Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

By Aurélien Géron. 2017

3,139 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2936cbd6a90d7153a9fa34e8e4fd947907fe7f6c

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Citations in the corpus

DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction (2018, methodology): “For our DAGAN method we applied the early stopping strategy, which can be considered as an additional and efﬁcient regularisation technique to avoid overﬁtting [68].”
Diabetes prediction using machine learning and explainable AI techniques (2022, methodology): “As a result, the random forest can be considered an ensemble learning model [7].”
TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems (2020, methodology): “These categorical values have been converted to consecutive numeric values by applying a label-encoding method [70].”
Deep Learning With TensorFlow: A Review (2019, unknown): “Ge´ron (2017”
Deep Metric Learning: A Survey (2019, background): “Thus, machine learning can be claimed to offer successful solutions for complex problems and large amounts of data [2,3].”
Deep learning for molecular generation and optimization - a review of the state of the art (2019, background): “120,121 An important detail to note however is that the type of RNN unit that is typically used for generating molecules is either the long short term memory (LSTM) unit, 122 or a newer more computationally efﬁcient variant called the gated recurrent unit (GRU).”
Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions (2021, methodology): “CNN also uses a ‘dropout’ [31] that can deal with the problem of over-fitting, which may occur”
Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives (2019, methodology): “In addition to the abovementioned physicsdriven models, data-driven models have been widely used in the field of AM; these models have the unified name of machine learning (ML) [10,11].”
Machine Learning in Aerodynamic Shape Optimization (2022, background): “” ML has emerged as a cuttingedge tool in modern life due to the promising advantages over traditional programming techniques [115].”
Machine learning–enabled high-entropy alloy discovery (2022, background): “Supplementary materials: Methods Figures S1 to S17 Tables S1 to S15 References (40-56)”
A Gentle Introduction to Deep Learning in Medical Image Processing (2018, background): “tutorials and reviews [9, 10, 11, 12, 13, 14, 15, 16, 17, 18] over blog posts and jupyter notebooks [19, 20, 21] to entire books [22, 23, 24, 25].”