Introduction to AI and Deep Learning

Welcome to our AI and Deep Learning course in collaboration with!

In our AI and Deep Learning course, we will be covering essential topics like Neural Networks, Activation Functions, Gradient Descent, Regularization, Convolutional Neural Networks, and Recurrent Neural Networks. Also, we prepared unique content using different worldwide resources to support your learning process!

During the class, we will create simple algorithms. A few homework assignments will be given after the lessons to improve your theoretical knowledge and coding skills.

Enroll in our AI and Deep Learning course and start to learn the basics of Deep Learning, understand how the Neural Networks work, gain hands-on experience, and start building algorithms by using one of the most popular Artificial Neural Networks Libraries in Python, Keras!

You can ask all your questions via the Deep Learning Hub in the Global AI Hub Community.

You can check most frequently asked questions about this course.


Day 1

Theory: Introduction to AI and Deep Learning

• Motivation: Foundations and Terminology of Deep Learning

• AI vs ML vs DL: A comparison

• Features and Weights

• Machine Learning Recap: Linear Regression, Logistic Regression

• Activation Functions


• Introduction to Python Programming

Day 2

Theory: Neural Networks

• Neural Networks

• Loss Functions

• Gradient Descent

• Feedforward and Backward Propagation

• Deep Learning Model training


• Building Neural Networks

Day 3

Theory: Dataset, Regularization and Hyperparameter Tuning

• Dataset splitting and distribution

• Evaluation Metrics

• Bias vs Variance

• Regularization: L1/L2 regularization, Dropout, Early Stopping

• Optimization methods

• Hyperparameter Tuning


• Tuning Neural Networks

Day 4

Theory: Convolutional Neural Networks

• ANN vs CNN

• Convolution, pooling, padding, striding

• Transfer Learning

• Applications of CNN


• Image Classification using CNN

Day 5

Theory: Recurrent Neural Networks

• ANN vs RNN

• Sequential Processing with RNN

• Forward and Back Propagation

• Language Models

• LSTM and GRU

• RNN Applications


• Text Classification using RNN