Deep Learning

Introduction

Deep Learning is a transformative branch of Artificial Intelligence that enables machines to learn from large amounts of data and make intelligent decisions, powering applications such as image recognition, natural language processing, autonomous systems, and recommendation engines. This course is designed to provide a comprehensive understanding of deep learning concepts, techniques, and practical implementations, covering the complete workflow from problem definition to deployment.

The program starts with the foundations of deep learning, including statistics, linear algebra, probability, and core neural network concepts such as the simple perceptron and types of layers (input, hidden, dense). Participants will learn essential steps like data collection, preprocessing, exploratory data analysis (EDA), and data partitioning, along with metrics for regression and classification to evaluate model performance.

The course covers advanced topics such as optimizers, regularization, batch normalization, and different model-building techniques, followed by convolutional neural networks (CNNs), prebuilt models, transfer learning, and GANs for creative AI applications. Learners will also explore clustering, RBMs, embeddings, LSTMs, and NLP tasks including encoder-decoder architectures and transformers, as well as autoencoders for unsupervised learning.

Practical aspects like hyperparameter tuning, model evaluation, deployment, monitoring, handling data drift, and retraining are emphasized to prepare participants for real-world applications. The course also introduces deep learning in cloud environments, mobile and IoT deployment, and AutoML tools, equipping learners with skills to design, build, and manage scalable deep learning solutions.

By the end of this course, participants will have a solid foundation in deep learning theory, hands-on experience with modern tools and frameworks, and the ability to develop and deploy intelligent AI applications across diverse domains.