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Machine Learning with TensorFlow.js

  • Course Code: Artificial Intelligence - Machine Learning with TensorFlow.js
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 4 Days Audience: This course is geared for Python experienced developers, analysts or others who are intending Get the browser-based JavaScript library for training and deploying machine learning models effectively

Course Snapshot 

  • Duration: 4 days 
  • Skill-level: Foundation-level Machine Learning with TensorFlow.js skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who are intending Get the browser-based JavaScript library for training and deploying machine learning models effectively  
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

Working in a hands-on learning environment, led by our Machine Learning with TensorFlow.js expert instructor, students will learn about and explore: 

  • Build, train and run machine learning models in the browser using TensorFlow.js 
  • Create smart web applications from scratch with the help of useful examples 
  • Use flexible and intuitive APIs from TensorFlow.js to understand how machine learning algorithms function 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Use the t-SNE algorithm in TensorFlow.js to reduce dimensions in an input dataset 
  • Deploy tfjs-converter to convert Keras models and load them into TensorFlow.js 
  • Apply the Bellman equation to solve MDP problems 
  • Use the k-means algorithm in TensorFlow.js to visualize prediction results 
  • Create tf.js packages with Parcel, Webpack, and Rollup to deploy web apps 
  • Implement tf.js backend frameworks to tune and accelerate app performance 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to get the browser-based JavaScript library for training and deploying machine learning models effectively. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills.  
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js 
  1. Machine Learning for the Web 

Machine Learning for the Web 

Technical requirement 

Why machine learning on the web 

Operation graphs 

What is TensorFlow.js? 

Installing TensorFlow.js 

The low-level API 

The Layers API 

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

Section 2: Real-World Applications of TensorFlow.js 

  1. Polynomial Regression 
  • Polynomial Regression 
  • Technical requirements 
  • What is polynomial regression? 
  • Two-dimensional curve fitting 
  1. Classification with Logistic Regression 
  • Classification with Logistic Regression 
  • Technical requirements 
  • Background of binary classification 
  • What is logistic regression? 
  • Classifying two-dimensional clusters 
  1. Unsupervised Learning 
  • Unsupervised Learning 
  • 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 
  1. Sequential Data Analysis 
  • Sequential Data Analysis 
  • Technical requirements 
  • What is Fourier transformation? 
  • Cosine curve decomposition 
  1. Dimensionality Reduction 
  • Dimensionality Reduction 
  • Technical requirements 
  • Why dimensionality reduction? 
  • Understanding principal component analysis 
  • Projecting 3D points into a 2D space with PCA 
  • Word embedding 
  1. Solving the Markov Decision Process 
  • Solving the Markov Decision Process 
  • Technical requirements 
  • Reinforcement learning 
  • Solving the four-states environment 
  1. Section 3: Productionizing Machine Learning Applications with TensorFlow.js 
  1. Deploying Machine Learning Applications 
  • Deploying Machine Learning Applications 
  • Technical requirements 
  • The ecosystem around the JavaScript platform 
  • Module bundler 
  • Deploying modules with GitHub Pages 
  1. Tuning Applications to Achieve High Performance 
  • Tuning Applications to Achieve High Performance 
  • Technical requirement 
  • The backend API of TensorFlow.js 
  • Tensor management 
  • Asynchronous data access 
  • Profiling 
  • Model visualization 
  1. Work Around TensorFlow.js 
  • Future Work Around TensorFlow.js 
  • Technical requirements 
  • Experimental backend implementations 
  • AutoML edge h 
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