DEEP LEARNING

Deep Learning is a subset of machine learning that mimics the way the human brain works, using artificial neural networks to solve complex problems. In this course, you will explore advanced neural network architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). Whether you're interested in computer vision, natural language processing (NLP), or reinforcement learning, this course will teach you the foundational techniques to build and deploy powerful deep learning models. Get ready to dive deep into the future of AI technology!

Course Content Highlight:

Introduction to Deep Learning and Neural Networks
Understanding Artificial Neural Networks (ANNs)
Activation Functions, Loss Functions, and Optimizers
Backpropagation and Gradient Descent
Convolutional Neural Networks (CNNs) for Image Recognition
Recurrent Neural Networks (RNNs) for Sequence Data
Generative Adversarial Networks (GANs)
Transfer Learning and Pretrained Models
Hyperparameter Tuning and Model Evaluation
Deep Learning Frameworks (TensorFlow, Keras, PyTorch)
Real-World Deep Learning Applications (Computer Vision, NLP)
Building and Deploying Deep Learning Models

Training Mode:
Online

Training Tool:
Google Meet

Duration:
80 Hours

Deliverables:
Live Mentored Sessions: Personalized guidance from industry experts through live interactive sessions to clarify concepts and help with problem-solving.

Recorded Sessions: Access to video recordings of all classes for self-paced review and reference.

Daily Hands-on Exercises: Practical coding exercises to reinforce learning with real-world applications, enhancing your problem-solving skills.

Mini Projects: Small-scale projects to apply deep learning techniques, such as image classification or text analysis.

Capstone Project: A comprehensive final project where you build a complete deep learning solution, from data preprocessing to model deployment.

Quizzes & Assessments: Regular quizzes to test your understanding of key concepts and deep learning algorithms.

Peer Discussions & Collaboration: Opportunities to engage with fellow students for problem-solving, sharing insights, and collaborative learning.

Industry-Relevant Resources: Access to case studies, research papers, and real-world datasets to stay updated with the latest trends in deep learning.

Job-Ready Portfolio: A portfolio of projects showcasing your deep learning skills, ready for presentation to potential employers.

Certification: Receive a certificate upon successful completion of the course and Capstone Project, verifying your deep learning expertise.