Neural Network & Deep Learning

the Neural Network & Deep Learning course provides a comprehensive introduction to neural networks and deep learning, covering both theoretical foundations and practical applications. Students will explore how neural networks mimic the human brain to process data, recognize patterns, and make intelligent decisions. The course begins with fundamental concepts such as perceptrons, activation functions, and gradient descent, gradually progressing to more advanced topics like deep neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

Throughout the course, students will gain hands-on experience by implementing neural networks using popular deep learning frameworks such as TensorFlow or PyTorch. They will work on real-world problems, including image classification, natural language processing, and time-series prediction. Special attention will be given to optimization techniques, regularization methods, and strategies to improve model performance.

By the end of the course, students will have a solid understanding of how deep learning models function and how to apply them effectively in various domains. Whether aspiring to work in AI research, data science, or industry applications, this course will equip students with the skills needed to build and deploy powerful neural network models.

In this course you will learn:

• Introduction to Neural Networks
• Optimization and Training Techniques
• Deep Neural Networks (DNNs)
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs) and Sequence Models
• Natural Language Processing (NLP) with Deep Learning
• Generative Models and Unsupervised Learning
• Practical Implementation with TensorFlow/PyTorch
• Ethics and Challenges in Deep Learning