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Fixing typos #142

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Fixing typos #142

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1b59cce
detetion -> detection
aausch Aug 4, 2023
8dc09a1
occurances - occurences
aausch Aug 4, 2023
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4 changes: 2 additions & 2 deletions lab2/Part2_FaceDetection.ipynb
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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