Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

• •Understand industry best-practices for building deep learning applications.
• •Be able to effectively use the common neural network tricks, including initialization, L2 and dropout regularization, Batch normalization, gradient checking.
• •Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
• •Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance.
• •Be able to implement a neural network in TensorFlow.

AWS Fundamentals: Addressing Security Risk

• •Detective controls, including Amazon CloudTrail, AWS Security Hub, Amazon GuardDuty, and AWS Config.
• •Encryption of data at rest, in motion, and best practices for how to store data within and between various AWS services.
• •The AWS Well-Architected Framework and protecting compute resources such as Amazon EC2 and AWS Lambda.

Neural Networks and Deep Learning

• •Understand the major technology trends driving Deep Learning
• •Be able to build, train and apply fully connected deep neural networks
• •Know how to implement efficient (vectorized) neural networks
• •Understand the key parameters in a neural network's architecture

AWS Fundamentals: Migrating to the Cloud

AWS Fundamentals: Building Serverless Applications

• •AWS serverless framework and architecture in the context of a real business problem
• •Provide AWS Lambda, Amazon API Gateway, Amazon DynamoDB, Amazon Lex, and other services needed to deploy serverless solutions.

Sequence Models