Deep Learning
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Course Overview
This course covers:
Mathematical foundations of deep learning
Backpropagation & gradient descent
Activation functions
Convolutional Neural Networks (CNNs)
Basic Recurrent Neural Networks (RNNs)
Model optimization & regularization
Transfer learning
Deployment fundamentals
Primary tools:
Python
TensorFlow / Keras or PyTorch
Learning Peeks
Course Duration
3 Months
Course Structure
Instructor-Led
Availability
Available Online/Offline
Flexible Schedules
Flexible study schedules
Language
English Language
Training Days
Offline: Mon, Wed & Fri
Online: Tues, Thur & Fri
Recognized Certification
Earn a certification on completion
Course Outcome
By the end of this course, participants will:
Understand deep learning theory and architecture
Implement feedforward neural networks
Build CNNs for image classification
Apply basic RNN models
Optimize and tune deep learning models
Deploy simple AI systems
Present deep learning solutions professionally
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Our classrooms are carefully designed to facilitate effective teaching and learning to enable tutors to deliver dynamic and engaging lessons that captivate students attention and spark their curiosity.
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