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Softwarez.Info - Software's World! Library Zone Video Tutorials Optimizers in Machine Learning and Deep Learning

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Optimizers in Machine Learning and Deep Learning
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Video  08-28-2024, 02:58 PM
[Image: 026a54d710b0664602d02d1833492a2e.jpeg]
Free Download Optimizers in Machine Learning and Deep Learning
Published 8/2024
Created by Mac Data Insights
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 34 Lectures ( 2h 5m ) | Size: 1 GB

A deep dive into the math behind popular optimizers in machine learning and deep learning
What you'll learn:
Understand the math behind popular optimizers - Stochastic gradient descent, Momentum, NAG, Adagrad, RMSprop, Adam
Gain intuition behind each of these optimizers, so you can decide the best optimizer for a given dataset
Revise TensorFlow basics
Master hyperparameter tuning of each of these optimizers in TensorFlow
Perform optimization calculations by hand and match the results with the outputs generated by TensorFlow optimizer libraries
Requirements:
A basic understanding of machine learning and the role of optimizers is beneficial.
Description:
Optimization is the heart of machine learning, and mastering this crucial subject can set you apart as a top-tier data scientist or machine learning/deep learning engineer. In this comprehensive course, "Optimizers in Machine Learning and Deep Learning," you will dive deep into the core algorithms that power the training of models, from the basics to the most advanced techniques. Whether you're a beginner looking to understand the foundations or an experienced practitioner aiming to fine-tune your skills, this course offers valuable insights that will elevate your understanding and application of optimization methods. You will learn how optimizers like SGD, momentum, NAG, Adagrad, RMSprop, and Adam work behind the scenes, driving model performance and accuracy.In addition to understanding the underlying concept behind each of these optimizers, you will get to perform manual calculations in excel to derive the gradient formulas, weight updates, loss values etc. for different loss and activation functions and compare these results with the outputs generated by TensorFlow.By the end of this course, you will have a solid grasp of how to choose and implement the right optimization techniques for various machine learning and deep learning tasks, giving you the confidence and expertise to tackle real-world challenges with ease.
Who this course is for:
From beginners who are getting started in deep learning to advanced professionals who would like to take a deep dive into the math behind optimizer calculations
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