Essentials of Deep Learning (Asynchronous e-Learning)
About This Course
Upon completion of this course, participants will be able to:
• Create deep learning models using Keras
• Apply the backpropagation algorithm to train deep learning models
• Analyze the performance of deep learning models and implement basic fine-tuning techniques
• Design Artificial Neural Networks to solve regression problems
• Develop Convolutional Neural Networks to solve image classification problems
• Formulate Recurrent Neural Networks to predict time series problems
What You'll Learn
Throughout the course, participants will learn and apply state-of-the-art techniques such as backpropagation for training models and fine-tuning for enhanced performance. By focusing on practical applications, attendees will gain hands-on experience in building and training Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks to tackle real-world challenges in regression, image classification, and time series prediction.
Upon completion, participants will possess the necessary skills to implement deep learning models effectively in their respective fields. They will also be better positioned to stay at the forefront of technological advancements, gaining a competitive edge in their careers. The course offers an unparalleled opportunity to delve into the world of deep learning, opening doors to new research, innovations, and professional growth. Don't miss this chance to expand your skillset and unlock the potential of deep learning in transforming your industry.
Entry Requirements
• Basic programming knowledge: Participants should possess a fundamental understanding of programming concepts and be familiar with at least one programming language, preferably Python, as it is widely used in the field of machine learning.
• Familiarity with machine learning concepts such as regression, classification, and clustering.
• Knowledge of data manipulation and analysis using libraries such as pandas and numpy.