pdfcoffee

optimizers
reference link 17, 27
testing 26-32
output
predicting 45
P
paragraph embedding 262, 264
Paragraph Vectors - Distributed Bag
of Words (PV-DBOW) 264
Paragraph Vectors - Distributed Memory
(PV-DM) 264
paraphrase database (PPDB) 247
Part-of-Speech (POS) analysis 175
peephole LSTM 288, 289
perceptron 6, 7
placeholder
about 55
defining 58
plot command 383
policy-based methods 412
pooling layers, DCNN
about 113
average pooling 113
max pooling 113
pose estimation
reference link 471
Positive Rate (TPR) 506
POS tagging 307-316
prebuilt deep learning models
recycling, for feature extraction 136, 137
pre-trained models, TensorFlow.js
BodyPix, reference link 486
Coco SSD, reference link 486
DeepLab v3, reference link 486
KNN Classifier, reference link 487
MobileNet, reference link 486
PoseNet, reference link 486
Speech Commands, reference link 487
Toxicity 487
Universal Sentence Encoder, reference
link 487
pre-trained models, TensorFlow Lite
image classification 468, 470
mobile GPUs 473
object detection 468, 471
pose estimation 468, 471
question and answer 468, 472
reference link 468
segmentations 468, 471
smart reply 468, 471
style transfers 468, 471
text classification 468, 472
pretrained TPU models
using 584, 585
principal component analysis (PCA)
about 375, 376
implementing, on MNIST dataset 376-378
k-means clustering 380, 381
reference link 379
TensorFlow embeddings API 379
principal components 375
principles, reinforcement learning (RL)
goal 407
interaction, with environment 407
trial and error 407
Prioritized Experience Replay (PER) 414
PyTorch
reference link 1, 272
Q
quantization
about 462
post-training quantization 462
quantization-aware training 462
R
ragged tensors 74
rainbow 434
Recurrent Neural Networks (RNN) 279
Region of Interest (ROI) 144
Region Proposal Network (RPN) 144
regression 87, 88
regularization
about 36
adoption, for avoiding overfitting 36-38
BatchNormalization 38
elastic regularization 38
L1 regularization (LASSO) 38
L2 regularization (Ridge) 38
[ 605 ]