Keras Deep Learning Practice Exam

Keras is an neural networks API developed in Python programming language. It is open-source, and provides fast experimentation with deep learning models. It uses TensorFlow. It simplifies the creation, training, and deployment of deep learning models with an easy interface and is modular. It supports convolutional and recurrent networks with support for CPU and GPU. It is used in image recognition, natural language processing, and time-series forecasting.
Certification in Keras Deep Learning certifies your skills and knowledge to design, implement, and optimize deep learning models using Keras. This certification assess you in managing machine learning tasks, neural networks, and using Keras.
Why is Keras Deep Learning certification important?

• Demonstrates expertise in designing and implementing deep learning models.
• Validates the ability to work with advanced neural network architectures.
• Enhances credibility for roles in AI, machine learning, and data science.
• Showcases skills in using TensorFlow and Keras for real-world applications.
• Highlights proficiency in solving complex problems like image recognition and NLP.
• Boosts employability in industries leveraging artificial intelligence technologies.

Who should take the Keras Deep Learning Exam?

• Machine Learning Engineers.
• Data Scientists.
• Artificial Intelligence Specialists.
• Deep Learning Engineers.
• Research Scientists in AI and ML.
• Computer Vision Engineers.
• Natural Language Processing (NLP) Engineers.
• Software Engineers focusing on AI/ML solutions.
• AI Consultants and Analysts.
• Robotics Engineers leveraging AI technologies.

Skills Evaluated

Candidates taking the certification exam on the Keras Deep Learning is evaluated for the following skills:

• Designing and training neural networks using Keras.
• Implementing convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
• Optimizing deep learning models for performance.
• Working with TensorFlow and integrating it with Keras.
• Preprocessing and augmenting data for training models.
• Applying Keras to solve real-world problems such as image classification and text analysis.
• Evaluating and tuning deep learning models.

Keras Deep Learning Certification Course Outline
The course outline for Keras Deep Learning certification is as below -

• Overview of deep learning concepts.
• Setting up the Keras and TensorFlow environment.

• Sequential and functional API in Keras.
• Dense layers and activation functions.

• Convolutional Neural Networks (CNNs) for image processing.
• Recurrent Neural Networks (RNNs) for sequence data.
• Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs).

• Compiling and fitting models in Keras.
• Optimizers, loss functions, and metrics.
• Techniques for model evaluation and tuning.

• Handling and preprocessing datasets.
• Data augmentation techniques for image and text data.

• Implement computer vision tasks.
• Natural language processing
  
• Time-series forecasting
• Anomaly detection.

• TensorFlow backend
  
• Distributed training
  

• Saving and loading Keras models.
• Deploying models to production.