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[null,"1.","Record Nr.",null,"UNINA9910526689103321"]
[null,null,"Autore",null,"Lee Wei-Meng"]
[null,null,"Titolo",null,"Python machine learning / / Wei-Meng Lee"]
[null,null,"Pubbl/distr/stampa",null,"Indianapolis, Indiana : , : Wiley, , [2019]"]
[{"$nulls":4},"©2019"]
[null,null,"ISBN",null,"1-5231-2845-3"]
[{"$nulls":4},"1-119-54569-2"]
[{"$nulls":4},"1-119-55750-X"]
[{"$nulls":4},"1-119-54567-6"]
[null,null,"Edizione",null,"[1st edition]"]
[null,null,"Descrizione fisica",null,"1 online resource (323 pages)"]
[null,null,"Disciplina",null,"005.133"]
[null,null,"Soggetti",null,"Python (Computer program language)"]
[{"$nulls":4},"Python (Llenguatge de programació)"]
[{"$nulls":4},"Llibres electrònics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,null,"Sommario/riassunto","Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. However, machine learning is not for the faint of heart—it requires a good foundation in statistics, as well as programming knowledge. Python Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today. Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how Python is used to solve the problem at hand. •          Python data science—manipulating data and data visualization •          Data cleansing •          Understanding Machine learning algorithms •          Supervised learning algorithms •          Unsupervised learning algorithms •          Deploying machine learning models Python Machine Learning is essential reading for students, developers, or anyone with a keen interest in taking their coding skills to the next level."]
[null,"2.","Record Nr.",null,"UNISA996635663603316"]
[null,null,"Autore",null,"Antonacopoulos Apostolos"]
[null,null,"Titolo",null,"Pattern Recognition : 27th International Conference, ICPR 2024, Kolkata, India, December 1–5, 2024, Proceedings, Part III / / edited by Apostolos Antonacopoulos, Subhasis Chaudhuri, Rama Chellappa, Cheng-Lin Liu, Saumik Bhattacharya, Umapada Pal"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"9783031781223"]
[{"$nulls":4},"3031781228"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (511 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 15303"]
[null,null,"Disciplina",null,"006.37"]
[null,null,"Soggetti",null,"Computer vision"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Computer Vision"]
[{"$nulls":4},"Machine Learning"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Deep Multi-order Context-aware Kernel Network for Multi-label Classification -- Classifier Enhanced Deep Learning Model for Erythroblast Differentiation with Limited Data -- PiExtract: An End-to-End Data Extraction pipeline for Pie-Charts -- Machine Learning Solutions for Predicting Bankruptcy in Indian Firms -- Efficient Object Detection via Fine-grained Regularization with Global Initialization -- On Trace of PGD-Like Adversarial Attacks -- CAB-KWS: Contrastive Augmentation: An Unsupervised Learning Approach for Keyword Spotting in Speech Technology -- Deep Learning in Automated Worm Identification and Tracking for C. Elegan Mating Behaviour Analysis -- Interactive-Time Text-Guided Editing of 3D Face -- Unlearning Vision Transformers without Retaining Data via Low-Rank Decompositions -- gWaveNet: Classification of Gravity Waves from Noisy Satellite Data using Custom Kernel Integrated Deep Learning Method -- Neural-Code PIFu: High-Fidelity Single Image 3D Human Reconstruction via Neural Code Integration -- Sea-ShipNet: Detect Any Ship in SAR Images -- Semantic Correlation Adaptation for Union-Set Multi-Label Image Recognition -- FedSC: Federated Generalized Face Anti-Spoofing via Shuffled Codebook -- LoHoSC: Low Order High Order Style Consistency for Syn-to-Real Domain Generalized Semantic Segmentation -- "]
[{"$nulls":4},"Incorporating Spatial Locality into Self-Attention for Training Vision Transformer on Small-Scale Datasets -- Cross-Domain Calibration and Boundary Denoising Network for Weakly Supervised Semantic Segmentation -- EFLLD-NET: Enhancing Few-Shot Learning With Local Descriptors -- Using Multiscale Information for Improved Optimization-based Image Attribution -- Split-DNN Computing for Video Analytics -- Task-Aware Local Descriptors Reconstruction Network for Few-Shot Find-Grained Image Classification -- TRIGS: Trojan Identification from Gradient-based Signatures -- Multifaceted Anchor Nodes Attack on Graph Neural Networks: A Budget-efficient Approach -- Causal Attentive Group Recommendation -- E2DAS: An Efficient Equivariant Dynamic Aggregation Saliency Model for Omnidirectional Images -- FewConv: Efficient variant convolution for few-shot image generation -- FixPix: Fixing Bad Pixels using Deep Learning -- Real-world Coarse to Fine-Grained Source-Free Multidomain Adaptation."]
[null,null,null,"Sommario/riassunto","The multi-volume set of LNCS books with volume numbers 15301-15333 constitutes the refereed proceedings of the 27th International Conference on Pattern Recognition, ICPR 2024, held in Kolkata, India, during December 1–5, 2024. The 963 papers presented in these proceedings were carefully reviewed and selected from a total of 2106 submissions. They deal with topics such as Pattern Recognition; Artificial Intelligence; Machine Learning; Computer Vision; Robot Vision; Machine Vision; Image Processing; Speech Processing; Signal Processing; Video Processing; Biometrics; Human-Computer Interaction (HCI); Document Analysis; Document Recognition; Biomedical Imaging; Bioinformatics."]