Skip to content

Fixing typos #142

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 2 commits into
base: master
Choose a base branch
from
Open

Fixing typos #142

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
1b59cce
detetion -> detection
aausch Aug 4, 2023
8dc09a1
occurances - occurences
aausch Aug 4, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions lab2/Part2_FaceDetection.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -515,7 +515,7 @@
"source": [
"## 2.5 Semi-supervised variational autoencoder (SS-VAE)\n",
"\n",
"Now, we will use the general idea behind the VAE architecture to build a model to automatically uncover (potentially) unknown biases present within the training data, while simultaneously learning the facial detection task. This draws direct inspiration from [a recent paper](http://introtodeeplearning.com/AAAI_MitigatingAlgorithmicBias.pdf) proposing this as a general approach for automatic bias detetion and mitigation.\n"
"Now, we will use the general idea behind the VAE architecture to build a model to automatically uncover (potentially) unknown biases present within the training data, while simultaneously learning the facial detection task. This draws direct inspiration from [a recent paper](http://introtodeeplearning.com/AAAI_MitigatingAlgorithmicBias.pdf) proposing this as a general approach for automatic bias detection and mitigation.\n"
]
},
{
Expand Down Expand Up @@ -638,7 +638,7 @@
" # Build the decoder network using the Sequential API\n",
" decoder = tf.keras.Sequential([\n",
" # Transform to pre-convolutional generation\n",
" Dense(units=4*4*6*n_filters), # 4x4 feature maps (with 6N occurances)\n",
" Dense(units=4*4*6*n_filters), # 4x4 feature maps (with 6N occurences)\n",
" Reshape(target_shape=(4, 4, 6*n_filters)),\n",
"\n",
" # Upscaling convolutions (inverse of encoder)\n",
Expand Down