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Neutral Networks

Neutral Networks

$9.99

$109.99

The "Neural Networks" course provides a thorough introduction to the core concepts and techniques of neural networks. You'll explore the architecture of neurons, layers, and activation functions, and learn to design, implement, and train various types of neural networks, including feedforward, convolutional, and recurrent networks. Through hands-on projects and real-world applications, this course equips you with the skills to apply neural networks to tasks such as image recognition and predictive modeling, preparing you to tackle complex challenges in artificial intelligence and machine learning.

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Has discount
Expiry period Lifetime
Made in English
Last updated at Tue Jul 2024
Level
Beginner
Total lectures 29
Total quizzes 0
Total duration 39:30:51 Hours
Total enrolment 134
Number of reviews 26
Avg rating
Short description The "Neural Networks" course provides a thorough introduction to the core concepts and techniques of neural networks. You'll explore the architecture of neurons, layers, and activation functions, and learn to design, implement, and train various types of neural networks, including feedforward, convolutional, and recurrent networks. Through hands-on projects and real-world applications, this course equips you with the skills to apply neural networks to tasks such as image recognition and predictive modeling, preparing you to tackle complex challenges in artificial intelligence and machine learning.
Outcomes
  • Design Neural Network Models: Create and implement various neural network architectures tailored to specific tasks and applications.
  • Train and Optimize Networks: Effectively train neural networks using techniques like gradient descent and fine-tune hyperparameters to improve performance.
  • Apply Networks to Practical Problems: Utilize neural networks for real-world applications in areas such as image recognition, speech analysis, and predictive modeling.
  • Evaluate Model Performance: Assess the performance of neural networks using appropriate metrics and techniques for model evaluation.
  • Enhance Neural Network Efficiency: Implement strategies for optimizing neural network models to achieve better accuracy and efficiency.
Requirements