Deep Learning - Plunge into Deep Learning

Deep Learning : Plunge into Deep Learning, available at $27.99, has an average rating of 3.75, with 36 lectures, based on 34 reviews, and has 2351 subscribers.

You will learn about Understand the intuition behind Artificial Neural Networks Build Deep Learning Models Convolution Neural Networks Sequence Models This course is ideal for individuals who are Anyone interested in Machine Learning and Deep Learning or Students who have high school knowledge in mathematics and who want to start learning Deep Learning or Any intermediate level people who know the basics of machine learning, who want to learn more advanced topics like deep learning or Any students in college who want to start a career in Data Science or Any data analysts who want to level up in Machine Learning and Deep Learning or Any people who are not satisfied with their job and who want to become a Data Scientist. or Any people who want to create added value to their business by using powerful Learning tools. or Build a foundation on the principles of Deep Learning to understand the latest trends It is particularly useful for Anyone interested in Machine Learning and Deep Learning or Students who have high school knowledge in mathematics and who want to start learning Deep Learning or Any intermediate level people who know the basics of machine learning, who want to learn more advanced topics like deep learning or Any students in college who want to start a career in Data Science or Any data analysts who want to level up in Machine Learning and Deep Learning or Any people who are not satisfied with their job and who want to become a Data Scientist. or Any people who want to create added value to their business by using powerful Learning tools. or Build a foundation on the principles of Deep Learning to understand the latest trends.

Enroll now: Deep Learning : Plunge into Deep Learning

Summary

Title: Deep Learning : Plunge into Deep Learning

Price: $27.99

Average Rating: 3.75

Number of Lectures: 36

Number of Published Lectures: 36

Number of Curriculum Items: 36

Number of Published Curriculum Objects: 36

Original Price: $189.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the intuition behind Artificial Neural Networks

Build Deep Learning Models

Convolution Neural Networks

Sequence Models

Who Should Attend

Anyone interested in Machine Learning and Deep Learning

Students who have high school knowledge in mathematics and who want to start learning Deep Learning

Any intermediate level people who know the basics of machine learning, who want to learn more advanced topics like deep learning

Any students in college who want to start a career in Data Science

Any data analysts who want to level up in Machine Learning and Deep Learning

Any people who are not satisfied with their job and who want to become a Data Scientist.

Any people who want to create added value to their business by using powerful Learning tools.

Build a foundation on the principles of Deep Learning to understand the latest trends

Target Audiences

Anyone interested in Machine Learning and Deep Learning

Students who have high school knowledge in mathematics and who want to start learning Deep Learning

Any intermediate level people who know the basics of machine learning, who want to learn more advanced topics like deep learning

Any students in college who want to start a career in Data Science

Any data analysts who want to level up in Machine Learning and Deep Learning

Any people who are not satisfied with their job and who want to become a Data Scientist.

Any people who want to create added value to their business by using powerful Learning tools.

Build a foundation on the principles of Deep Learning to understand the latest trends

Interested in the field of Machine Learning and Deep Learning??Then this course is for you!

This course is designed in a very simple and easily understandable content.

You might have seen lots of? buzz on deep learning and you want to figure out where to start and explore.

This course is designed exactly for people like you!

If basics are strong, we can do bigger things with ease.

My focus in this course is to build complicated things starting from very basics

In this course, I will cover the following things

Session 1 – Introductory material on Deep learning, its applications and significance.

Session 2 –? Introduces the fundamental building block of deep learning

Session 3 – Logistic Regression, Activation Functions, Perceptron, One Hot Encoding, XOR problem and Multi-Layer Perceptron models

Session 4 – Training of Neural Networks: Cross Entropy, Loss Function, Gradient descent Algorithm, Non-Linear Models, Feed Forward, Backward propagation, Overfitting problem, Early stopping, Regularization, drop out and Vanishing Gradient problem.

Session 5 – Convolution Neural Networks:? Feature Extraction, Convolution Layer, Pooling Layer, Relu, Flattening and Deep Convolution Neural Networks.

Session 6 – Sequence Models: Recurrent Neural Networks, LSTMs

Are there any course requirements or prerequisites?

Just some high school mathematics level.

Who this course is for:

Anyone interested in Machine Learning and Deep Learning

Students who have high school knowledge in mathematics and who want to start learning Deep Learning

Any intermediate level people who know the basics of machine learning, who want to learn more advanced topics like deep learning

Any students in college who want to start a career in Data Science

Any data analysts who want to level up in Machine Learning and Deep Learning

Any people who are not satisfied with their job and who want to become a Data Scientist

Any people who want to create added value to their business by using powerful Learning tools

Build a foundation on the principles of Deep Learning to understand the latest trends

Course Curriculum

Chapter 1: Introduction

Lecture 1: Applications of Deep Learning

Lecture 2: What is Deep Learning?

Lecture 3: Why Deep Learning?

Lecture 4: Why now?

Chapter 2: Fundamentals

Lecture 1: Hello World of Deep learning

Lecture 2: Dataset and Features

Lecture 3: Classification

Chapter 3: Neural Networks

Lecture 1: Perceptron

Lecture 2: Sigmoid Function

Lecture 3: Softmax Function

Lecture 4: One Hot Encoding

Lecture 5: Activation Functions

Lecture 6: Logic Gates and XOR Problem

Chapter 4: Training Neural Networks

Lecture 1: Cross Entropy

Lecture 2: Loss Optimization

Lecture 3: Gradient Descent

Lecture 4: Non Linear Models

Lecture 5: Feed Forward

Lecture 6: Backward Propagation

Lecture 7: Overfitting problem

Lecture 8: Early Stopping

Lecture 9: Regularization

Lecture 10: Drop out

Lecture 11: Vanishing Gradient Problem

Chapter 5: Convolution Neural Networks

Lecture 1: Need for feature extraction

Lecture 2: Preprocessing

Lecture 3: Convolution Operation

Lecture 4: Pooling Layer

Lecture 5: Flattening

Chapter 6: Sequence Models

Lecture 1: Recurrent Neural Networks

Lecture 2: LSTMs

Lecture 3: Architecture of LSTMs

Lecture 4: Forget Gate

Lecture 5: Learn Gate

Lecture 6: Remember Gate

Lecture 7: Use Gate

Instructors

Muni Kumar Gopu V R
Machine Learning Engineer

Rating Distribution

1 stars: 3 votes

2 stars: 1 votes

3 stars: 8 votes

4 stars: 13 votes

5 stars: 9 votes

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