Deep Learning powers today’s AI revolution — from self-driving cars to natural language processing and medical image analysis.

This course provides a hands-on introduction to deep learning using Python, TensorFlow, and Keras. You’ll learn the theory and application of neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), autoencoders, and generative adversarial networks (GANs).

With live mentor-led coding labs and pre-recorded tutorials, you’ll build multiple projects, including image classifiers, sentiment analysis models, and generative models. By the end, you’ll be able to design, train, and deploy deep learning models for real-world applications.

Target Audience

• Students and professionals exploring AI/Deep Learning
• Data scientists expanding beyond ML to DL
• Developers working on image, speech, or NLP applications
• Researchers interested in AI model building

Prerequisites

• Python programming (NumPy, Pandas, Matplotlib)
• Basic machine learning knowledge (regression/classification)
• Math foundations (linear algebra, probability helpful)

Learning Outcomes

By completing this course, learners will:

• Understand deep learning fundamentals & neural network architectures
• Build ANN, CNN, RNN, and LSTM models with TensorFlow/Keras
• Train models on real datasets (images, text, time series)
• Use transfer learning with pre-trained models
• Deploy deep learning models into applications
• Build advanced AI projects (image recognition, text generation, GANs)

Live Components

• Weekly mentor-led coding labs with TensorFlow/Keras
• Peer project reviews & group discussions
• Mock interview preparation for DL questions

Pre-recorded Access

• Tutorials on TensorFlow/Keras fundamentals
• Sample datasets (MNIST, CIFAR-10, IMDB, etc.)
• Recorded project walkthroughs