"Deep Learning with Python"
E435216
"Deep Learning with Python" is a practical book that introduces deep learning concepts and techniques using the Keras library and the Python ecosystem, aimed at helping developers and researchers build and understand modern neural networks.
All labels observed (1)
| Label | Occurrences |
|---|---|
| "Deep Learning with Python" canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T4390938 — 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: "Deep Learning with Python" Context triple: [François Chollet, authored, "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.
-
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.
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D.
Very Deep Convolutional Networks for Large-Scale Image Recognition
"Very Deep Convolutional Networks for Large-Scale Image Recognition" is the influential 2014 research paper that introduced the VGG family of deep convolutional neural network architectures, demonstrating that significantly increasing network depth with small convolutional filters leads to substantial improvements in image classification performance.
-
E.
“Large-Scale Machine Learning with Stochastic Gradient Descent”
“Large-Scale Machine Learning with Stochastic Gradient Descent” is a widely cited work by Léon Bottou that analyzes and advocates stochastic gradient descent as an efficient optimization method for large-scale machine learning problems.
- 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: "Deep Learning with Python" Target entity description: "Deep Learning with Python" is a practical book that introduces deep learning concepts and techniques using the Keras library and the Python ecosystem, aimed at helping developers and researchers build and understand modern neural networks.
-
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.
Very Deep Convolutional Networks for Large-Scale Image Recognition
"Very Deep Convolutional Networks for Large-Scale Image Recognition" is the influential 2014 research paper that introduced the VGG family of deep convolutional neural network architectures, demonstrating that significantly increasing network depth with small convolutional filters leads to substantial improvements in image classification performance.
-
E.
“Large-Scale Machine Learning with Stochastic Gradient Descent”
“Large-Scale Machine Learning with Stochastic Gradient Descent” is a widely cited work by Léon Bottou that analyzes and advocates stochastic gradient descent as an efficient optimization method for large-scale machine learning problems.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
book
ⓘ
computer science book ⓘ non-fiction book ⓘ technical book ⓘ |
| aimsTo |
enable readers to build deep learning models in Python
ⓘ
help readers understand modern neural networks ⓘ |
| approach |
code-centric
ⓘ
example-driven ⓘ |
| author | François Chollet NERFINISHED ⓘ |
| covers |
computer vision applications
ⓘ
convolutional neural networks ⓘ feedforward neural networks ⓘ natural language processing applications ⓘ optimization for deep learning ⓘ recurrent neural networks ⓘ regularization techniques ⓘ training deep neural networks ⓘ |
| ecosystem | Python scientific computing stack ⓘ |
| edition |
first edition
ⓘ
second edition ⓘ |
| explains |
backpropagation
ⓘ
fundamental deep learning concepts ⓘ gradient-based optimization ⓘ representation learning ⓘ |
| firstEditionPublicationYear | 2017 ⓘ |
| focusesOn |
hands-on code examples
ⓘ
practical applications of deep learning ⓘ |
| format |
ebook
ⓘ
print ⓘ |
| language | English ⓘ |
| libraryUsed | Keras NERFINISHED ⓘ |
| marketedAs | practical introduction to deep learning with Keras ⓘ |
| programmingLanguage | Python ⓘ |
| publisher | Manning Publications NERFINISHED ⓘ |
| secondEditionPublicationYear | 2021 ⓘ |
| subject |
artificial intelligence
ⓘ
deep learning ⓘ machine learning ⓘ neural networks ⓘ |
| targetAudience |
data scientists
ⓘ
machine learning practitioners ⓘ researchers ⓘ software developers ⓘ |
| teaches |
how to build neural networks with Keras
ⓘ
how to train and evaluate deep learning models ⓘ |
| uses |
NumPy
NERFINISHED
ⓘ
TensorFlow (as Keras backend) NERFINISHED ⓘ |
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: "Deep Learning with Python" Description of subject: "Deep Learning with Python" is a practical book that introduces deep learning concepts and techniques using the Keras library and the Python ecosystem, aimed at helping developers and researchers build and understand modern neural networks.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.