Deep Learning and Modern ML Recognition
Understand neural network basics and key deep learning architectures like CNNs and RNNs. Learn how to choose between deep learning and traditional ML for real-world AWS applications using SageMaker and managed AI services. This lesson equips you to design appropriate ML solutions based on data types and problem complexity.
We'll cover the following...
Deep learning is a critical branch of machine learning that every AWS ML engineer must understand, both for the certification exam and for real-world architecture decisions. As a subset of machine learning, deep learning uses multilayer neural networks to learn complex, hierarchical patterns directly from raw data. This capability has made it indispensable for modern workloads involving unstructured data, such as images, text, and audio, where traditional feature engineering falls short.
On AWS, two categories of services support deep learning workloads. Amazon SageMaker provides the infrastructure for custom model development, allowing engineers to build, train, and deploy neural networks using frameworks like TensorFlow and PyTorch on GPU-accelerated instances, such as P3 and P4. In contrast, managed AI services like Amazon Rekognition (computer vision) and Amazon Comprehend (natural language processing) expose pretrained deep learning models through simple API calls and require no model development. Use SageMaker to build custom neural networks, and use AI services for ready-to-use models for standard tasks.
This lesson walks through neural network fundamentals, how to choose between CNNs and RNNs, a decision framework for deep learning vs. traditional ML, and real-world use cases mapped to AWS services.
Neural network fundamentals
A neural network consists of three structural components: an input layer that receives raw data, one or more hidden layers that transform the data, and an output layer that produces predictions. Each neuron in a layer receives inputs, multiplies them by learned weights, adds a bias term, and passes the result through an
How neural networks learn
Training proceeds through two complementary processes. Forward propagation computes predictions by passing data sequentially through each layer. The network then calculates a loss value that measures prediction error. Backpropagation flows backward through the network, computing gradients of the loss with respect to each weight and updating those weights using an optimization algorithm like stochastic gradient descent. The update rule follows:
where