Deep Learning for Beginner (AI) - Data Science
- Description
- Curriculum
- FAQ
- Reviews
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:
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Introduction to Deep Learning
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Artificial Neural Network vs Biological Neural Network
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Activation Functions
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Types of Activation functions
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Artificial Neural Network (ANN) model
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Complex ANN model
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Forward ANN model
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Backward ANN model
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Python project of ANN model
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Convolutional Neural Network (CNN) model
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Filters or Kernels in CNN model
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Stride Technique
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Padding Technique
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Pooling Technique
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Flatten procedure
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Python project of a CNN model
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Recurrent Neural Network (RNN) model
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Operation of RNN model
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One-one RNN model
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One-many RNN model
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Many-many RNN model
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Many-one RNN model
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10
Linear Activation Function - Step Function and its graphical representation -
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Linear Activation Function - Sign Function - Mathematical and graphical show
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12
Linear Activation Function - Linear Function - Mathematical & Graphical show
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13
Linear Activation Function - ReLU Function - Mathematical & Graphical show
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14
Activation Function - Leaky ReLU Function - Mathematical & Graphical show
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15
Ebook: Linear activation function
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20
Introduction to Artificial Neural Network (ANN)
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21
How ANN model looks like graphically?
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22
Complex Artificial Neural Network (ANN) model
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Labelled model of Artificial Neural Network (ANN)
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Forward Artificial Neural Network (ANN) model
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Backward Artificial Neural Network (ANN) model
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26
Python project of ANN model
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27
Ebook: Artificial Neural Network (ANN)
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28
Introduction to a Convolutional Neural Network (CNN)
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29
Block diagram of a Convolutional Neural Network model
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30
What is Filter or Kernel in Convolutional Neural Network?
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31
Mathematical explanation of a Kernel or Filter in CNN model
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32
Low-level filter or kernel in CNN
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Middle-level filter or kernel in CNN
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34
High-level filter or kernel in CNN
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35
Introduction to Stride
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36
Mathematical perspective of a Stride in CNN with example
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37
Introduction to a Padding technique in Convolutional neural network (CNN)
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Mathematical perspective of Padding technique in CNN model with example
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39
Introduction to a Pooling technique in Convolutional Neural Network (CNN) model
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40
Max Pooling technique of CNN model deep learning
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Average Pooling technique of CNN model deep learning
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42
Introduction to a Flatten process in CNN model
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Graphical representation to know that how Flatten process takes place in CNN
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44
Build a Convolutional Neural Network (CNN) model in Python programming
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45
Ebook: Convolutional Neural Network (CNN)
