description Description

Price‌ ‌-‌ ‌Paid:‌ 
7410‌ 
 
JobsForHer‌ ‌Offer:‌ 
0‌ 
 
Description:‌ 
The‌ ‌goal‌ ‌of‌ ‌this‌ ‌course‌ ‌is‌ ‌to‌ ‌give‌ ‌learners‌ ‌basic‌ ‌understanding‌ ‌of‌ ‌modern‌ ‌neural‌ ‌networks‌ ‌and‌ 
their‌ ‌applications‌ ‌in‌ ‌computer‌ ‌vision‌ ‌and‌ ‌natural‌ ‌language‌ ‌understanding.‌ ‌The‌ ‌course‌ ‌starts‌ ‌with‌ 
a‌ ‌recap‌ ‌of‌ ‌linear‌ ‌models‌ ‌and‌ ‌discussion‌ ‌of‌ ‌stochastic‌ ‌optimization‌ ‌methods‌ ‌that‌ ‌are‌ ‌crucial‌ ‌for‌ 
training‌ ‌deep‌ ‌neural‌ ‌networks.‌ ‌Learners‌ ‌will‌ ‌study‌ ‌all‌ ‌popular‌ ‌building‌ ‌blocks‌ ‌of‌ ‌neural‌ ‌networks‌ 
including‌ ‌fully‌ ‌connected‌ ‌layers,‌ ‌convolutional‌ ‌and‌ ‌recurrent‌ ‌layers.‌  
 
Learners‌ ‌will‌ ‌use‌ ‌these‌ ‌building‌ ‌blocks‌ ‌to‌ ‌define‌ ‌complex‌ ‌modern‌ ‌architectures‌ ‌in‌ ‌TensorFlow‌ 
and‌ ‌Keras‌ ‌frameworks.‌ ‌In‌ ‌the‌ ‌course‌ ‌project‌ ‌learner‌ ‌will‌ ‌implement‌ ‌deep‌ ‌neural‌ ‌network‌ ‌for‌ ‌the‌ 
task‌ ‌of‌ ‌image‌ ‌captioning‌ ‌which‌ ‌solves‌ ‌the‌ ‌problem‌ ‌of‌ ‌giving‌ ‌a‌ ‌text‌ ‌description‌ ‌for‌ ‌an‌ ‌input‌ 
image.‌ 
 
The‌ ‌prerequisites‌ ‌for‌ ‌this‌ ‌course‌ ‌are:‌  
1)‌ ‌Basic‌ ‌knowledge‌ ‌of‌ ‌Python.‌ 
2)‌ ‌Basic‌ ‌linear‌ ‌algebra‌ ‌and‌ ‌probability.‌ 
 
Please‌ ‌note‌ ‌that‌ ‌this‌ ‌is‌ ‌an‌ ‌advanced‌ ‌course‌ ‌and‌ ‌we‌ ‌assume‌ ‌basic‌ ‌knowledge‌ ‌of‌ ‌machine‌ 
learning.‌ ‌You‌ ‌should‌ ‌understand:‌ 
1)‌ ‌Linear‌ ‌regression:‌ ‌mean‌ ‌squared‌ ‌error,‌ ‌analytical‌ ‌solution.‌ 
2)‌ ‌Logistic‌ ‌regression:‌ ‌model,‌ ‌cross-entropy‌ ‌loss,‌ ‌class‌ ‌probability‌ ‌estimation.‌ 
3)‌ ‌Gradient‌ ‌descent‌ ‌for‌ ‌linear‌ ‌models.‌ ‌Derivatives‌ ‌of‌ ‌MSE‌ ‌and‌ ‌cross-entropy‌ ‌loss‌ ‌functions.‌ 
4)‌ ‌The‌ ‌problem‌ ‌of‌ ‌overfitting.‌ 
5)‌ ‌Regularization‌ ‌for‌ ‌linear‌ ‌models.‌ 
 
Duration:‌ ‌6‌ ‌weeks‌ 
 
 
Price:‌ ‌‌INR‌ ‌7410‌ 
 
JobsForHer‌ ‌Price:‌ ‌FREE‌  
 
By‌ ‌National‌ ‌Research‌ ‌University‌ ‌Higher‌ ‌School‌ ‌of‌ ‌Economics‌ 
 
 
 
 
 
Apply‌ ‌to‌ ‌this‌ ‌course‌ ‌now‌ ‌and‌ ‌stand‌ ‌a‌ ‌chance‌ ‌to‌ ‌win‌ ‌it‌ ‌for‌ ‌FREE!‌  
 
Hurry,‌ ‌last‌ ‌date‌ ‌is‌ ‌July‌ ‌25th!‌