TensorFlow.js models and techniques
Tutorial: Hands-on Practice with Tensorflow.js
Familiarize yourself with the basics of Tensorflow.js and explore its capabilities through interactive tutorials.
Browse courses on TensorFlow.js
Show steps
  • Follow the Tensorflow.js tutorial series on the official website.
  • Experiment with the code examples provided in the tutorials.
  • Create a small demo project using Tensorflow.js to solidify your understanding.
Attend a Tensorflow.js workshop
Enhance your knowledge by attending a workshop focused on TensorFlow.js applications.
Browse courses on TensorFlow.js
Show steps
  • Find a local or online Tensorflow.js workshop
  • Attend the workshop and participate actively
  • Apply what you learn to your own projects
Coding Challenges with Tensorflow.js
Sharpen your Tensorflow.js and coding skills through hands-on exercises.
Browse courses on TensorFlow.js
Show steps
  • Solve coding problems related to image recognition using Tensorflow.js.
  • Experiment with different model architectures and hyperparameters.
  • Debug and optimize your code for performance.
Practice coding exercises on Tensorflow.js Playground
Gain practical experience implementing image recognition models using Tensorflow.js through guided tutorials and hands-on exercises.
Show steps
  • Visit the Tensorflow.js Playground website
  • Follow along with guided tutorials to understand the basics of using Tensorflow.js
  • Complete hands-on coding exercises to apply your knowledge
Study Group Discussions
Collaborate with fellow students to deepen your understanding of the concepts covered in the course.
Show steps
  • Join or form a study group with other learners in this course.
  • Meet regularly to discuss course materials, share insights, and solve problems together.
  • Take turns presenting and explaining concepts to each other.
Explore Advanced Tensorflow.js Techniques
Expand your knowledge of Tensorflow.js by following guided tutorials.
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Show steps
  • Explore advanced model architectures, such as GANs or RNNs.
  • Learn about techniques for data preprocessing and augmentation.
Tensorflow.js Coding Challenges
Test your skills and reinforce your understanding by solving coding challenges related to Tensorflow.js.
Browse courses on TensorFlow.js
Show steps
  • Find online resources or platforms that offer Tensorflow.js coding challenges.
  • Select challenges that align with the topics covered in the course.
  • Solve the challenges using Tensorflow.js and review your solutions.
Implement a TensorFlow.js model for object detection
Build a hands-on project to solidify your understanding of image recognition models and object detection.
Browse courses on Object Detection
Show steps
  • Gather and preprocess your dataset
  • Train your TensorFlow.js model using the COCO-SSD model
  • Deploy your model to a web application
  • Test and evaluate your model
Build a Simple Object Detection Web App
Apply your knowledge to create a practical project that showcases your understanding of object detection.
Browse courses on Object Detection
Show steps
  • Design and implement the user interface for the web app.
  • Integrate Tensorflow.js for object detection functionality.
  • Test and deploy the web app for real-world use.
Connect with mentors in the field
Seek out mentors who work in computer vision development, natural language processing, or other related domains to gain insights, advice, and support.
Show steps
  • Identify potential mentors through professional networking platforms like LinkedIn
  • Reach out to mentors via email or messaging, expressing your interest in their work and requesting guidance
Smart Webcam Object Detector Application
Put your newfound skills to the test by building a practical application that leverages Tensorflow.js for object detection.
Browse courses on Image Recognition
Show steps
  • Design and implement the frontend interface for your webcam application.
  • Integrate Tensorflow.js and the COCO-SSD model into your application.
  • Develop the core object detection functionality using the Tensorflow.js API.
  • Test and refine your application to ensure accurate object detection.
  • Deploy your application and share it with others.
Blog Post: Exploring the Applications of Tensorflow.js
Share your knowledge and insights about Tensorflow.js by creating a blog post on its applications in various domains.
Browse courses on TensorFlow.js
Show steps
  • Research different use cases and applications of Tensorflow.js across industries.
  • Choose a specific topic or domain to focus on for your blog post.
  • Write a detailed and engaging blog post that explains the concepts and showcases the potential of Tensorflow.js in your chosen domain.
  • Promote your blog post on social media and share it with the Tensorflow.js community.
Build a simple web application for object detection
Apply your understanding of Tensorflow.js to build a functional web application that can detect objects in real-time.
Show steps
  • Design the user interface and functionality of your web application
  • Integrate Tensorflow.js into your web application to enable object detection capabilities
  • Test and iterate on your application to improve its accuracy and usability

Career center

Learners who complete Getting Started with Tensorflow.js will develop knowledge and skills that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision systems. These systems allow computers to see and interpret the world around them. Computer Vision Engineers work in a variety of industries, including robotics, autonomous vehicles, and healthcare. This course may be particularly useful for those pursuing a career as a Computer Vision Engineer as it provides hands-on experience with TensorFlow.js, a library that is commonly used for computer vision tasks.
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. They work in a variety of industries, including finance, healthcare, and manufacturing. This course may be useful for aspiring Machine Learning Engineers as it provides a foundation in machine learning, a field that is essential for this role.
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and develop artificial intelligence systems. These systems can perform a variety of tasks, such as recognizing speech, translating languages, and playing games. This course may be useful for those pursuing a career as an Artificial Intelligence Engineer as it provides a foundation in machine learning, a field that is essential for this role.
Web Developer
Web Developers design, develop, and maintain websites. They work in a variety of industries, including finance, healthcare, and e-commerce. This course may be useful for those pursuing a career as a Web Developer as it provides a foundation in JavaScript, a language that is essential for web development.
Robotics Engineer
Robotics Engineers design, build, and maintain robots. They work in a variety of industries, including manufacturing, healthcare, and defense. This course may be useful for aspiring Robotics Engineers as it provides a foundation in machine learning, a field that is becoming increasingly important in robotics.
Full-Stack Developer
Full Stack Developers have a broad range of responsibilities, working on both the front-end and back-end of websites and web applications. They typically have expertise in a variety of programming languages and technologies. This course may be useful for aspiring Full Stack Developers as it provides a foundation in JavaScript, a language that is commonly used for both front-end and back-end development.
Data Scientist
Data Scientists use their knowledge of statistics, mathematics, and programming to extract insights from data. They work in a variety of industries, including finance, healthcare, and e-commerce. This course may be useful for those pursuing a career as a Data Scientist as it provides a foundation in machine learning, a field that is becoming increasingly important in data science.
Software Engineer
Software Engineers design, develop, and maintain software applications. They work in a variety of industries, including finance, healthcare, and e-commerce. This course may be useful for those pursuing a career as a Software Engineer as it provides a foundation in JavaScript, a language that is commonly used for web and mobile development.
Data Analyst
Data Analysts use their knowledge of statistics, mathematics, and programming to analyze data and extract insights. They work in a variety of industries, including finance, healthcare, and e-commerce. This course may be useful for aspiring Data Analysts as it provides a foundation in machine learning, a field that is becoming increasingly important in data analysis.
Front-End Developer
Front-end Developers are primarily responsible for the visual layout of a website or web application. They use their knowledge of programming languages such as HTML, CSS, and JavaScript to bring a designer's vision to life. This course may be useful for those seeking to become Front-end Developers as it offers hands-on experience with JavaScript, a language that is essential for creating interactive and dynamic web pages.
User Experience Designer
User Experience Designers design and evaluate the user experience of websites and web applications. They work closely with developers to ensure that websites and web applications are easy to use and enjoyable to interact with. This course may be useful for those pursuing a career as a User Experience Designer as it provides a foundation in JavaScript, a language that is commonly used for web development.
Operations Manager
Operations Managers are responsible for the day-to-day operations of a business. They oversee a variety of functions, such as production, inventory, and customer service. This course may be useful for aspiring Operations Managers as it provides a foundation in machine learning, which can be used to improve operational efficiency.
Product Manager
Product Managers are responsible for the development and launch of new products. They work closely with engineers, designers, and marketers to ensure that products meet the needs of users. This course, though not directly related to the field of product management, provides a foundation in machine learning, which may be helpful for Product Managers who are interested in developing data-driven products.
Marketing Manager
Marketing Managers are responsible for the development and execution of marketing campaigns. They work closely with creative teams, sales teams, and public relations teams to ensure that marketing campaigns are effective. This course may be useful for aspiring Marketing Managers as it provides a foundation in machine learning, which is becoming increasingly important in marketing.
Sales Manager
Sales Managers are responsible for leading and motivating sales teams. They set sales targets, develop sales strategies, and close deals. This course may be useful for aspiring Sales Managers as it provides a foundation in machine learning, which can be used to improve sales forecasting and customer segmentation.

Reading list

We've selected six books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Getting Started with Tensorflow.js.
Provides a practical guide to using TensorFlow.js for machine learning. It covers the basics of TensorFlow.js and how to use it to build and train machine learning models in a web browser. It valuable resource for beginners who want to learn more about TensorFlow.js and its applications.
Provides a comprehensive introduction to deep learning with Python. It covers the basics of deep learning, including how to build and train deep learning models. The book also includes several case studies that show how to use deep learning to build real-world applications.
Provides a comprehensive introduction to deep learning with R. It covers the basics of deep learning, including how to build and train deep learning models. The book also includes several case studies that show how to use deep learning to build real-world applications.
Provides a comprehensive introduction to machine learning with Python. It covers the basics of machine learning, including how to build and train machine learning models. The book also includes several case studies that show how to use machine learning to build real-world applications.
Provides a comprehensive introduction to deep learning with PyTorch. It covers the basics of deep learning, including how to build and train deep learning models. The book also includes several case studies that show how to use deep learning to build real-world applications.
Provides a comprehensive introduction to machine learning with C++. It covers the basics of machine learning, including how to build and train machine learning models. The book also includes several case studies that show how to use machine learning to build real-world applications.

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