Deep Learning with Keras is a focused two-day training program designed to help participants quickly build, train, and evaluate deep learning models using the Keras high-level API. This course emphasizes simplicity, clarity, and practical understanding of deep learning workflows, enabling learners to develop neural network models efficiently without deep framework complexity, while still following best practices for real-world AI applications.

• Understand deep learning with Keras
• Learn neural network development basics
• Understand Keras model workflows
• Gain knowledge of model training techniques
• Learn image and speech AI concepts
• Understand model optimization basics
• Explore Keras libraries and tools
• Identify practical deep learning use cases

• Aspiring deep learning practitioners
• Data scientists and machine learning engineers
• Software developers working with AI models
• AI and analytics professionals
• Students and early-career technologists

• Basic understanding of machine learning concepts
• Familiarity with Python programming fundamentals
• Awareness of neural networks and deep learning terminology
• Interest in rapid deep learning model development

• Building deep learning models using Keras
• Implementing neural network architectures efficiently
• Training and evaluating deep learning models
• Applying Keras to practical AI use cases
• Using responsible and interpretable deep learning practices

By the end of this training, participants will be able to build and train deep learning models using Keras, prepare and preprocess data effectively, implement CNN-based solutions, optimize model performance, and apply responsible deep learning practices in practical AI projects.

Introduction to Keras and Deep Learning Workflow
• Overview of Keras and its role in deep learning
• Relationship between Keras and TensorFlow
• End-to-end deep learning workflow using Keras
• When to use Keras for model development

Building Neural Networks with Keras
• Sequential and functional API concepts
• Layers, activation functions, and loss functions
• Compiling and training neural networks
• Evaluating model performance

Data Preparation for Keras Models
• Preparing datasets for deep learning
• Data normalization and preprocessing
• Training, validation, and testing splits
• Managing input pipelines

Convolutional Neural Networks with Keras
• CNN fundamentals and use cases
• Implementing image classification models
• Feature extraction and pooling layers
• Evaluating CNN model results

Model Optimization and Regularization
• Preventing overfitting and underfitting
• Dropout and regularization techniques
• Hyperparameter tuning basics
• Improving model generalization

Responsible Deep Learning and Practical Deployment
• Model explainability and trust
• Bias, fairness, and ethical considerations
• Deployment readiness concepts
• Best practices for real-world Keras models

• Keras fundamentals
• Neural network concepts
• Model training and evaluation
• CNN and RNN basics
• Data preprocessing workflows
• Image and speech AI applications
• Keras tools and libraries
• Model optimization techniques
• Performance evaluation concepts
• Practical Keras scenarios

• Hands-on Keras model building exercises
• Model training and evaluation assessment
• CNN implementation activity
• Final knowledge evaluation quiz

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in Deep Learning with Keras Training, validating their expertise in building, training, and applying deep learning models using Keras for practical AI applications.