29.09.2020, 15:07
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Train and deploy deep learning models
Video: .mp4 (1280x720, 30 fps®) | Audio: aac, 48000 Hz, 2ch | Size: 5.36 GB
Genre: eLearning Video | Duration: 61 lectures (8 hour, 14 mins) | Language: English
Build your neural network using Keras, train it using Google AI-Platform then deploy it using Flask and Google Cloud Run
What you'll learn
Build a deep convolutional neural network using Keras and tensorflow.
Leverage the power of transfer learning to get high accuracy on your classification task.
Use google cloud platform to make training and deploying your deep learning model easy and scalable.
Leverage the power of AI-Platform on Google Cloud Platform to focus on the training of your deep learning model and not on infrastructure.
Containerize your training code and deployment code to make sure your code runs smoothly and everywhere.
How to deploy your deep learning model as a web app using Flask and Cloud Run.
Requirements
Basic knowledge of Python.
Basic knowledge of Keras (although I will be explaining the code thoroughly).
Some knowledge of cloud computing is a plus but not required.
Description
This course will take you through the steps that a machine learning engineer would take to train and deploy a deep learning model. We will start the course by defining an end goal that we want to achieve. Then, we will download a dataset that will help us achieve that goal. We will build a Convolutional Neural Network using Tensorflow with Keras and then we will train this network on Google AI-Platform. After saving the best trained model, we will deploy it as a web app using Flask and Google Cloud Run. Throughout the course, we will be using Docker to containerize our code.
Who this course is for:
Data scientists who want to learn how to leverage cloud computing to train and deploy deep learning model.
Software engineers who want to learn how to train and deploy deep learning models using Keras and tensorflow.
Students who are trying to decide whether to choose data science and machine learning as a possible career.
Hobbyists who want to learn how to build and deploy deep learning models for their DIY or side projects.
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