Overview:

Deep learning has revolutionized industries by enabling machines to learn from vast amounts of data. This course, Deep Learning Neural Network with R, provides a comprehensive introduction to deep learning concepts and their implementation using R. You will explore neural networks, activation functions, backpropagation, and optimization techniques. The course covers popular deep learning frameworks in R, such as Keras and TensorFlow, helping you build and train complex neural networks.

By the end of this course, you will have hands-on experience developing deep learning models for classification, regression, and image recognition. Additionally, we will discuss hyperparameter tuning, model evaluation, and real-world applications of deep learning. Whether you are a data scientist, analyst, or researcher, this course will equip you with the practical skills needed to build powerful AI models using R.

Learning Outcomes:

By the end of this course, you will be able to:

• Understand the fundamentals of deep learning and neural networks.
• Implement deep learning models using R and its libraries.
• Train and optimize neural networks using backpropagation and activation functions.
• Apply deep learning techniques to real-world datasets.
• Perform hyperparameter tuning for improved model accuracy.
• Evaluate model performance and prevent overfitting.
• Develop classification, regression, and image recognition models.
• Utilize TensorFlow and Keras for deep learning in R.

Description:

This course is designed to introduce you to deep learning concepts using R. You will start with the basics of neural networks, exploring their structure and key components. Then, you will learn how to implement and train deep learning models using R’s powerful libraries, including TensorFlow and Keras. The course includes practical examples and real-world case studies to enhance your understanding.

You will work with different types of neural networks, such as feedforward, convolutional, and recurrent networks, to tackle problems in classification, regression, and image recognition. Additionally, you will learn about optimization techniques, regularization methods, and strategies to improve model performance. By the end of the course, you will have the confidence to build and deploy deep learning models in various applications.

Who is This Course For?

This course is ideal for data scientists, analysts, researchers, and AI enthusiasts looking to expand their knowledge of deep learning with R. It is suitable for professionals in machine learning, finance, healthcare, and academia who want to develop AI-driven solutions. Basic knowledge of R and machine learning concepts is recommended but not mandatory.

Career Path:

Completing this course can open doors to various career opportunities, including:

• Deep Learning Engineer
• Data Scientist
• AI Researcher
• Machine Learning Engineer
• Business Intelligence Analyst
• Computational Scientist
• AI Consultant