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Tag: GRU
Prévision précise des cours boursiers à l’aide de réseaux neuronaux LSTM et GRU: Une approche d’apprentissage profond pour la prévision de séries … de prix d’actions en groupe. (French Edition)
Price: $47.00
(as of Dec 29,2024 00:44:09 UTC – Details)
Publisher : Editions Notre Savoir (August 11, 2021)
Language : French
Paperback : 52 pages
ISBN-10 : 6204005634
ISBN-13 : 978-6204005638
Item Weight : 2.82 ounces
Dimensions : 5.91 x 0.12 x 8.66 inches
Prévision précise des cours boursiers à l’aide de réseaux neuronaux LSTM et GRU: Une approche d’apprentissage profond pour la prévision de séries de prix d’actions en groupe.Les réseaux neuronaux LSTM (Long Short-Term Memory) et GRU (Gated Recurrent Unit) ont révolutionné la prédiction des cours boursiers en permettant une analyse plus précise et plus rapide des données financières. Dans ce livre, nous explorons comment ces deux architectures de réseaux neuronaux peuvent être utilisées pour prédire de manière précise les prix des actions en groupe.
En utilisant des données historiques sur les prix des actions, nous montrons comment les réseaux neuronaux LSTM et GRU peuvent être entraînés pour capturer les tendances et les motifs complexes des marchés financiers. Nous expliquons également comment ces modèles peuvent être utilisés pour prédire les mouvements futurs des prix des actions et aider les investisseurs à prendre des décisions éclairées.
Que vous soyez un trader expérimenté ou un novice en matière d’investissement, ce livre vous fournira les connaissances et les outils nécessaires pour améliorer vos prévisions de cours boursiers et maximiser vos rendements. Ne manquez pas cette opportunité d’apprendre des techniques avancées d’apprentissage profond pour prédire avec précision les mouvements des marchés financiers.
#Prévision #précise #des #cours #boursiers #laide #réseaux #neuronaux #LSTM #GRU #Une #approche #dapprentissage #profond #pour #prévision #séries #prix #dactions #groupe #French #Edition,lstm
Unraveling the Intricacies of LSTM and GRU Architectures in Recurrent Neural Networks
Recurrent Neural Networks (RNNs) have gained popularity in recent years for their ability to process sequential data. Among the various types of RNNs, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are two of the most commonly used architectures due to their effectiveness in capturing long-range dependencies in data.LSTM and GRU architectures were designed to address the vanishing gradient problem that plagues traditional RNNs. This problem occurs when gradients become too small during backpropagation, leading to difficulties in learning long-term dependencies in the data. LSTM and GRU architectures incorporate mechanisms that allow them to retain information over long periods of time, making them well-suited for tasks such as natural language processing, speech recognition, and time series prediction.
One of the key features of LSTM and GRU architectures is the use of gate mechanisms to control the flow of information within the network. In LSTM, there are three gates – the input gate, forget gate, and output gate – that regulate the flow of information by selectively updating and forgetting information at each time step. This allows LSTM to maintain long-term dependencies in the data by storing relevant information in its memory cells.
On the other hand, GRU simplifies the architecture by combining the input and forget gates into a single update gate. This makes GRU more computationally efficient compared to LSTM, while still being able to capture long-term dependencies in the data. Additionally, GRU has fewer parameters than LSTM, which can be beneficial for training on smaller datasets or limited computational resources.
Despite their differences, LSTM and GRU architectures have been shown to achieve comparable performance on a wide range of tasks. The choice between LSTM and GRU often depends on the specific requirements of the task at hand, such as the size of the dataset, computational resources available, and the complexity of the data being processed.
In conclusion, LSTM and GRU architectures have revolutionized the field of recurrent neural networks by addressing the vanishing gradient problem and enabling the modeling of long-term dependencies in sequential data. Understanding the intricacies of these architectures is essential for building effective models for tasks such as natural language processing, speech recognition, and time series prediction. By unraveling the complexities of LSTM and GRU architectures, researchers and practitioners can harness the power of recurrent neural networks to tackle a wide range of challenging tasks.
#Unraveling #Intricacies #LSTM #GRU #Architectures #Recurrent #Neural #Networks,recurrent neural networks: from simple to gated architectures
Previsione accurata dei prezzi delle azioni usando reti neurali LSTM e GRU: Un approccio di Deep Learning per la previsione di dati di serie temporali di prezzi azionari in gruppi (Italian Edition)
Price: $47.00
(as of Dec 29,2024 00:14:49 UTC – Details)
Publisher : Edizioni Sapienza (August 11, 2021)
Language : Italian
Paperback : 52 pages
ISBN-10 : 6204005650
ISBN-13 : 978-6204005652
Item Weight : 3.39 ounces
Dimensions : 5.91 x 0.12 x 8.66 inches
Previsione accurata dei prezzi delle azioni usando reti neurali LSTM e GRU: Un approccio di Deep Learning per la previsione di dati di serie temporali di prezzi azionari in gruppi (Italian Edition)Il trading azionario è un settore in cui la previsione accurata dei prezzi delle azioni è di fondamentale importanza per massimizzare i profitti e minimizzare le perdite. In questo post, esploreremo come le reti neurali LSTM e GRU possono essere utilizzate per prevedere i prezzi delle azioni in gruppi, utilizzando un approccio di Deep Learning.
Le reti neurali LSTM (Long Short-Term Memory) e GRU (Gated Recurrent Unit) sono due tipi di reti neurali ricorrenti che sono particolarmente adatti per la previsione di dati di serie temporali, come i prezzi delle azioni. Queste reti sono in grado di catturare le relazioni temporali complesse nei dati e di apprendere modelli a lungo termine, rendendole ideali per la previsione dei prezzi delle azioni.
Utilizzando un dataset storico di prezzi delle azioni, possiamo addestrare una rete neurale LSTM o GRU per prevedere i prezzi futuri delle azioni in base ai dati passati. Questo modello può essere utilizzato per identificare pattern e tendenze nei dati e per fare previsioni accurate sui movimenti futuri dei prezzi delle azioni.
Con un’accurata previsione dei prezzi delle azioni, i trader possono prendere decisioni informate sul quando comprare e vendere le azioni, massimizzando i loro profitti e minimizzando le perdite. L’utilizzo di reti neurali LSTM e GRU per la previsione dei prezzi delle azioni in gruppi rappresenta un approccio innovativo e efficace per il trading azionario.
Se sei interessato a saperne di più su come utilizzare reti neurali LSTM e GRU per la previsione dei prezzi delle azioni, assicurati di leggere il nostro ebook “Previsione accurata dei prezzi delle azioni usando reti neurali LSTM e GRU”. Grazie per aver letto!
#Previsione #accurata #dei #prezzi #delle #azioni #usando #reti #neurali #LSTM #GRU #approccio #Deep #Learning #previsione #dati #serie #temporali #prezzi #azionari #gruppi #Italian #Edition,lstm
Structural Equation Modelling, Bi-LSTM, GRU, DNN, CNN, PDDL, FMEA, Shapiro-Wilk Test and Wilcoxon Signed Rank; : Layman’s term and Step by step example for Social Studies and Engineering Research
Price: $4.99
(as of Dec 26,2024 16:32:14 UTC – Details)
ASIN : B0CRZBSQHR
Publication date : January 9, 2024
Language : English
File size : 1097 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Enabled
Print length : 138 pages
Structural Equation Modelling, Bi-LSTM, GRU, DNN, CNN, PDDL, FMEA, Shapiro-Wilk Test, and Wilcoxon Signed Rank: Layman’s Terms and Step-by-Step Example for Social Studies and Engineering ResearchIn simpler terms, these are all different methods and techniques that researchers use to analyze data and make sense of complex relationships in their studies.
Let’s break it down with a step-by-step example for a social studies research project:
1. Research Question: Let’s say you are interested in studying the relationship between social media usage and mental health among teenagers.
2. Data Collection: You would collect data on social media usage (e.g., hours spent on social media per day) and mental health indicators (e.g., levels of anxiety and depression) from a sample of teenagers.
3. Structural Equation Modelling (SEM): This technique allows you to test a theoretical model that shows how social media usage influences mental health. You can use SEM to analyze the relationships between different variables and see if there is a direct or indirect effect.
4. Bi-LSTM and GRU: These are types of neural networks that are commonly used in natural language processing and text analysis. You could use Bi-LSTM and GRU to analyze the text data from social media posts to understand the sentiments and emotions expressed by teenagers.
5. DNN and CNN: Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN) are used for image recognition and analysis. You could use DNN and CNN to analyze visual content shared on social media platforms and see if there is a correlation between certain types of images and mental health indicators.
Now, let’s look at an engineering research project example:
1. Research Question: Let’s say you are interested in studying the reliability of a new mechanical component in a machine.
2. Failure Mode and Effects Analysis (FMEA): This technique helps you identify potential failure modes of the component and their effects on the overall system. By conducting FMEA, you can prioritize potential failure modes and develop strategies to mitigate risks.
3. PDDL: Planning Domain Definition Language (PDDL) is a formal language used in artificial intelligence and automated planning. You could use PDDL to create a model of the machine system and simulate different scenarios to see how the new component performs under different conditions.
4. Shapiro-Wilk Test and Wilcoxon Signed Rank: These are statistical tests used to analyze data and determine if there are significant differences between groups or conditions. You could use the Shapiro-Wilk Test to check the normality of the data and the Wilcoxon Signed Rank test to compare the reliability of the new component with the old one.
By utilizing these methods and techniques in your research projects, you can gain valuable insights and make informed decisions based on data-driven analysis.
#Structural #Equation #Modelling #BiLSTM #GRU #DNN #CNN #PDDL #FMEA #ShapiroWilk #Test #Wilcoxon #Signed #Rank #Laymans #term #Step #step #Social #Studies #Engineering #Research
Accurately Forecasting Stock Prices using LSTM and GRU Neural Networks: A Deep Learning approach for forecasting stock price time-series data in groups
Price: $47.00
(as of Dec 24,2024 19:24:10 UTC – Details)
Publisher : LAP LAMBERT Academic Publishing (July 30, 2021)
Language : English
Paperback : 52 pages
ISBN-10 : 620419092X
ISBN-13 : 978-6204190921
Item Weight : 3.39 ounces
Dimensions : 5.91 x 0.12 x 8.66 inches
In today’s fast-paced and volatile stock market environment, accurately forecasting stock prices is essential for making informed investment decisions. Traditional forecasting methods often fall short when it comes to capturing the complex patterns and trends in stock price time-series data. However, with the advancement of deep learning techniques such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks, forecasting stock prices has become more accurate and reliable.LSTM and GRU neural networks are specifically designed to handle sequential data and are well-suited for time-series forecasting tasks. By leveraging the memory capabilities of these networks, we can effectively capture long-term dependencies and patterns in stock price data, making them ideal for forecasting future stock prices.
In this post, we will explore how LSTM and GRU neural networks can be used to accurately forecast stock prices in groups. By grouping stocks based on similar characteristics or industry sectors, we can improve the forecasting accuracy by capturing common trends and patterns within each group.
To start, we will preprocess and normalize the stock price time-series data for each group. We will then train LSTM and GRU neural networks on historical stock price data to learn the underlying patterns and trends. By fine-tuning the network parameters and optimizing the model architecture, we can improve the forecasting accuracy and reduce prediction errors.
Once the models are trained and validated, we can use them to forecast future stock prices for each group. By comparing the predicted prices with the actual prices, we can evaluate the accuracy of the forecasting models and make adjustments as needed.
Overall, using LSTM and GRU neural networks for forecasting stock prices in groups offers a powerful and effective approach to capturing the complex dynamics of the stock market. By leveraging the memory capabilities of these networks, we can improve the accuracy and reliability of stock price forecasts, enabling investors to make more informed decisions and maximize their returns.
#Accurately #Forecasting #Stock #Prices #LSTM #GRU #Neural #Networks #Deep #Learning #approach #forecasting #stock #price #timeseries #data #groups
Deep Learning: Recurrent Neural Networks in Python: LSTM, GRU, and more RNN machine learning architectures in Python and Theano (Machine Learning in Python)
Price: $2.99
(as of Dec 24,2024 14:37:11 UTC – Details)
ASIN : B01K31SQQA
Publication date : August 8, 2016
Language : English
File size : 402 KB
Simultaneous device usage : Unlimited
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 87 pages
Deep Learning: Recurrent Neural Networks in Python: LSTM, GRU, and more RNN machine learning architectures in Python and Theano (Machine Learning in Python)In this post, we will delve into the world of Recurrent Neural Networks (RNNs) in Python, exploring popular architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). RNNs are a powerful class of neural networks that are designed to handle sequential data, making them ideal for tasks such as time series forecasting, natural language processing, and speech recognition.
We will walk through the implementation of RNNs in Python using the Theano library, a popular deep learning framework. We will cover the basics of RNNs, including how they work and why they are well-suited for sequential data. We will then dive into the implementation of LSTM and GRU architectures, exploring their differences and advantages.
By the end of this post, you will have a solid understanding of how to use RNNs in Python for a variety of machine learning tasks. Whether you are a beginner looking to learn more about deep learning or an experienced data scientist looking to expand your skill set, this post will provide you with the knowledge and tools you need to harness the power of RNNs in Python.
#Deep #Learning #Recurrent #Neural #Networks #Python #LSTM #GRU #RNN #machine #learning #architectures #Python #Theano #Machine #Learning #Python
