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Course
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Building and Deploying Keras Models in a Multi-cloud Environment
Deep learning is merged into the normal operations of many companies due to the availability of huge repositories of data and easy to develop learning frameworks. Here, you'll use Keras to develop one such network or implement into your own model.
What you'll learn
As machine learning and deep learning techniques become popular, the importance of intuitive and simple abstractions that enable fast development and quick prototyping of these models become critical. In this course, Building and Deploying Keras Models in a Multi-cloud Environment, you'll learn the simple and intuitive functions and classes that Keras offers to build neural network models. First, you'll gain an understanding of the basic working of a neuron and how neural networks are structured and trained. You'll study the simplest form of a model, a network for linear regression which can be built using the simple Sequential model class in Keras, along with other forms of Sequential models such as convolutional neural networks for image classification. Next, you'll move on to recurrent neural networks and understand their ability to store state using outputs from previous time instances, and build a sequence-to-sequence RNN for language translation from English to French using Keras' functional API. Lastly, you'll learn to build and train these models on the most popular cloud platforms, Azure, AWS and the GCP. You'll study their IaaS and PaaS offerings for machine learning and use deep learning VMs or the distributed training framework to train our models. By the end of this course, you will be very comfortable using the Keras high-level API to build your machine learning models and know how you can take these models to the cloud for training at scale.
Table of contents
- Version Check | 20s
- Module Overview | 1m 19s
- Prerequisites and Course Outline | 1m 57s
- Neurons and Neural Networks | 5m 26s
- Introducing Keras | 2m 28s
- Demo: Installing TensorFlow and Keras | 1m 44s
- Working with Sequential Models | 4m 25s
- Training a Neural Network: Gradient Descent Optimization and Back Propagation | 3m 55s
- Saving Models | 1m 25s
- Demo: Sequential Model for Linear Regression | 5m 39s
- Demo: Classification Using the Iris Dataset - Data Preparation and One Hot Encoding | 4m 34s
- Demo: Sequential Model for Classification and Saving to Disk | 5m 43s
- Demo: Loading a Saved Model | 1m 47s
- CNNs and the Local Receptive Field | 1m 36s
- Convolution | 2m 27s
- Feature Maps and the Convolutional Layer | 3m 30s
- Pooling | 2m 34s
- CNN Architectures | 1m 36s
- Demo: CNN for Image Classification - Cat and Dog | 4m 42s
- Demo: Training A CNN | 3m 19s
- Demo: Classification Using a CNN | 2m 13s
- Demo: Visualizing NNs Using GraphViz and Quiver | 4m 10s
About the author
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.
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