Data Engineering For Deep Learning Training Course: Prepare Deep Learning Data

Introduction

Unlock the potential of deep learning with our Data Engineering for Deep Learning Training Course. This program is designed to equip you with the essential skills to prepare data for deep learning models, enabling you to build high-performance AI applications. In today's data-intensive world, mastering data engineering for deep learning is crucial for organizations seeking to leverage complex neural networks. Our deep learning data engineering training course offers hands-on experience and expert guidance, empowering you to optimize your data pipelines for deep learning model training and deployment.

This prepare deep learning data training delves into the core concepts of data engineering tailored for deep learning, covering topics such as large-scale data processing, feature extraction, and data augmentation. You'll gain expertise in using industry-standard tools and techniques to prepare data for deep learning models, meeting the demands of modern AI projects. Whether you're a data engineer, machine learning engineer, or AI researcher, this Data Engineering for Deep Learning course will empower you to design and implement efficient data pipelines for deep learning.

Target Audience:

• Data Engineers
• Machine Learning Engineers
• AI Researchers
• Data Scientists
• Deep Learning Practitioners
• Software Developers
• Anyone needing deep learning data engineering skills

Course Objectives:

• Understand the fundamentals of data engineering for deep learning.
• Master large-scale data processing and storage for deep learning.
• Utilize feature extraction and transformation techniques for deep learning models.
• Implement data augmentation and preprocessing for complex datasets.
• Design and build efficient data pipelines for deep learning training.
• Optimize data pipelines for GPU-accelerated training.
• Troubleshoot and address common issues in deep learning data preparation.
• Implement data versioning and reproducibility for deep learning projects.
• Integrate data pipelines with various deep learning frameworks.
• Understand how to handle unstructured data (images, text, audio) for deep learning.
• Explore advanced data engineering techniques for deep learning (e.g., distributed training, data sharding).
• Apply real world use cases for data engineering in deep learning projects.
• Leverage data engineering tools and libraries for efficient deep learning data preparation.

Duration

10 Days

Course content

Module 1: Introduction to Data Engineering for Deep Learning

• Fundamentals of data engineering for deep learning.
• Overview of large-scale data processing and feature engineering.
• Setting up a deep learning data engineering development environment.
• Introduction to data engineering tools and frameworks.
• Best practices for deep learning data engineering.

Module 2: Large-Scale Data Processing and Storage

• Mastering large-scale data processing and storage for deep learning.
• Utilizing distributed file systems and data lakes.
• Implementing data partitioning and sharding.
• Designing and building efficient data storage solutions.
• Best practices for large-scale data.

Module 3: Feature Extraction and Transformation

• Utilizing feature extraction and transformation techniques for deep learning models.
• Implementing techniques for image, text, and audio data.
• Designing and building feature extraction pipelines.
• Optimizing feature sets for deep learning models.
• Best practices for feature extraction.

Module 4: Data Augmentation and Preprocessing

• Implementing data augmentation and preprocessing for complex datasets.
• Utilizing techniques for image augmentation and text preprocessing.
• Designing and building data augmentation pipelines.
• Optimizing preprocessing for model performance.
• Best practices for data augmentation.

Module 5: Data Pipelines for Deep Learning Training

• Designing and building efficient data pipelines for deep learning training.
• Utilizing data pipeline frameworks (e.g., TensorFlow Data API, PyTorch DataLoader).
• Implementing automated data preprocessing workflows.
• Optimizing pipelines for GPU-accelerated training.
• Best practices for data pipelines.

Module 6: GPU-Accelerated Training Optimization

• Optimizing data pipelines for GPU-accelerated training.
• Utilizing data batching and caching techniques.
• Implementing data prefetching and asynchronous loading.
• Designing efficient data loading strategies.
• Best practices for GPU training.

Module 7: Troubleshooting Deep Learning Data Preparation

• Debugging common issues in deep learning data preparation.
• Analyzing data loading and preprocessing errors.
• Utilizing troubleshooting techniques for problem resolution.
• Resolving common data preparation errors.
• Best practices for troubleshooting.

Module 8: Data Versioning and Reproducibility

• Implementing data versioning and reproducibility for deep learning projects.
• Utilizing data version control systems (e.g., DVC).
• Implementing data lineage tracking.
• Designing reproducible data pipelines.
• Best practices for data versioning.

Module 9: Integration with Deep Learning Frameworks

• Integrating data pipelines with various deep learning frameworks.
• Utilizing TensorFlow, PyTorch, and other frameworks.
• Implementing data loaders and datasets for specific frameworks.
• Optimizing integration for model training.
• Best practices for framework integration.

Module 10: Handling Unstructured Data

• Understanding how to handle unstructured data (images, text, audio) for deep learning.
• Utilizing techniques for data parsing and embedding.
• Implementing data preprocessing for unstructured formats.
• Designing data pipelines for unstructured data.
• Best practices for unstructured data.

Module 11: Advanced Data Engineering Techniques

• Exploring advanced data engineering techniques for deep learning (distributed training, data sharding).
• Utilizing distributed data loading strategies.
• Implementing data sharding for large datasets.
• Designing and building advanced data pipelines.
• Optimizing advanced techniques for specific applications.
• Best practices for advanced techniques.

Module 12: Real-World Use Cases

• Implementing data engineering for computer vision applications.
• Utilizing data engineering for natural language processing tasks.
• Implementing data engineering for audio processing.
• Utilizing data engineering for recommendation systems.
• Best practices for real-world applications.

Module 13: Data Engineering Tools Implementation

• Utilizing data engineering tools and libraries (Apache Spark, TensorFlow Data API).
• Implementing data pipelines with specific tools.
• Designing and building automated data processing scripts.
• Optimizing tool usage for efficient development.
• Best practices for tool implementation.

Module 14: Data Performance Monitoring

• Implementing data performance monitoring.
• Utilizing data loading and preprocessing metrics.
• Designing and building performance dashboards.
• Optimizing monitoring for data pipeline efficiency.
• Best practices for monitoring.

Module 15: Future Trends in Deep Learning Data Engineering

• Emerging trends in deep learning data engineering.
• Utilizing AI for automated data preprocessing.
• Implementing data engineering for federated learning.
• Best practices for future applications.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.