Deep Learning for Beginner (AI) – Data Science

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Deep Learning for Beginner (AI) – Data Science

Requirements

  • Basics of Python and Machine Learning

Description

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:

  1. Introduction to Deep Learning
  2. Artificial Neural Network vs Biological Neural Network
  3. Activation Functions
  4. Types of Activation functions
  5. Artificial Neural Network (ANN) model
  6. Complex ANN model
  7. Forward ANN model
  8. Backward ANN model
  9. Python project of ANN model
  10. Convolutional Neural Network (CNN) model
  11. Filters or Kernels in CNN model
  12. Stride Technique
  13. Padding Technique
  14. Pooling Technique
  15. Flatten procedure
  16. Python project of a CNN model
  17. Recurrent Neural Network (RNN) model
  18. Operation of RNN model
  19. One-one RNN model
  20. One-many RNN model
  21. Many-many RNN model
  22. Many-one RNN model

Who this course is for:

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

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