Completed
Recognize custom objects with TensorFlow.js.
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Machine Learning for Web Developers - Web ML
Automatically move to the next video in the Classroom when playback concludes
- 1 1.1: Machine Learning for Web Devs & Creatives (Web ML) - Next gen web apps with TensorFlow.js
- 2 How web developers can use machine learning
- 3 1.3: Breakdown of WebML course
- 4 AI demystified: The difference between artificial intelligence, machine learning, and deep learning
- 5 2.2: Demystifying Machine Learning
- 6 Machine learning systems primer: How to train ML models
- 7 2.4: What is TensorFlow.js? (JavaScript + Machine Learning)
- 8 2.5: 3 ways to use Machine Learning on the web with TensorFlow.js
- 9 Using pre-trained models in TensorFlow | Machine Learning for web developers
- 10 3.2: Selecting an ML model to use
- 11 3.3.1: Make your own web based smart camera in JS - Part 1
- 12 3.3.2: Make your own web based smart camera in JS - Part 2
- 13 3.3.3: Build a web based smart camera in JavaScript - Part 3
- 14 3.3.4: Make your own web based smart camera in JS - Part 4
- 15 3.3.5: Make your own web based smart camera in JS - Part 5
- 16 Tutorial: Make a web-based smart camera with the COCO-SSD machine learning model in TensorFlow
- 17 TensorFlow fundamentals: What are tensors in TensorFlow.js?
- 18 Tutorial: How to use raw tensorFlow.js pre-trained models in browser
- 19 Exploring Tensorflow Hub: Using pre-trained web ML models
- 20 3.6.2: Using advanced pre-trained Web ML models - Part 2: Use MoveNet for pose estimation in browser
- 21 4.1: Rolling your own Web ML models from a blank canvas
- 22 4.2: Gathering, refining, and using data effectively for ML model datasets
- 23 4.3.1: What's a neuron?
- 24 ML tutorial: How to train neurons
- 25 4.4.1: Implement a neuron for linear regression - Training data and outliers
- 26 4.4.2: Implement a neuron for linear regression - Importing and normalizing training data
- 27 TensorFlow.js tutorial: A neuron implementation for linear regression
- 28 4.5.1: Multi-layer perceptrons - The limits of a single neuron
- 29 4.5.2: Multi-layer perceptrons - Deep neural networks for non linear data
- 30 ML tutorial: How to solve classification problems with TensorFlow and multi-layer perceptrons
- 31 4.6.2: Multi-layer perceptrons for classification - Implementing a classifier in TensorFlow.js
- 32 4.7.1: Beyond perceptrons: Convolutional Neural Network (CNNs) in the web browser
- 33 4.7.2: Beyond perceptrons: Convolutional Neural Network (CNNs) - Implementation with TensorFlow.js
- 34 5.1: Transfer learning: Retraining existing models in the web browser with TensorFlow.js
- 35 Recognize custom objects with TensorFlow.js.
- 36 5.3: Using layers models for transfer learning
- 37 6.1: Using models from Python in the web browser with TensorFlow.js
- 38 6.2: Converting Python saved models with the TensorFlow.js command line converter
- 39 6.3: Natural language processing (NLP) - understanding written text
- 40 6.4.1: Using a natural language model: Comment spam detection - setting up the web scaffolding
- 41 6.4.2: Using a natural language model: Comment spam detection - loading a pretrained NLP model
- 42 6.4.3: Using a natural language model: Comment spam detection - word tokenization
- 43 6.4.4: Using a natural language model: Comment spam detection - web sockets
- 44 6.5: Dealing with edge cases in spam detection
- 45 6.6: Using a retrained spam detection model in the web browser with TensorFlow.js
- 46 7.1: Machine Learning as a Web Engineer - putting knowledge into practice
- 47 Advanced machine learning for web developers: Autoencoders, GANs, RNNs and more
