Optimizers in Neural Networks Skills you will learn

  • Understanding Optimizer
  • Gradient Descent Algorithm
  • Learning Rate Optimization
  • Momentum and Accelerated Gradient
  • Regularization Techniques
  • Hyperparameter Tuning

Who should learn this Optimizers in Neural Networks course?

  • Machine Learning Engineer
  • Deep Learning Engineer
  • Data Scientist
  • NLP Engineer
  • Robotics Engineer

What you will learn in this Optimizers in Neural Networks course?

  • Optimizers in Neural Network

    • Introduction

      02:26
      • Introduction
        02:26

    • Lesson 1: Optimizers in Neural Network Part-1

      57:45
      • Optimizers in Neural Network Part-1
        57:45

    • Lesson 2: Optimizers in Neural Network Part-2

      31:52
      • Optimizers in Neural Network Part-2
        31:52

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Why you should learn Optimizers in Neural Networks?

$421.1 Billion

Expected size of the global Machine Learning market by 2030.

$163K+ (USA) | INR 10.3 LPA

Average Salary of a Machine learning Engineer annually.

About the Course

The Optimizers in Neural Network Course provides an in-depth exploration of optimization techniques crucial for training neural networks. Learn how different optimizers, such as SGD, Adam, and RMSProp, impact model performance, convergence, and training efficiency. The course covers key concepts like learning rates, momentum, regularization, and practical implementation using popular deep learning frameworks like TensorFlow and PyTorch. Ideal for learners seeking to optimize their neural network models and enhance their skills in machine learning and deep learning.

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FAQs

  • What are optimizers in neural networks?

    Optimizers in neural networks are algorithms or methods used to minimize the loss function, adjusting model weights to improve performance during training.
     

  • Why are optimizers important in neural networks?

    Optimizers are essential because they determine how a neural network learns and converges towards an optimal solution, directly impacting its accuracy and efficiency.

  • Which optimizers will be covered in this Optimizers in Neural Networks course?

    This course covers popular optimizers like Stochastic Gradient Descent (SGD), Adam, RMSProp, Adagrad, and more, discussing their strengths and weaknesses.

  • Is prior knowledge of neural networks required for this Optimizers in Neural Networks course?

    A basic understanding of neural networks is helpful, but this course is designed for learners who want to dive deeper into the optimization techniques.

  • How will this Optimizers in Neural Networks course help improve my neural network models?

    By learning different optimization strategies, you will be able to choose the best optimizer for your neural network, improving training efficiency and model performance.

  • What types of neural networks will I work with in this Optimizers in Neural Networks course?

    This course covers optimization in various neural network architectures, including feedforward networks, convolutional networks, and recurrent networks.

  • Will I be able to implement these optimizers in real-world projects?

    Yes, this course includes hands-on exercises and examples where you will implement different optimizers using popular deep learning frameworks like TensorFlow and PyTorch.

  • Are there any prerequisites for this course?

    Basics of machine learning and Python programming is recommended for this course.

  • How long will it take to complete this Optimizers in Neural Networks Course?

    This Optimizers in Neural Network course is 2 hours long.

  • What can I expect after completing this Optimizers in Neural Networks course?

    After completing this course, you'll have a solid understanding of optimizers, enabling you to apply the right techniques to improve the performance and efficiency of your neural network models.

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  • Acknowledgement
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, OPM3 and the PMI ATP seal are the registered marks of the Project Management Institute, Inc.