Optimizers in Machine Learning and Deep Learning

Optimizers in Machine Learning and Deep Learning, available at $84.99, with 34 lectures, and has 1 subscribers.

You will learn about 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 This course is ideal for individuals who are 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 It is particularly useful 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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Summary

Title: Optimizers in Machine Learning and Deep Learning

Price: $84.99

Number of Lectures: 34

Number of Published Lectures: 34

Number of Curriculum Items: 34

Number of Published Curriculum Objects: 34

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Resources

Chapter 2: Stochastic Gradient Descent

Lecture 1: Stochastic Gradient Descent (SGD) – Intro

Lecture 2: SGD with Mean Squared Error – Gradient derivation

Lecture 3: SGD – Excel implementation

Lecture 4: SGD – Validating excel outputs using TensorFlow

Lecture 5: SGD – Pros and Cons

Chapter 3: Momentum

Lecture 1: Momentum – Intro

Lecture 2: Momentum – Excel implementation

Lecture 3: Momentum – Validating excel outputs using TensorFlow

Lecture 4: Momentum – Pros and Cons

Chapter 4: NAG

Lecture 1: NAG – Intro

Lecture 2: NAG – Excel implementation

Lecture 3: NAG – Validating excel outputs using TensorFlow

Lecture 4: NAG – Pros and Cons

Chapter 5: Adagrad

Lecture 1: Adagrad – Intro

Lecture 2: Adagrad – Excel implementation

Lecture 3: Adagrad – Validating excel outputs using TensorFlow

Lecture 4: Adagrad – Pros and Cons

Chapter 6: RMSprop

Lecture 1: RMSprop – Intro

Lecture 2: RMSprop – Excel implementation

Lecture 3: RMSprop – Validating excel outputs using TensorFlow

Lecture 4: RMSprop – Pros and Cons

Chapter 7: Adam

Lecture 1: Adam – Intro

Lecture 2: Adam – Excel implementation

Lecture 3: Adam – Validating excel outputs using TensorFlow

Lecture 4: Adam – Pros and Cons

Chapter 8: Gradient derivation for different loss and activation functions

Lecture 1: Gradient derivation – Intro

Lecture 2: SGD with Mean Absolute Error

Lecture 3: SGD with Root Mean Squared Error

Lecture 4: SGD with ReLu Activation and Mean Absolute Error

Lecture 5: SGD with Sigmoid Activation and Binary Log loss – Part 1

Lecture 6: SGD with Sigmoid Activation and Binary Log loss – Part 2

Lecture 7: Summary of gradients

Instructors

Mac Data Insights
Empowering data scientists

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