Keras is a neural network API written in Python and integrated with TensorFlow. You can learn how to use Keras in a new video course on the YouTube channel.

In this course from deeplizard, you will learn how to prepare and process data for artificial neural networks, build and train  artificial neural networks from scratch, build and train convolutional neural networks (CNNs), implement fine-tuning and transfer learning, and more.

Each section of the course focuses on a specific concept, and shows how the full implementation is done in code using Keras and Python.

You will learn to build some networks from scratch. Others will be pre-trained state-of-the-art models that you'll get to fine-tune to the data. Then you'll learn how to deploy models using both front-end and back-end deployment techniques.

Here's the full course syllabus:

Part 1: Artificial Neural Network Basics

Section 1: Intro to Keras and neural networks

  • Processing data
  • Building and training neural networks
  • Validation and inference
  • Saving and loading models

Section 2: Convolutional Neural Networks (CNNs)

  • Image processing
  • Building and training CNNs
  • Using CNNs for inference

Section 3: Fine-tuning and transfer learning

  • Intro to fine-tuning and VGG16 model
  • Implement fine-tuning on VGG16 model
  • Using fine-tuned models for inference
  • Intro to MobileNet
  • Fine-tuning MobileNet on subset of data

Section 4: Additional topics

  • Data augmentation
  • Keras' image labeling implementation
  • Achieving reproducible results
  • Learnable parameters

Part 2: Neural network model deployment

Section 1: Deployment with Flask

  • Introduction to Flask and web services
  • Build a simple Flask app and web app
  • Send and receive data with Flask
  • Host neural network with Flask
  • Build neural network web app to interact with Flask service
  • Integrating data visualization with D3, DC, Crossfilter
  • Alternative ways to access neural network from Powershell and Curl
  • Information privacy and data protection

Section 2: Deployment with TensorFlow.js

  • Introduction to client-side neural networks
  • Convert Keras model to TFJS model
  • Set up Node.js and Express
  • Build UI for neural network web app
  • Host a neural network with TFJS
  • Explore tensor operations through image processing
  • Examine tensor operations with debugger
  • Broadcasting tensors
  • Efficiency of hosting MobileNet in the browser

You can watch the full course on the YouTube channel (3 hour watch).