1
CS 7643 QUIZ 3 EXAM NEWEST VERSION -2025/2026- 100+
QUESTIONS AND VERIFIED ANSWERS 100% CORRECT
GUARANTEED SUCCESS
Inception Net
Deeper and more complex than VGGNet
Average Pooling before FC Layer
Repeated blocks that are repeated over again to form NN
Blocks are made of simple layers, FC, Conv, MaxPool, and softmax
Parallel filters of different sizes to get features at multiple scales
Critical Development: Blocks of parallel paths
Uses Network In Network concept i.e 1x1 Convolution -sort of Dimensionality
reduction see slide
Optimization Error
Even if your NN can perfectly model the world, your algo may not find good
weights that model the function.
When model complexity increases, modeling error reduces, but optimization error
increases.
Effectiveness of transfer learning under certain conditions
Remove last FC layer of CNN and initialize it randomly, then run new data through
network to train only that layer
In order to train the NN for transfer learning -freeze the CNN layers or early layers
and learn parameters in the FC layers.
Performs very well on very small amount of training, if similar to the original data
Does not work very well if the target task's dataset is very different
, 2
If you have enough data in the target domain, and is different than the source,
better to just train on the new data
Transfer learning = reuse features we learn on a very large dataset on a completely
new thing
Steps:
Train on very large dataset
Take custom dataset and initialize network with weights trained in Step 1 (replace
last fully connected layer since classes in new network will be different)
Final step -> continue training on new dataset
Can either retrain all weights ("finetune") or freeze (ie: not update) weights in
certain layers (freezing reduces number of parameters that you need to learn)
AlexNet
2x(CONV=>MAXPOOL=>NORM)=>3xCONV=>MAXPOOL=>3xFC
ReLU, specialized normalization layers, PCA-based data augmentation, Dropout,
Ensembling (used 7 NN with different random weights)
Critical development: More depth and ReLU
VGGNet
2x(2xCONV=>POOL)=>3x(3xCONV=>POOL)=>3xFC
Repeated Application of 3x3 Conv (stride of 1, padding of) & 2x2 Max Pooling
(stride 2) blocks
Very large number of parameters (most in FC) layers, most memory in Conv Layers
(you are storing activation produced in forward pass)
Critical Development: Blocks of repeated structures
CS 7643 QUIZ 3 EXAM NEWEST VERSION -2025/2026- 100+
QUESTIONS AND VERIFIED ANSWERS 100% CORRECT
GUARANTEED SUCCESS
Inception Net
Deeper and more complex than VGGNet
Average Pooling before FC Layer
Repeated blocks that are repeated over again to form NN
Blocks are made of simple layers, FC, Conv, MaxPool, and softmax
Parallel filters of different sizes to get features at multiple scales
Critical Development: Blocks of parallel paths
Uses Network In Network concept i.e 1x1 Convolution -sort of Dimensionality
reduction see slide
Optimization Error
Even if your NN can perfectly model the world, your algo may not find good
weights that model the function.
When model complexity increases, modeling error reduces, but optimization error
increases.
Effectiveness of transfer learning under certain conditions
Remove last FC layer of CNN and initialize it randomly, then run new data through
network to train only that layer
In order to train the NN for transfer learning -freeze the CNN layers or early layers
and learn parameters in the FC layers.
Performs very well on very small amount of training, if similar to the original data
Does not work very well if the target task's dataset is very different
, 2
If you have enough data in the target domain, and is different than the source,
better to just train on the new data
Transfer learning = reuse features we learn on a very large dataset on a completely
new thing
Steps:
Train on very large dataset
Take custom dataset and initialize network with weights trained in Step 1 (replace
last fully connected layer since classes in new network will be different)
Final step -> continue training on new dataset
Can either retrain all weights ("finetune") or freeze (ie: not update) weights in
certain layers (freezing reduces number of parameters that you need to learn)
AlexNet
2x(CONV=>MAXPOOL=>NORM)=>3xCONV=>MAXPOOL=>3xFC
ReLU, specialized normalization layers, PCA-based data augmentation, Dropout,
Ensembling (used 7 NN with different random weights)
Critical development: More depth and ReLU
VGGNet
2x(2xCONV=>POOL)=>3x(3xCONV=>POOL)=>3xFC
Repeated Application of 3x3 Conv (stride of 1, padding of) & 2x2 Max Pooling
(stride 2) blocks
Very large number of parameters (most in FC) layers, most memory in Conv Layers
(you are storing activation produced in forward pass)
Critical Development: Blocks of repeated structures
