Deep Learning for Beginner (AI) – Data Science

Deep Learning for Beginner (AI) – Data Science, available at $49.99, has an average rating of 4.35, with 55 lectures, based on 14 reviews, and has 3019 subscribers.

You will learn about Introduction to Deep learning, resemblance of artificial neural network and biological neural network Activation function and its types, Application of activation function, Linear activation function, Non-linear activation function Types of activation function: Step function, Sign function, Linear function, ReLU function, Leaky ReLU function, Tangent Hyperbolic function, Sigmoid, Softmax Artificial neural network, ANN model, Complex ANN model, Labelled ANN model, Forward ANN, Backward ANN, ANN python project Convolutional Neural Network (CNN), CNN block diagram, Filter or Kernel, Types of filters, Stride, Padding, Pooling, Flatten, CNN Python project Recurrent Neural Network (RNN), RNN model, Operation of RNN model, Types; One-one RNN model, One-many RNN model, Many-many RNN model This course is ideal for individuals who are Beginner of a Deep Learning of artificial intelligence who wants to learn from scratch It is particularly useful for Beginner of a Deep Learning of artificial intelligence who wants to learn from scratch.

Enroll now: Deep Learning for Beginner (AI) – Data Science

Summary

Title: Deep Learning for Beginner (AI) – Data Science

Price: $49.99

Average Rating: 4.35

Number of Lectures: 55

Number of Published Lectures: 55

Number of Curriculum Items: 55

Number of Published Curriculum Objects: 55

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Introduction to Deep learning, resemblance of artificial neural network and biological neural network
Activation function and its types, Application of activation function, Linear activation function, Non-linear activation function
Types of activation function: Step function, Sign function, Linear function, ReLU function, Leaky ReLU function, Tangent Hyperbolic function, Sigmoid, Softmax
Artificial neural network, ANN model, Complex ANN model, Labelled ANN model, Forward ANN, Backward ANN, ANN python project
Convolutional Neural Network (CNN), CNN block diagram, Filter or Kernel, Types of filters, Stride, Padding, Pooling, Flatten, CNN Python project
Recurrent Neural Network (RNN), RNN model, Operation of RNN model, Types; One-one RNN model, One-many RNN model, Many-many RNN model

Who Should Attend

Beginner of a Deep Learning of artificial intelligence who wants to learn from scratch

Target Audiences

Beginner of a Deep Learning of artificial intelligence who wants to learn from scratch

Learn Deep Learning from scratch. It is the extension of a Machine Learning, this course is for beginner who wants to learn the fundamental of deep learning and artificial intelligence. The course includes video explanation with introductions (basics), detailed theory and graphical explanations. Some daily life projects have been solved by using Python programming. Downloadable files of ebooks and Python codes have been attached to all the sections. The lectures are appealing, fancy and fast. They take less time to walk you through the whole content. Each and every topic has been taught extensively in depth to cover all the possible areas to understand the concept in most possible easy way. It’s highly recommended for the students who don’t know the fundamental of machine learning studying at college and university level.

The main goal of publishing this course is to explain the deep learning and artificial intelligence in a very simple and easy way. All the codes have been conducted through colab which is an online editor. Python remains a popular choice among numerous companies and organization. Python has a reputation as a beginner-friendly language, replacing Java as the most widely used introductory language because it handles much of the complexity for the user, allowing beginners to focus on fully grasping programming concepts rather than minute details.

Below is the list of different topics covered in Deep Learning:

Introduction to Deep Learning
Artificial Neural Network vs Biological Neural Network
Activation Functions
Types of Activation functions
Artificial Neural Network (ANN) model
Complex ANN model
Forward ANN model
Backward ANN model
Python project of ANN model
Convolutional Neural Network (CNN) model
Filters or Kernels in CNN model
Stride Technique
Padding Technique
Pooling Technique
Flatten procedure
Python project of a CNN model
Recurrent Neural Network (RNN) model
Operation of RNN model
One-one RNN model
One-many RNN model
Many-many RNN model
Many-one RNN model

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Introduction to deep learning

Lecture 1: What is Deep Learning?

Lecture 2: How Neural Network looks like? Comparison of artificial and biological network

Lecture 3: Ebook: Introduction to Deep Learning

Chapter 3: Activation function

Lecture 1: What is activation function?

Lecture 2: Activation function in neural network

Lecture 3: Graphical perspective to know that why we need activation function function?

Lecture 4: How man types of activation functions we have?

Lecture 5: Ebook: Introduction to activation function

Chapter 4: Linear Activation Functions

Lecture 1: Linear Activation Function – Step Function and its graphical representation

Lecture 2: Linear Activation Function – Sign Function – Mathematical and graphical show

Lecture 3: Linear Activation Function – Linear Function – Mathematical & Graphical show

Lecture 4: Linear Activation Function – ReLU Function – Mathematical & Graphical show

Lecture 5: Activation Function – Leaky ReLU Function – Mathematical & Graphical show

Lecture 6: Ebook: Linear activation function

Chapter 5: Non – Linear Activation Functions

Lecture 1: Non-Linear Activation Function – Tangent Hyperbolic function in detail

Lecture 2: Non-Linear Activation Function – Sigmoid Function in detail

Lecture 3: Softmax Activation – Mathematical and Graphical representation

Lecture 4: Ebook: Non-Linear activation function

Chapter 6: Artificial Neural Network – ANN Model

Lecture 1: Introduction to Artificial Neural Network (ANN)

Lecture 2: How ANN model looks like graphically?

Lecture 3: Complex Artificial Neural Network (ANN) model

Lecture 4: Labelled model of Artificial Neural Network (ANN)

Lecture 5: Forward Artificial Neural Network (ANN) model

Lecture 6: Backward Artificial Neural Network (ANN) model

Lecture 7: Python project of ANN model

Lecture 8: Ebook: Artificial Neural Network (ANN)

Chapter 7: Convolutional Neural Network – CNN Model

Lecture 1: Introduction to a Convolutional Neural Network (CNN)

Lecture 2: Block diagram of a Convolutional Neural Network model

Lecture 3: What is Filter or Kernel in Convolutional Neural Network?

Lecture 4: Mathematical explanation of a Kernel or Filter in CNN model

Lecture 5: Low-level filter or kernel in CNN

Lecture 6: Middle-level filter or kernel in CNN

Lecture 7: High-level filter or kernel in CNN

Lecture 8: Introduction to Stride

Lecture 9: Mathematical perspective of a Stride in CNN with example

Lecture 10: Introduction to a Padding technique in Convolutional neural network (CNN)

Lecture 11: Mathematical perspective of Padding technique in CNN model with example

Lecture 12: Introduction to a Pooling technique in Convolutional Neural Network (CNN) model

Lecture 13: Max Pooling technique of CNN model deep learning

Lecture 14: Average Pooling technique of CNN model deep learning

Lecture 15: Introduction to a Flatten process in CNN model

Lecture 16: Graphical representation to know that how Flatten process takes place in CNN

Lecture 17: Build a Convolutional Neural Network (CNN) model in Python programming

Lecture 18: Ebook: Convolutional Neural Network (CNN)

Chapter 8: Recurrent Neural Network (RNN) Model in Deep Learning

Lecture 1: Introduction to Recurrent Neural Network in deep learning

Lecture 2: Graphical representation of a Recurrent Neural Network (RNN) model

Lecture 3: Typical shape of a Recurrent Neural Network model

Lecture 4: Complex model of a Recurrent Neural Network (RNN)

Lecture 5: Operation of a Recurrent Neural Network model

Lecture 6: One-one RNN model

Lecture 7: One-many RNN model

Lecture 8: Many-many RNN model

Lecture 9: Many-one RNN model

Lecture 10: Ebook: Recurrent Neural Network (RNN)

Instructors

Moein Ud Din
Engineer

Rating Distribution

1 stars: 0 votes
2 stars: 3 votes
3 stars: 2 votes
4 stars: 2 votes
5 stars: 7 votes

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