Mastering the Fundamentals of Neural Networks Online Course

Mastering the Fundamentals of Neural Networks Online Course

This course provides a comprehensive introduction to neural networks, covering supervised, semi-supervised, and unsupervised learning techniques. It explores deep learning architectures, including deep neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), along with their applications in fields such as computer vision, natural language processing, medical imaging, and more.

The curriculum is structured into three key sections. The first section focuses on neural networks, introducing concepts like logistic and linear regression, the purpose of neural networks, forward and backward propagation, and the cross-entropy function. The second section delves into convolutional neural networks, covering image data processing, convolutional operations, and advanced architectures such as residual networks. The final section explores recurrent neural networks, including RNNs, Gated Recurrent Units (GRUs), and Long Short-Term Memory (LSTM) networks, which are essential for sequential data processing.

Throughout the course, interactive coding exercises and notebooks will provide hands-on experience, reinforcing theoretical concepts with practical implementation. By the end of this course, you will have a strong foundation in neural networks and their real-world applications.

Key Benefits

• Gain a deep understanding of the fundamental concepts behind Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), including their architectures, functionalities, and real-world applications. 
• Develop a strong grasp of forward and backward propagation in ANNs, essential for optimizing neural network performance through efficient weight adjustments. 
• Additionally, explore Bidirectional Recurrent Neural Networks (BRNNs) and their ability to process sequential data in both forward and backward directions, enhancing model accuracy in tasks such as natural language processing and time-series analysis.

Target Audience

This course is designed for beginners seeking a comprehensive introduction to Artificial Intelligence (AI), Deep Learning, and the three primary types of neural networks: Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). It provides an in-depth understanding of these concepts without requiring any prior coding or programming experience. Participants will need access to their own laptop, as all coding exercises and practical implementations will be conducted using Google Colab, ensuring a seamless and accessible learning experience.

Learning Objectives

• This course provides a thorough understanding of key concepts in neural networks and deep learning. 
• You will explore linear and logistic regression within Artificial Neural Networks (ANNs), gaining insights into their role in predictive modeling. 
• Additionally, you will learn about cross-entropy as a crucial loss function for measuring the difference between two probability distributions in classification tasks.
• You will develop a strong understanding of the convolution operation, which processes input data by scanning across its dimensions to extract essential features. 
• You will also explore VGG16, a widely used deep convolutional neural network model known for its efficient architecture in image classification.
• The course will also provide an in-depth understanding of Long Short-Term Memory (LSTM) networks, a specialized type of RNN designed to capture long-term dependencies and overcome the limitations of traditional recurrent networks.