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Quick and Easy Application of Network Graph Analysis: Measure Connectivity Between Countries by Air Traffic

Towards AI

The Louvain algorithm ([link] is useful in this case to correctly identify clusters that correlate to the continents of the countries, with some exceptions that can be explained by looking at the flight routes. deg_cent = nx.degree_centrality(graph)cent_array = np.fromiter(deg_cent.values(), float)pd.DataFrame(pd.Series(deg_cent) ).sort_values(0,

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Structural Evolutions in Data

O'Reilly Media

” Consider the structural evolutions of that theme: Stage 1: Hadoop and Big Data By 2008, many companies found themselves at the intersection of “a steep increase in online activity” and “a sharp decline in costs for storage and computing.” A basic, production-ready cluster priced out to the low-six-figures.

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[Latest] 20+ Top Machine Learning Projects with Source Code

Mlearning.ai

Source code projects provide valuable hands-on experience and allow you to understand the intricacies of machine learning algorithms, data preprocessing, model training, and evaluation. We have the IPL data from 2008 to 2017. We will also be building a beautiful-looking interactive Flask model. Checkout the code walkthrough [link] 13.

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[Latest] 20+ Top Machine Learning Projects for final year

Mlearning.ai

HOGs are great feature detectors and can also be used for object detection with SVM but due to many other State of the Art object detection algorithms like YOLO, and SSD , present out there, we don’t use HOGs much for object detection. We have the IPL data from 2008 to 2017. Checkout the code walkthrough [link] 13.

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70+ Best and Unique Python Machine Learning Projects with source code [2023]

Mlearning.ai

HOGs are great feature detectors and can also be used for object detection with SVM but due to many other State of the Art object detection algorithms like YOLO, SSD, present out there, we don’t use HOGs much for object detection. We have the IPL data from 2008 to 2017. This is going to be a very easy and fun project.

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Zero-shot prompting for the Flan-T5 foundation model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

JumpStart is the machine learning (ML) hub of Amazon SageMaker that offers a one-click access to over 350 built-in algorithms; pre-trained models from TensorFlow, PyTorch, Hugging Face, and MXNet; and pre-built solution templates. He focuses on developing scalable machine learning algorithms.

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Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

As an example, in the following figure, we separate Cover 3 Zone (green cluster on the left) and Cover 1 Man (blue cluster in the middle). We design an algorithm that automatically identifies the ambiguity between these two classes as the overlapping region of the clusters. probability and Cover 1 Man with 31.3%

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