This course teaches how to use Keras, a Python neural network API, to preprocess data, build and train artificial neural networks, fine-tune convolutional neural networks, implement transfer learning, and deploy models using front-end and back-end techniques. The course covers topics such as data processing, building neural networks, training models, image preparation for CNNs, fine-tuning models, data augmentation, deploying models to Flask web services, and using TensorFlow.js for client-side neural networks. The intended audience for this course is individuals interested in deep learning, neural networks, and deploying machine learning models.
Overview
Syllabus
Keras with TensorFlow Prerequisites - Getting Started With Neural Networks.
TensorFlow and Keras GPU Support - CUDA GPU Setup.
Keras with TensorFlow - Data Processing for Neural Network Training.
Create an Artificial Neural Network with TensorFlow's Keras API.
Train an Artificial Neural Network with TensorFlow's Keras API.
Build a Validation Set With TensorFlow's Keras API.
Neural Network Predictions with TensorFlow's Keras API.
Create a Confusion Matrix for Neural Network Predictions.
Save and Load a Model with TensorFlow's Keras API.
Image Preparation for Convolutional Neural Networks with TensorFlow's Keras API.
Code Update for CNN Training with TensorFlow's Keras API.
Build and Train a Convolutional Neural Network with TensorFlow's Keras API.
Convolutional Neural Network Predictions with TensorFlow's Keras API.
Build a Fine-Tuned Neural Network with TensorFlow's Keras API.
Train a Fine-Tuned Neural Network with TensorFlow's Keras API.
Predict with a Fine-Tuned Neural Network with TensorFlow's Keras API.
MobileNet Image Classification with TensorFlow's Keras API.
Process Images for Fine-Tuned MobileNet with TensorFlow's Keras API.
Fine-Tuning MobileNet on Custom Data Set with TensorFlow's Keras API.
Data Augmentation with TensorFlow's Keras API.
Mapping Keras labels to image classes.
Reproducible results with Keras.
Initializing and Accessing Bias with Keras.
Learnable parameters ("trainable params") in a Keras model.
Learnable parameters ("trainable params") in a Keras Convolutional Neural Network.
Deploy Keras Neural Network to Flask web service | Part 1 - Overview.
Deploy Keras neural network to Flask web service | Part 2 - Build your first Flask app.
Deploy Keras neural network to Flask web service | Part 3 - Send and Receive Data with Flask.
Deploy Keras neural network to Flask web service | Part 4 - Build a front end web application.
Deploy Keras neural network to Flask web service | Part 5 - Host VGG16 model with Flask.
Deploy Keras neural network to Flask web service | Part 6 - Build web app to send images to VGG16.
Deploy Keras neural network to Flask web service | Part 7 - Visualizations with D3, DC, Crossfilter.
Deploy Keras neural network to Flask web service | Part 8 - Access model from Powershell, Curl.
Deploy Keras neural network to Flask web service | Part 9 - Information Privacy, Data Protection.
TensorFlow.js - Introducing deep learning with client-side neural networks.
TensorFlow.js - Convert Keras model to Layers API format.
TensorFlow.js - Serve deep learning models with Node.js and Express.
TensorFlow.js - Building the UI for neural network web app.
TensorFlow.js - Loading the model into a neural network web app.
TensorFlow.js - Explore tensor operations through VGG16 preprocessing.
TensorFlow.js - Examining tensors with the debugger.
Broadcasting Explained - Tensors for Deep Learning and Neural Networks.
TensorFlow.js - Running MobileNet in the browser.
Taught by
deeplizard
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