Module 1: Deep Learning Foundations

• Neural network mathematics and backpropagation
• Activation functions and loss landscapes
• Gradient descent optimization variants

Module 2: Keras Fundamentals and TensorFlow Backend

• Sequential vs Functional API model building
• Layers, optimizers, and callbacks configuration
• TensorFlow eager execution and graphs

Module 3: Feedforward Neural Networks

• Multi-layer perceptron architectures
• Batch normalization and layer normalization
• Dropout regularization techniques

Module 4: Convolutional Neural Networks (CNNs)

• Convolution operations and pooling layers
• Filter design and stride configurations
• Data augmentation strategies

Module 5: Advanced CNN Architectures

• VGGNet, ResNet residual connections
• Inception modules and efficient nets
• Object detection (YOLO, Faster R-CNN intro)

Module 6: Recurrent Neural Networks (RNNs)

• LSTM and GRU cell architectures
• Bidirectional recurrent layers
• Sequence-to-sequence models

Module 7: Attention Mechanisms and Transformers

• Self-attention and multi-head attention
• Transformer encoder-decoder architecture
• Positional encoding techniques

Module 8: Natural Language Processing with Deep Learning

• Word embeddings and tokenization
• Sequence classification and NER
• Text generation with LSTMs

Module 9: Generative Adversarial Networks (GANs)

• Generator-discriminator architecture
• Training stability techniques (WGAN)
• Conditional GAN variations

Module 10: Autoencoders and Dimensionality Reduction

• Denoising autoencoders
• Variational autoencoders (VAE)
• Anomaly detection applications

Module 11: Transfer Learning and Fine-tuning

• Pre-trained model loading (ImageNet)
• Feature extraction vs fine-tuning
• Domain adaptation strategies

Module 12: Model Optimization and Regularization

• Learning rate scheduling and warm restarts
• Early stopping and model checkpointing
• Class imbalance handling techniques

Module 13: Custom Layers and Models

• Lambda layers and custom training loops
• Model subclassing in Keras
• TensorFlow operations integration

Module 14: Production Deployment

• TensorFlow Serving and TensorFlow Lite
• ONNX model conversion
• Docker deployment pipelines

Experience in the Industry Gain knowledge from experts with practical Deep Learning with Python (Keras + TensorFlow) project experience in a variety of sectors.

Backgrounds at the Top Prominent corporations such as HCL, TCS, Accenture and Cognizant employ trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

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