book "Deep Learning with Python"
E435215
"Deep Learning with Python" is a practical, example-driven book that introduces deep learning concepts and techniques using the Keras library and the Python ecosystem.
All labels observed (1)
| Label | Occurrences |
|---|---|
| book "Deep Learning with Python" canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4390926 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: book "Deep Learning with Python" Context triple: [François Chollet, knownFor, book "Deep Learning with Python"]
-
A.
Deep Learning (book)
Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
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B.
Deeplearning.ai
Deeplearning.ai is an online education company specializing in artificial intelligence and deep learning courses and resources.
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C.
Keras
Keras is a high-level neural networks API written in Python that simplifies building, training, and deploying deep learning models, often running on top of frameworks like TensorFlow.
-
D.
book "Probabilistic Robotics"
"Probabilistic Robotics" is a foundational textbook that systematically introduces probabilistic methods for perception, localization, and control in mobile robotics.
-
E.
Book Lambda
Book Lambda is a central section of Aristotle’s Metaphysics that focuses on the nature of substance, causation, and the unmoved mover as the ultimate principle of reality.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: book "Deep Learning with Python" Target entity description: "Deep Learning with Python" is a practical, example-driven book that introduces deep learning concepts and techniques using the Keras library and the Python ecosystem.
-
A.
Deep Learning (book)
Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
-
B.
Deeplearning.ai
Deeplearning.ai is an online education company specializing in artificial intelligence and deep learning courses and resources.
-
C.
Keras
Keras is a high-level neural networks API written in Python that simplifies building, training, and deploying deep learning models, often running on top of frameworks like TensorFlow.
-
D.
book "Probabilistic Robotics"
"Probabilistic Robotics" is a foundational textbook that systematically introduces probabilistic methods for perception, localization, and control in mobile robotics.
-
E.
Book Lambda
Book Lambda is a central section of Aristotle’s Metaphysics that focuses on the nature of substance, causation, and the unmoved mover as the ultimate principle of reality.
- F. None of above. chosen
Statements (44)
| Predicate | Object |
|---|---|
| instanceOf |
book
ⓘ
book edition ⓘ |
| approach |
example-driven
ⓘ
hands-on ⓘ |
| author | François Chollet ⓘ |
| countryOfPublication |
United States
ⓘ
linked to:
United States of America
|
| covers |
best practices for model evaluation
ⓘ
convolutional neural networks ⓘ optimization for deep learning ⓘ recurrent neural networks ⓘ regularization techniques ⓘ |
| firstEditionPublicationYear | 2017 ⓘ |
| focusesOn |
conceptual understanding
ⓘ
practical applications ⓘ |
| genre |
computer science book
ⓘ
technical literature ⓘ |
| hasEdition |
Deep Learning with Python, Second Edition
ⓘ
linked to:
"Deep Learning with Python"
|
| intendedPrerequisites |
basic Python knowledge
ⓘ
basic linear algebra ⓘ |
| language | English ⓘ |
| libraryUsed | Keras ⓘ |
| mediaType |
eBook
ⓘ
print ⓘ |
| notableFor | popularizing Keras for practitioners ⓘ |
| programmingLanguage | Python ⓘ |
| publicationYear | 2021 ⓘ |
| publisher | Manning Publications ⓘ |
| subject |
Keras
ⓘ
TensorFlow 2 ⓘ
linked to:
TensorFlow
artificial intelligence ⓘ deep learning ⓘ machine learning ⓘ neural networks ⓘ |
| targetAudience |
data scientists
ⓘ
machine learning practitioners ⓘ software developers ⓘ |
| teaches |
Keras API
ⓘ
linked to:
Keras
building neural networks ⓘ computer vision with deep learning ⓘ text and sequence processing with deep learning ⓘ training deep learning models ⓘ |
| uses |
NumPy
ⓘ
Python scientific stack ⓘ TensorFlow ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: book "Deep Learning with Python" Description of subject: "Deep Learning with Python" is a practical, example-driven book that introduces deep learning concepts and techniques using the Keras library and the Python ecosystem.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.