Deep Learning for Beginner (AI) - Data Science

Deep Learning for beginner, Mathematical & Graphical explanation of deep learning with ebooks and Python projects

Ratings: 4.58 / 5.00




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

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