Keras Training and Certification in USA

This course provides a comprehensive introduction to Keras, a high-level deep learning API that runs on top of TensorFlow. Participants will learn how to build, train, and deploy deep learning models using Keras, covering fundamental concepts like neural networks, activation functions, optimization techniques, and model evaluation. The course includes hands-on exercises with real-world datasets to help participants gain practical experience.

Welcome to the official Keras Training and Certification certification program. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge in in USA. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 14 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

• Basic proficiency in Python programming
• Understanding of machine learning and deep learning fundamentals
• Familiarity with NumPy, Pandas, and Matplotlib
• Knowledge of linear algebra and basic neural network concepts

• Building and training deep learning models using Keras
• Implementing optimization techniques for better model performance
• Working with CNNs, RNNs, and LSTMs in Keras
• Applying data augmentation and regularization techniques
• Deploying trained models in production environments
• Understanding and using TensorFlow backend features

By the end of the course, participants will be able to:

• Understand the fundamentals of deep learning and Keras architecture
• Work with Keras layers, models, and optimizers
• Build and train feedforward neural networks (FNNs) and convolutional neural networks (CNNs)
• Optimize deep learning models using loss functions and regularization techniques
• Use pre-trained models and transfer learning for efficient training
• Deploy trained Keras models using TensorFlow Serving and ONNX