Modeling 3 Flashcards

Question

a use case for Lex?

Answer 1

making an amazon alexa use Transcribe to convert voice to text use Lex to extract the intents use polly to return a voice to user

Answer 2

AWS mobile SDK Facebook Messenger Slack Twilio

Answer 3

collaborative filtering engine recommender system feed in data about a user, in return it gives you what other stuff this user might be interested in

Answer 4

Optical Character Recognition (OCR) supports table, forms, fields

Answer 5

Reinforcement learning powered 1/18 scale race car

Answer 6

Deep learning-enabled video camera integrated with rekognition, SageMaker, Polly, Tensorflow, MXNet and Caffe

Answer 7

Kinesis Video Streams

Answer 8

learning about an environment and how to navigate in an optimal manner as you encounter different states within that environment There are: - Environment: Layout e.g. Board/ maze - Choices (actions) - Conditions (states) - Rewards: values associated with the action from state - Observation: i.e., surroundings in a maze, state of chess board keep track of reward or penalty associated with each action given a condition use those values to inform its future choices

Answer 9

Markov Decision Process mathematical framework for modeling decision making MDP is a discrete time stochastic control process

Answer 10

yes it does it uses a deep learning framework with Tensorflow and MXNet supports Intel Coach and Ray Rllib toolkits

Answer 11

``` MATLAB Simulink EnergyPlus RoboSchool PyBullet Amazon Sumerian AWS RoboMaker ```

Answer 12

yes it does it contribute training and/or environment rollout Multi-core and multi-instance

Answer 13

parameters are abstracted hyperparameter tuning in SageMaker can then optimize them

Answer 14

no specific guidance given by aws it's deep learning though, GPU might be helpful supports multiple instances and cores

Answer 15

# define the hyperparameters we care about and their ranges we assume is good to try and the metrics we are optimizing for e. g. - learning rate - batch size - depth - etc

Answer 16

It spins up a "HyperParameter Tuning Job" and train as many as combinations that we allow potentially a lot if instances are spun up It also learns as it goes so it doesn't have to try every possible combination

Answer 17

don't optimize too many hyperparameters at once limit ranges to as small a range as possible use logarithmic scales when appropriate don't run too many training jobs concurrently - it limits how well the process can learn as it goes make sure the training jobs running on multiple instances report the correct objective metric in the end

Answer 18

yes there is a SageMaker library that you can use in a spark driver script instead of spark mllib implementation, use SageMaker Estimator e.g. XGBoost, PCA, K-mean

Answer 19

connect notebook to a remote EMR running spark or use Zeppelin Training df should have: - features column that is a vector of doubles - optional labels column of doubles fit on SageMaker estimator and get a SageMaker model transform on SageMakerModel to make inferences work with Spark Pipelines

Answer 20

combine pre-processing big data in spark with training and inference in SageMaker

Answer 21

Whether it's your own code, a built-in algorithm from SageMaker, or a model you've purchased in the marketplace - all training code deployed to SageMaker training instances come from ECR

Answer 22

Latent Dirichlet Allocation is a topic modeling technique. Neural Topic Model would also be a correct answer.

Modeling 3 Flashcards

(46 cards)