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
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Session 1 – Introductory material on Deep learning, its applications and significance.
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Session 2 – Introduces the fundamental building block of deep learning
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Session 3 – Logistic Regression, Activation Functions, Perceptron, One Hot Encoding, XOR problem and Multi-Layer Perceptron models
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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.
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Session 5 – Convolution Neural Networks: Feature Extraction, Convolution Layer, Pooling Layer, Relu, Flattening and Deep Convolution Neural Networks.
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Session 6 – Sequence Models: Recurrent Neural Networks, LSTMs
Are there any course requirements or prerequisites?
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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
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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
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
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