Deep Learning with Keras Training in Marina Bay, Singapore

The Deep Learning with Keras training offers a practical introduction to designing, building, and deploying deep learning models using the Keras library. Participants will explore key concepts such as neural network architecture, activation functions, loss functions, and model optimization while leveraging Keras's user-friendly API. Through hands-on exercises, this training enables participants to implement and fine-tune deep learning models for applications like image classification, text processing, and predictive analytics.

Introduction to Deep Learning and Keras Basics

1. Introduction to Deep Learning and Keras
• Overview of deep learning concepts and applications
• Introduction to the Keras API: Simple vs Functional models
• Installing and setting up Keras and TensorFlow
• Practical exercise
2. Neural Network Basics
• Understanding the architecture of neural networks: Layers, neurons, and weights
• Activation functions: ReLU, sigmoid, and softmax
• Loss functions and optimizers: MSE, categorical cross-entropy, Adam, and SGD
• Practical exercise
3. Training and Evaluating Models
• Training neural networks with Keras: Fitting, batch size, epochs
• Model evaluation metrics: Accuracy, precision, recall, F1-score
• Cross-validation and validation data usage
• Practical exercise

Advanced Models and Real-World Applications

4. Convolutional Neural Networks (CNNs)
• Introduction to CNNs and their applications in image recognition
• Layers in CNNs: Convolution, pooling, and fully connected layers
• Practical exercise: Building a CNN for image classification (e.g., CIFAR-10)
5. Recurrent Neural Networks (RNNs) and LSTMs
• Understanding RNNs for sequence data processing
• LSTMs and GRUs: Addressing long-term dependencies in data
• Practical exercise
6. Transfer Learning with Keras
• Understanding transfer learning and pre-trained models (e.g., VGG16, ResNet50)
• Fine-tuning pre-trained models for custom tasks
• Practical exercise
7. Model Deployment and Saving
• Saving and exporting trained models in Keras
• Using model.save() and tf.keras.models.load_model() for deployment
• Practical exercise
8. Course Recap and Q&A Session
• Recap of key concepts and techniques learned during the course
• Q&A session to address any challenges or queries
• Final thoughts on next steps in deep learning