Deep Learning

Master neural networks, CNNs, RNNs and modern architectures with TensorFlow and PyTorch through hands-on projects.

Course Details

Course Highlights

Comprehensive DL Training

From neural network basics to cutting-edge architectures and applications.

Hands-on Projects

Build and train models for computer vision, NLP, and generative tasks.

Industry Frameworks

Master TensorFlow, Keras, and PyTorch with production-grade practices.

Capstone Project

Complete an end-to-end DL project with model deployment.

Learning Outcomes

By the end of this course, you will be able to:

  • Design and train various neural network architectures
  • Implement CNNs for computer vision tasks
  • Build RNNs/LSTMs for sequence data
  • Use transfer learning effectively
  • Deploy DL models in production
  • Optimize model performance and interpret results

Course Curriculum

Module 1: Neural Network Fundamentals
  • Introduction to Deep Learning
  • Neural Network Mathematics
  • Training Deep Networks
  • Activation Functions
  • Optimization Techniques
Module 2: Convolutional Neural Networks
  • CNN Architectures
  • Image Classification
  • Object Detection
  • Transfer Learning
  • Data Augmentation
Module 3: Recurrent Neural Networks
  • RNN Fundamentals
  • LSTMs and GRUs
  • Sequence Prediction
  • Time Series Analysis
  • Attention Mechanisms
Module 4: Advanced Architectures
  • Transformers
  • Autoencoders
  • GANs
  • Reinforcement Learning
Module 5: Deployment & Optimization
  • Model Quantization
  • TensorFlow Serving
  • ONNX Format
  • Edge Deployment

Who Should Attend?

ML Engineers

Professionals looking to specialize in deep learning architectures.

Computer Vision Engineers

Developers working on image/video processing applications.

NLP Specialists

Those working on text/speech recognition and generation.

AI Researchers

Individuals exploring cutting-edge neural network architectures.