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Course Skill Level:


Course Duration:

4 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with Python and basic IT and Linux skills

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others with Python skills who are intending to get the browser-based JavaScript library for training and deploying machine learning models effectively.
  • Skill-level: Foundation-level Machine Learning with TensorFlow.js skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

TensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this course, you will learn how to use TensorFlow.js to implement various ML models through an example-based approach. Starting with the basics, you’ll understand how ML models can be built on the web. Moving on, you will get to grips with the TensorFlow.js ecosystem to develop applications more efficiently. The course will then guide you through implementing ML techniques and algorithms such as regression, clustering, fast Fourier transform (FFT), and dimensionality reduction. You will later cover the Bellman equation to solve Markov decision process (MDP) problems and understand how it is related to reinforcement learning. Finally, you will explore techniques for deploying ML-based web applications and training models with TensorFlow Core. Throughout this ML course, you’ll discover useful tips and tricks that will build on your knowledge. By the end of this course, you will be equipped with the skills you need to create your own web-based ML applications and fine-tune models to achieve high performance.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning with TensorFlow.js expert instructor, students will learn about and explore:
  • Building, training, and running machine learning models in the browser using TensorFlow.js
  • Creating smart web applications from scratch with the help of useful examples
  • Using flexible and intuitive APIs from TensorFlow.js to understand how machine learning algorithms function
  • Using the t-SNE algorithm in TensorFlow.js to reduce dimensions in an input dataset
  • Deploying tfjs-converter to convert Keras models and load them into TensorFlow.js
  • Applying the Bellman equation to solve MDP problems
  • Using the k-means algorithm in TensorFlow.js to visualize prediction results
  • Creating tf.js packages with Parcel, Webpack, and Rollup to deploy web apps
  • Implementing tf.js backend frameworks to tune and accelerate app performance

Course breakdown / modules

  • Technical requirement
  • Why machine learning on the web
  • Operation graphs
  • What is TensorFlow.js?
  • Installing TensorFlow.js
  • The low-level API
  • The Layers API

  • Technical requirements
  • The portable model format
  • Exporting a model from TensorFlow
  • Converting models using tfjs-converter
  • Loading the model into TensorFlow.js

  • Technical requirements
  • Why high-level libraries?
  • Using existing models
  • Loading the data from various kinds of storage
  • Pose detection with ML5.js
  • Drawing cats with Magenta.js
  • XOR classification with machinelearn.js

  • Technical requirements
  • What is polynomial regression?
  • Two-dimensional curve fitting

  • Technical requirements
  • Background of binary classification
  • What is logistic regression?
  • Classifying two-dimensional clusters

  • Technical requirements
  • What is unsupervised learning?
  • Learning how K-means works
  • Generalizing K-means with the EM algorithm
  • Clustering two groups in a 2D space

  • Technical requirements
  • What is Fourier transformation?
  • Cosine curve decomposition

  • Technical requirements
  • Why dimensionality reduction?
  • Understanding principal component analysis
  • Projecting 3D points into a 2D space with PCA
  • Word embedding

  • Technical requirements
  • Reinforcement learning
  • Solving the four-states environment

  • Technical requirements
  • The ecosystem around the JavaScript platform
  • Module bundler
  • Deploying modules with GitHub Pages

  • Technical requirement
  • The backend API of TensorFlow.js
  • Tensor management
  • Asynchronous data access
  • Profiling
  • Model visualization

  • Future Work Around TensorFlow.js
  • Technical requirements
  • Experimental backend implementations
  • AutoML edge h