WSQ Deep Learning (SF) (Synchronous and Asynchronous E-Learning)
About This Course
The "Deep Learning" module equips learners with essential knowledge and skills in the field of Artificial Intelligence (AI) and Deep Learning. Throughout this module, learners will gain a comprehensive understanding of AI and Deep Learning concepts, including Artificial Neural Networks, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). They will explore techniques for optimizing model performance and develop proficiency in implementing deep learning pipelines. Furthermore, learners will learn how to deploy Deep Learning models effectively in real-world applications.
By the end of this module, learners will be able to implement a Deep Learning model for image recognition using Convolutional Neural Networks (CNNs). They will acquire the necessary skills to design and develop Neural Networks that can effectively process and analyze images. Through hands-on projects, learners will gain practical experience in training CNNs, fine-tuning model performance, and deploying the trained models for real-world image recognition tasks. This module provides learners with the ability to apply Deep Learning techniques to solve complex image recognition problems, empowering them to contribute to advancements in the field of AI and Computer Vision.
Through a combination of theoretical learning, practical application, and project-based tasks, learners will develop a strong foundation in deep learning and acquire the skills necessary to embark on challenging AI projects. The "Deep Learning" module serves as a crucial steppingstone for learners to unlock their potential in the exciting and rapidly expanding field of AI.
What You'll Learn
This course comprises of following Instructional Units
1. Introduction to AI and Deep Learning
2. Artificial Neural Networks
3. Convolutional Neural Networks
4. Recurrent Neural Networks
5. Long Short-Term Memory
6. Transfer Learning
Knowledge Outcomes
Analyze the components of Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs).
Explain techniques for optimizing model performance in Deep Learning.
Recognize methods for deploying Deep Learning models in real-world applications.
Identify strategies for training and fine-tuning Convolutional Neural Networks (CNNs) for image recognition tasks.
Describe the theoretical foundations of Deep Learning and its application in Artificial Intelligence (AI).
Skills Outcomes
Construct Deep Learning models utilizing Artificial Neural Networks (ANNs).
Develop Deep Learning models employing Convolutional Neural Networks (CNNs)
Create Deep Learning models incorporating Recurrent Neural Networks (RNNs)
Implement Long Short-Term Memory (LSTM) architectures in Deep Learning models.
Incorporate Gated Recurrent Unit (GRU) architectures in Deep Learning models.
Entry Requirements
Academic Qualification - Minimum O Level credit in Maths or Minimum one credit in Nitech in STEM
Experience - 2 years experience in Programming or Data analytics