EENG 427 — INTRODUCTION TO DEEP NEURAL NETWORKS and INTRODUCTION TO DEEP NEURAL NETWORKS LAB
Learning Objectives & Matches
By the end of this class you should be able to:
Compile, administer, and grade examinations, or assign this work to others.
Compile, administer, and grade examinations, or assign this work to others.
Compile, administer, and grade examinations, or assign this work to others.
Compile, administer, and grade examinations, or assign this work to others.
Compile, administer, and grade examinations, or assign this work to others.
Compile, administer, and grade examinations, or assign this work to others.
Compile, administer, and grade examinations, or assign this work to others.
Compile, administer, and grade examinations, or assign this work to others.
Compile, administer, and grade examinations, or assign this work to others.
Compile, administer, and grade examinations, or assign this work to others.
Analyze simple/shallow NNs (e.g., simple/shallow perceptrons, feedforward NNs)
Break systems into their components, assign numerical values to each component, and examine the mathematical relationships between them.
Interpret the results of all methods of non-destructive testing (NDT), such as acoustic emission, electromagnetic, leak, liquid penetrant, magnetic particle, neutron radiographic, radiographic, thermal or infrared, ultrasonic, vibration analysis, and visual testing.
Understand design issues related to underfitting/overfitting and vanishing/exploding gradient problems.
Troubleshoot new or existing product problems involving designs, materials, or processes.
Modify and revise designs to correct operating deficiencies or to reduce production problems.
Adjust and correct machine set-ups to reduce thicknesses, reshape products, and eliminate product defects.
Investigate or resolve operational problems, such as material use variances or bottlenecks.
Identify quality problems and recommend solutions.
Instruct vendors or contractors on quality guidelines, testing procedures, or ways to eliminate deficiencies.
Resolve problems concerning transportation, logistics systems, imports or exports, or customer issues.
Provide feedback to design engineers on customer problems or needs.
Inspect finished dies for smoothness, contour conformity, and defects.
Determine load weights and check them against lifting capacities to prevent overload.
Apply backpropagation (BP) to a DNN.
Take dictation using shorthand, a stenotype machine, or headsets and transcribing machines.
Understand advanced learning algorithms such as SGD, AdaGrad, Adam etc.
Develop or apply data mining and machine learning algorithms.
Arrange for lectures by experts in designated fields.
Help clients improve decision making, abstract reasoning, memory, sequencing, coordination, and perceptual skills, using computer programs.
Develop alternative training methods if expected improvements are not seen.
Design and apply bioinformatics algorithms including unsupervised and supervised machine learning, dynamic programming, or graphic algorithms.
Prepare and deliver lectures to undergraduate or graduate students on topics such as linear algebra, differential equations, and discrete mathematics.
Formulate and implement training programs, applying principles of learning and individual differences.
Train users and answer questions.
Train users and answer questions.
Train subordinates in programming and program coding.
Train a DNN using various learning algorithms and validate/test the trained DNN to a large data set (e.g., MNIST, ImageNet, CIFAR10/100, VoxCeleb etc.)
Participate in internal or external training programs to maintain knowledge of validation principles, industry trends, or novel technologies.
Build models for algorithm or control feature verification testing.
Validate known intelligence with data from other sources.
Set up, program, or record montages or electrical combinations when testing peripheral nerve, spinal cord, subcortical, or cortical responses.
Administer oral, written, or performance tests to measure progress and to evaluate training effectiveness.
Participate in conferences, seminars, and training sessions to keep abreast of developments in the field, and integrate relevant information into training programs.
Analyze large molecular datasets, such as raw microarray data, genomic sequence data, or proteomics data, for clinical or basic research purposes.
Provide consultation or training on topics such as mechatronics or automated control.
Maintain validation test equipment.
Check source data to verify completeness and accuracy.
Apply a DNN to a real-world large data set.
Process large amounts of data for statistical modeling and graphic analysis, using computers.
Take dictation using shorthand, a stenotype machine, or headsets and transcribing machines.
Write and execute scripts to automate tasks, such as parsing large data files.
Validate known intelligence with data from other sources.
Back up network data.
Gather records pertinent to specific problems, review them for completeness and accuracy, and attach records to correspondence as necessary.
Build and update digital databases.
Input data into databases.
Analyze, manipulate, or process large sets of data using statistical software.
Check source data to verify completeness and accuracy.