• Suche: Introduction to Deep Learning Models With TensorFlow

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Abstract This report delves into the real-world application of Machine Learning and Deep Learning techniques in the domain of sports prediction, specifically focusing on the implementation of a Deep-Wide Neural Network Multiple-Input Multiple-Output (MIMO) model compatible to both numerical and categorical data features and targets. The purpose of this study is to provide insights into the real-world capabilities and limitations of such a predictive model, emphasizing its educational scope. Audience This study is tailored for audiences with a keen interest in real-world application of deep learning techniques in sports prediction, machine learning enthusiasts, and educators and researchers in the field. A basic understanding of machine learning and deep learning concepts would be beneficial for comprehending the findings discoursed herein. 1. Introduction In recent years, the intersection of deep learning and sports analytics has gained significant attention, offering a novel approach to predicting sports outcomes. The backend code presented herein implements a deep-wide neural network for football prediction using TensorFlow. The model is designed to handle multi-input, multi-output (MIMO) data of both numerical and categorical-text features and targets, incorporating both deep and wide neural network components. The wide part is constructed through one-hot encoding, while the deep part consists of several dense layers, the numbers and dimensions of which the interested audience may consider to appropriately modify so as to adjust the prediction accuracy of the model among other potential model-tuning endeavors. The code also includes transfer learning by saving and reloading the trained model.Tensorflow_based_MIMO_Deep_Wide_Neural_Network_with_Transfer_Learning_for_Football_Prediction
Kayemba, Augustine ; Kayemba, Luwaga ; Kayemba, Augustine ; et al.

Machine Learning MIMO Sports Prediction Deep-Wide Neural Network Deep Learnig
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Machine learning and deep learning prediction of patient specific quality assurance in breast IMRT radiotherapy plans using Halcyon specific complexity indices
Boutry, Christine ; Moreau, Noémie N ; Jaudet, Cyril ; et al.
In Radiotherapy and Oncology November 2024 200

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Deep learning in bioinformatics: Introduction, application, and perspective in the big data era
Li, Yu ; Huang, Chao ; Ding, Lizhong ; et al.
In Methods 15 August 2019 166:4-21

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A deep learning algorithm for classification of oral lichen planus lesions from photographic images: A retrospective study
Keser, Gaye ; Bayrakdar, İbrahim Şevki ; Pekiner, Filiz Namdar ; et al.
In Journal of Stomatology oral and Maxillofacial Surgery February 2023 124(1)

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Green AI: a Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures
Negar Alizadeh ; Fernando Castor
Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI. :134-139

Computer Science - Softw... Computer Science - Machi... green AI deep learning SDG 7 - Affordable and C... model conversion …
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MRI-based brain tumour image detection using CNN based deep learning method
Chattopadhyay, Arkapravo ; Maitra, Mausumi
In Neuroscience Informatics December 2022 2(4)

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Optical Recognition of the Philippines' Ancient Text: A Deep Learning Approach
Ador Y. Franco
INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS.

Baybayin, Optical Charac...
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Subsurface velocity inversion from deep learning-based data assimilation
Mao, Bo ; Han, Li-Guo ; Feng, Qiang ; et al.
In Journal of Applied Geophysics August 2019 167:172-179

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A tutorial on automatic hyperparameter tuning of deep spectral modelling for regression and classification tasks
Passos, Dário ; Mishra, Puneet
In Chemometrics and Intelligent Laboratory Systems 15 April 2022 223

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Algoritmos de Deep Learning utilizando Tensorflow para el Tratamiento de Datos de Producción Científica. (Spanish)
Falconí Punguil, Diego Geovanny
Tesla Revista Científica; jul-dic2023, Vol. 3 Issue 2, p1-15, 15p

MACHINE learning ARTIFICIAL intelligence WEB hosting DEEP learning DECISION making ALGORITHMS
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Abstract 15352: Deep Learning Model-Enabled Electrocardiogram to Localize Premature Ventricular Contractions in Patients Referred for Catheter Ablation
Qiying Dai ; John A Wight ; Gurukripa N Kowlgi ; et al.
Circulation. 148

03 medical and health sc... 0302 clinical medicine
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Introduction to Tensorflow Package
Kolla Bhanu Prakash ; Adarsha Ruwali ; G. R. Kanagachidambaresan
EAI/Springer Innovations in Communication and Computing ISBN: 9783030570767

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Enhanced Malaria Detection Using 2D CNN and Transfer Learning: An Efficient Deep Learning Solution
Muhammad Shameem P ; Muthukumaran Malarvel
South Eastern European Journal of Public Health. :180-192

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Chapter 1 - Introduction to TensorFlow 2
In Principles and Labs for Deep Learning 2021:1-26

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A DEEP LEARNING APPROACH TO PREDICTING PAIN PROGRESSION IN OSTEOARTHRITIS FROM 3-D KNEE JOINT PARAMETERS: A MOST INVESTIGATION
Parkin, J.M. ; Ghobrial, S.E. ; Tonkin, C.J. ; et al.
In Osteoarthritis Imaging 2023 3 Supplement 1

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Prediction of time averaged wall shear stress distribution in coronary arteries' bifurcation varying in morphological features via deep learning.
Sarkhosh, Mohammad Hossein ; Edrisnia, Hadis ; Raveshi, Mohammad Reza ; et al.
Frontiers in Physiology; 2025, p1-12, 12p

CORONARY circulation COMPUTATIONAL fluid dyna... CORONARY arteries BLOOD circulation SHEARING force
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Introduction to Deep Learning: From mathematical equations to Python code
Initiation au Deep Learning : Des équations mathématiques aux codes Python
Kazadi, Joël, K ; Université de Kinshasa ; International University of Applied Sciences
Deep Learning

https://www.researchgate... Backward propagation TensorFlow Réseaux de neurones Forward propagation [INFO.INFO-AI]Computer S... …
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