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Model index

Every artifact here, what it is, and how to load it.

00_ai_universe_overview.png

  • Type: figure from the book
  • Size: 0.51 MB
# image

01_expert_system.png

  • Type: figure from the book
  • Size: 0.04 MB
# image

01_planning_search.png

  • Type: figure from the book
  • Size: 0.03 MB
# image

01x_fuzzy_logic.png

  • Type: figure from the book
  • Size: 0.12 MB
# image

02_ensemble.png

  • Type: figure from the book
  • Size: 0.04 MB
# image

02_kmeans_pca.png

  • Type: figure from the book
  • Size: 0.15 MB
# image

02_ml_classifiers.png

  • Type: figure from the book
  • Size: 0.17 MB
# image

02_pca_tsne.png

  • Type: figure from the book
  • Size: 0.05 MB
# image

02_regression.png

  • Type: figure from the book
  • Size: 0.09 MB
# image

02x_feature_engineering.png

  • Type: figure from the book
  • Size: 0.16 MB
# image

02x_semi_supervised.png

  • Type: figure from the book
  • Size: 0.05 MB
# image

03_activation_functions.png

  • Type: figure from the book
  • Size: 0.13 MB
# image

03_cnn_filters.png

  • Type: figure from the book
  • Size: 0.02 MB
# image

03_lstm_rnn.png

  • Type: figure from the book
  • Size: 0.21 MB
# image

03_mlp_confusion.png

  • Type: figure from the book
  • Size: 0.06 MB
# image

03_mlp_mnist.png

  • Type: figure from the book
  • Size: 0.06 MB
# image

03_perceptron.png

  • Type: figure from the book
  • Size: 0.07 MB
# image

03_som.png

  • Type: figure from the book
  • Size: 0.07 MB
# image

03x_backpropagation.png

  • Type: figure from the book
  • Size: 0.10 MB
# image

04_attention.png

  • Type: figure from the book
  • Size: 0.13 MB
# image

04_cifar10_samples.png

  • Type: figure from the book
  • Size: 0.11 MB
# image

04_dropout.png

  • Type: figure from the book
  • Size: 0.10 MB
# image

04_gan_generated.png

  • Type: figure from the book
  • Size: 0.05 MB
# image

04_gan_loss.png

  • Type: figure from the book
  • Size: 0.04 MB
# image

04_qlearning.png

  • Type: figure from the book
  • Size: 0.09 MB
# image

04_transfer_learning.png

  • Type: figure from the book
  • Size: 0.06 MB
# image

04x_capsnet.png

  • Type: figure from the book
  • Size: 0.06 MB
# image

04x_dbn_filters.png

  • Type: figure from the book
  • Size: 0.17 MB
# image

05_chatbot_similarity.png

  • Type: figure from the book
  • Size: 0.14 MB
# image

05_ngram_lm.png

  • Type: figure from the book
  • Size: 0.05 MB
# image

05_transformer.png

  • Type: figure from the book
  • Size: 0.06 MB
# image

07_mamba_loss.png

  • Type: figure from the book
  • Size: 0.03 MB
# image

07_stretch_goals_summary.png

  • Type: figure from the book
  • Size: 0.24 MB
# image

07_summary_table.png

  • Type: figure from the book
  • Size: 0.26 MB
# image

models/cnn_mnist.keras

  • Type: Keras full model - loads standalone
  • Size: 1.18 MB
from tensorflow import keras
model = keras.models.load_model('models/cnn_mnist.keras')

models/dnn_cifar.keras

  • Type: Keras full model - loads standalone
  • Size: 22.17 MB
from tensorflow import keras
model = keras.models.load_model('models/dnn_cifar.keras')

models/dropout_mnist.keras

  • Type: Keras full model - loads standalone
  • Size: 2.85 MB
from tensorflow import keras
model = keras.models.load_model('models/dropout_mnist.keras')

models/ensemble_voting.skops

  • Type: scikit-learn model (skops, safer than pickle)
  • Size: 20.46 MB
from skops.io import load, get_untrusted_types
u = get_untrusted_types(file='models/ensemble_voting.skops')   # review this list before trusting
model = load('models/ensemble_voting.skops', trusted=u)

models/expert_rules.json

  • Type: configuration / symbolic state
  • Size: 0.00 MB
import json
cfg = json.load(open('models/expert_rules.json'))

models/features_randomforest.skops

  • Type: scikit-learn model (skops, safer than pickle)
  • Size: 3.88 MB
from skops.io import load, get_untrusted_types
u = get_untrusted_types(file='models/features_randomforest.skops')   # review this list before trusting
model = load('models/features_randomforest.skops', trusted=u)

models/fuzzy_controller.json

  • Type: configuration / symbolic state
  • Size: 0.00 MB
import json
cfg = json.load(open('models/fuzzy_controller.json'))

models/gan_discriminator.keras

  • Type: Keras full model - loads standalone
  • Size: 6.43 MB
from tensorflow import keras
model = keras.models.load_model('models/gan_discriminator.keras')

models/hybrid_qnn.npz

  • Type: NumPy archive - trained circuit parameters
  • Size: 0.00 MB
import numpy as np
p = np.load('models/hybrid_qnn.npz')
print(p.files)

models/iris_decisiontree.skops

  • Type: scikit-learn model (skops, safer than pickle)
  • Size: 0.01 MB
from skops.io import load, get_untrusted_types
u = get_untrusted_types(file='models/iris_decisiontree.skops')   # review this list before trusting
model = load('models/iris_decisiontree.skops', trusted=u)

models/iris_knn.skops

  • Type: scikit-learn model (skops, safer than pickle)
  • Size: 0.02 MB
from skops.io import load, get_untrusted_types
u = get_untrusted_types(file='models/iris_knn.skops')   # review this list before trusting
model = load('models/iris_knn.skops', trusted=u)

models/iris_logreg.skops

  • Type: scikit-learn model (skops, safer than pickle)
  • Size: 0.01 MB
from skops.io import load, get_untrusted_types
u = get_untrusted_types(file='models/iris_logreg.skops')   # review this list before trusting
model = load('models/iris_logreg.skops', trusted=u)

models/iris_naivebayes.skops

  • Type: scikit-learn model (skops, safer than pickle)
  • Size: 0.01 MB
from skops.io import load, get_untrusted_types
u = get_untrusted_types(file='models/iris_naivebayes.skops')   # review this list before trusting
model = load('models/iris_naivebayes.skops', trusted=u)

models/iris_randomforest.skops

  • Type: scikit-learn model (skops, safer than pickle)
  • Size: 1.82 MB
from skops.io import load, get_untrusted_types
u = get_untrusted_types(file='models/iris_randomforest.skops')   # review this list before trusting
model = load('models/iris_randomforest.skops', trusted=u)

models/iris_scaler.skops

  • Type: scikit-learn model (skops, safer than pickle)
  • Size: 0.01 MB
from skops.io import load, get_untrusted_types
u = get_untrusted_types(file='models/iris_scaler.skops')   # review this list before trusting
model = load('models/iris_scaler.skops', trusted=u)

models/iris_svm.skops

  • Type: scikit-learn model (skops, safer than pickle)
  • Size: 0.02 MB
from skops.io import load, get_untrusted_types
u = get_untrusted_types(file='models/iris_svm.skops')   # review this list before trusting
model = load('models/iris_svm.skops', trusted=u)

models/kmeans_iris.skops

  • Type: scikit-learn model (skops, safer than pickle)
  • Size: 0.01 MB
from skops.io import load, get_untrusted_types
u = get_untrusted_types(file='models/kmeans_iris.skops')   # review this list before trusting
model = load('models/kmeans_iris.skops', trusted=u)

models/lstm_sine.keras

  • Type: Keras full model - loads standalone
  • Size: 0.26 MB
from tensorflow import keras
model = keras.models.load_model('models/lstm_sine.keras')

models/manifest.json

  • Type: configuration / symbolic state
  • Size: 0.00 MB
import json
cfg = json.load(open('models/manifest.json'))

models/mlp_mnist.keras

  • Type: Keras full model - loads standalone
  • Size: 2.85 MB
from tensorflow import keras
model = keras.models.load_model('models/mlp_mnist.keras')

models/planner_astar.json

  • Type: configuration / symbolic state
  • Size: 0.00 MB
import json
cfg = json.load(open('models/planner_astar.json'))

models/qaoa_maxcut.npz

  • Type: NumPy archive - trained circuit parameters
  • Size: 0.00 MB
import numpy as np
p = np.load('models/qaoa_maxcut.npz')
print(p.files)

models/qgan_generator.npz

  • Type: NumPy archive - trained circuit parameters
  • Size: 0.00 MB
import numpy as np
p = np.load('models/qgan_generator.npz')
print(p.files)

models/qsvm_kernel.npz

  • Type: NumPy archive - trained circuit parameters
  • Size: 0.00 MB
import numpy as np
p = np.load('models/qsvm_kernel.npz')
print(p.files)

models/regression_poly3.skops

  • Type: scikit-learn model (skops, safer than pickle)
  • Size: 0.02 MB
from skops.io import load, get_untrusted_types
u = get_untrusted_types(file='models/regression_poly3.skops')   # review this list before trusting
model = load('models/regression_poly3.skops', trusted=u)

models/semisup_labelspreading.skops

  • Type: scikit-learn model (skops, safer than pickle)
  • Size: 1.09 MB
from skops.io import load, get_untrusted_types
u = get_untrusted_types(file='models/semisup_labelspreading.skops')   # review this list before trusting
model = load('models/semisup_labelspreading.skops', trusted=u)

models/transfer_cifar.keras

  • Type: Keras full model - loads standalone
  • Size: 13.59 MB
from tensorflow import keras
model = keras.models.load_model('models/transfer_cifar.keras')

models/vqc_iris.npz

  • Type: NumPy archive - trained circuit parameters
  • Size: 0.00 MB
import numpy as np
p = np.load('models/vqc_iris.npz')
print(p.files)

models/vqe_ising.npz

  • Type: NumPy archive - trained circuit parameters
  • Size: 0.00 MB
import numpy as np
p = np.load('models/vqe_ising.npz')
print(p.files)