15 Topics
10 Days
72 Sessions
Programme Overview
Training Description
Who Should Attend
- Data Scientists
- Deep Learning Engineers
- Machine Learning Engineers
- AI Engineers and Developers
- Data Engineers
- Big Data Engineers
- TensorFlow Developers
- PyTorch Developers
- Computer Vision Professionals
- Natural Language Processing (NLP) Specialists
- AI Researchers and Research Scientists
- MLOps Engineers
- Cloud AI and Machine Learning Engineers
- Software Engineers developing AI applications
Session Objectives
- Understand the fundamentals of deep learning and neural networks.
- Master the TensorFlow and PyTorch frameworks for deep learning.
- Develop and train convolutional neural networks (CNNs) for image recognition.
- Implement recurrent neural networks (RNNs) for sequence data and time series analysis.
- Utilize transformers for natural language processing (NLP) tasks.
- Optimize deep learning models for performance and accuracy.
- Deploy deep learning models in production environments.
- Troubleshoot and debug deep learning models and pipelines.
- Implement data security and access control in deep learning workflows.
- Integrate deep learning models with big data platforms.
- Understand how to monitor and maintain deep learning models.
- Explore advanced deep learning techniques and architectures.
- Apply real world use cases for deep learning in Big Data.
About the Course
This advanced training course equips participants with the knowledge and practical skills required to develop, train, optimize, and deploy deep learning models at scale using large and complex datasets. It combines deep learning fundamentals with big data processing techniques, enabling participants to understand how modern AI architectures can be applied to high-volume, high-dimensional, and distributed data environments.Participants will work with leading deep learning frameworks such as TensorFlow and PyTorch, exploring neural network architectures, distributed training, GPU acceleration, data pipelines, model optimization, and scalable AI workflows. The course covers advanced architectures including CNNs, RNNs, Transformers, autoencoders, and transfer learning, with emphasis on selecting appropriate architectures for large-scale machine learning challenges.
Through practical exercises and real-world case studies, participants will learn how to prepare big datasets for deep learning, build scalable training pipelines, optimize computational resources, evaluate model performance, and deploy deep learning solutions in production environments.
Curriculum & Topics
15 Topics | 72 Sessions
-
Workshop 1.1:
Fundamentals of deep learning and neural networks.
-
Workshop 1.2:
Overview of TensorFlow and PyTorch frameworks.
-
Workshop 1.3:
Setting up a development environment.
-
Workshop 1.4:
Introduction to deep learning concepts and applications.
-
Workshop 1.5:
Best practices for deep learning.
-
Workshop 2.1:
Working with TensorFlow tensors and operations.
-
Workshop 2.2:
Building and training neural networks with Keras.
-
Workshop 2.3:
Implementing custom layers and models.
-
Workshop 2.4:
Utilizing TensorFlow Datasets and Data Pipelines.
-
Workshop 2.5:
Best practices for TensorFlow development.
-
Workshop 3.1:
Working with PyTorch tensors and automatic differentiation.
-
Workshop 3.2:
Building and training neural networks with PyTorch modules.
-
Workshop 3.3:
Implementing custom layers and models.
-
Workshop 3.4:
Utilizing PyTorch DataLoader and Transforms.
-
Workshop 3.5:
Best practices for PyTorch development.
-
Workshop 4.1:
Architecture and design of CNNs.
-
Workshop 4.2:
Implementing image classification and object detection.
-
Workshop 4.3:
Utilizing pre-trained CNN models.
-
Workshop 4.4:
Optimizing CNNs for performance.
-
Workshop 4.5:
Best practices for CNN development.
-
Workshop 5.1:
Architecture and design of RNNs.
-
Workshop 5.2:
Implementing sequence data analysis and time series forecasting.
-
Workshop 5.3:
Utilizing LSTM and GRU networks.
-
Workshop 5.4:
Handling variable-length sequences.
-
Workshop 5.5:
Best practices for RNN development.
-
Workshop 6.1:
Architecture and design of transformer models.
-
Workshop 6.2:
Implementing text classification and sentiment analysis.
-
Workshop 6.3:
Utilizing pre-trained transformer models (e.g., BERT, GPT).
-
Workshop 6.4:
Fine-tuning transformers for specific NLP tasks.
-
Workshop 6.5:
Best practices for transformer development.
-
Workshop 7.1:
Optimizing deep learning models for performance.
-
Workshop 7.2:
Implementing hyperparameter tuning and regularization.
-
Workshop 7.3:
Utilizing model compression and quantization.
-
Workshop 7.4:
Handling imbalanced datasets.
-
Workshop 7.5:
Best practices for model optimization.
-
Workshop 8.1:
Deploying deep learning models in production environments.
-
Workshop 8.2:
Utilizing containerization and orchestration tools (e.g., Docker, Kubernetes).
-
Workshop 8.3:
Implementing model serving and API endpoints.
-
Workshop 8.4:
Monitoring model performance in production.
-
Workshop 8.5:
Best practices for model deployment.
-
Workshop 9.1:
Debugging deep learning models and pipelines.
-
Workshop 9.2:
Analyzing model errors and performance issues.
-
Workshop 9.3:
Utilizing debugging tools and techniques.
-
Workshop 9.4:
Identifying and resolving model biases.
-
Workshop 9.5:
Best practices for model troubleshooting.
-
Workshop 10.1:
Implementing data security in deep learning workflows.
-
Workshop 10.2:
Utilizing authentication and authorization.
-
Workshop 10.3:
Implementing data encryption and masking.
-
Workshop 10.4:
Auditing and compliance in deep learning.
-
Workshop 10.5:
Best practices for data security.
-
Workshop 11.1:
Integrating deep learning models with Hadoop and Spark.
-
Workshop 11.2:
Utilizing cloud-based deep learning services (e.g., AWS SageMaker, Azure Machine Learning).
-
Workshop 11.3:
Implementing real-time deep learning pipelines.
-
Workshop 11.4:
Best practices for integration.
-
Workshop 12.1:
Monitoring model performance and drift.
-
Workshop 12.2:
Implementing model retraining and updating.
-
Workshop 12.3:
Utilizing model monitoring tools and techniques.
-
Workshop 12.4:
Handling model versioning and rollback.
-
Workshop 12.5:
Best practices for model maintenance.
-
Workshop 13.1:
Generative adversarial networks (GANs).
-
Workshop 13.2:
Reinforcement learning with deep neural networks.
-
Workshop 13.3:
Graph neural networks (GNNs).
-
Workshop 13.4:
Federated learning and privacy-preserving deep learning.
-
Workshop 13.5:
Advanced techniques for large-scale deep learning.
-
Workshop 14.1:
Utilizing cloud-based deep learning services.
-
Workshop 14.2:
Deploying deep learning models on AWS, Azure, and GCP.
-
Workshop 14.3:
Optimizing cloud resources for deep learning.
-
Workshop 14.4:
Best practices for cloud-based deep learning.
-
Workshop 15.1:
Emerging trends in deep learning research and applications.
-
Workshop 15.2:
Utilizing AI and automation in deep learning workflows.
-
Workshop 15.3:
Implementing explainable AI (XAI) for deep learning models.
-
Workshop 15.4:
Best practices for future deep learning.
