Convolutional Neural Networks: Deep Learning

Convolutional Neural Networks: Deep Learning, available at $49.99, has an average rating of 4.35, with 26 lectures, based on 23 reviews, and has 7076 subscribers.

You will learn about Understand the basics and types of 2D Signals (Images) Understand and implement the process of convolution Learn and implement the Convolutional neural networks for any real time applications Review the fundamentals of deep learning This course is ideal for individuals who are Anyone who wants to understand CNN in depth It is particularly useful for Anyone who wants to understand CNN in depth.

Enroll now: Convolutional Neural Networks: Deep Learning

Summary

Title: Convolutional Neural Networks: Deep Learning

Price: $49.99

Average Rating: 4.35

Number of Lectures: 26

Number of Published Lectures: 26

Number of Curriculum Items: 26

Number of Published Curriculum Objects: 26

Original Price: ₹799

Quality Status: approved

Status: Live

What You Will Learn

Understand the basics and types of 2D Signals (Images)
Understand and implement the process of convolution
Learn and implement the Convolutional neural networks for any real time applications
Review the fundamentals of deep learning

Who Should Attend

Anyone who wants to understand CNN in depth

Target Audiences

Anyone who wants to understand CNN in depth

In this course, you’ll be learning the fundamentals of deep neural networks and CNN in depth.

This course offers an extensive exploration of deep neural networks with a focus on Convolutional Neural Networks (CNNs).
The course begins by delving into the fundamental concepts to provide a strong foundation for learners.
Initial sections of the course include:
- Understanding what deep learning is and its significance in modern machine learning.
- Exploring the intricacies of neural networks, the building blocks of deep learning.
- Discovering where CNNs fit into the larger landscape of machine learning techniques.
- In-depth examination of the fundamentals of Perceptron Networks.
- Comprehensive exploration of Multilayer Perceptrons (MLPs).
- A detailed look into the mathematics behind feed forward networks.
- Understanding the significance of activation functions in neural networks.
A major portion of the course is dedicated to Convolutional Neural Networks (CNNs):
- Exploring the architecture of CNNs.
- Investigating their applications, especially in image processing and computer vision.
- Understanding convolutional layers that extract relevant features from input data.
- Delving into pooling layers, which reduce spatial dimensions while retaining essential information.
- Examining fully connected layers for making predictions and decisions.
- Learning about design choices and hyperparameters influencing CNN performance.
The course also covers training and optimization of CNNs:
- Understanding loss functions and their role in training.
- Grasping the concept of backpropagation.
- Learning techniques to prevent overfitting.
- Introduction to optimization algorithms for fine-tuning CNNs.
Practical implementation is a significant component:
- Hands-on coding and implementation using Python and deep learning frameworks like TensorFlow or PyTorch.
- Building and training CNN models for various applications.
- Gaining real-world skills to develop your own CNN-based projects.
By the course’s conclusion, you’ll have comprehensive knowledge of CNNs and practical skills for their application in various real-world scenarios. This knowledge empowers you in the field of deep learning and CNNs, whether you’re interested in image recognition, object detection, or other computer vision tasks.

The last section is all about doing a project by implementing CNN

Course Curriculum

Chapter 1: Introduction

Lecture 1: Where does CNN lie?

Lecture 2: Explanation, Types of Deep Learning Networks

Chapter 2: Neural Networks – A review

Lecture 1: Perceptron Networks

Lecture 2: Mathematics behind Feed Forward Networks

Lecture 3: Purpose of Activation Functions

Lecture 4: ReLU Activation Function

Chapter 3: Convolution in Digital Image Processing

Lecture 1: Convolution – A Deep Dive

Lecture 2: 1D Convolution Example – HPF, LPF

Lecture 3: Basics of Images

Lecture 4: Example of 2D Convolution

Lecture 5: Convolution in Action

Lecture 6: Edge Detector Algorithm

Lecture 7: Mathematical model of CNN

Chapter 4: CNN – Layerwise study

Lecture 1: Why CNN is ideal for Image Processing

Lecture 2: Applications of CNN

Lecture 3: CNN Architecture and Layers

Lecture 4: ReLU Activation

Lecture 5: How to perform Pooling

Lecture 6: Batch Normalization

Chapter 5: The Project – Fruits Classifier using CNN

Lecture 1: Importing all the essential libraries

Lecture 2: Visualization and Preprocessing of Images

Lecture 3: Using glob to find out the number of classes

Lecture 4: Defining Convolutional Layers

Lecture 5: Training the CNN Architecture

Lecture 6: The Testing Phase

Chapter 6: Resources

Lecture 1: Bonus Lecture

Instructors

Sujithkumar MA
Engineer | Course Instructor

Rating Distribution

1 stars: 2 votes
2 stars: 0 votes
3 stars: 5 votes
4 stars: 5 votes
5 stars: 11 votes

Frequently Asked Questions

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