15 Topics
10 Days
74 Sessions
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Scientists
- AI Developers
- Machine Learning Engineers
- Researchers
- Software Developers
- Computer Vision Engineers
- Anyone needing deep learning skills
Session Objectives
- Understand the fundamentals of deep learning with TensorFlow/PyTorch.
- Master the architecture and implementation of neural networks.
- Utilize convolutional neural networks (CNNs) for image processing.
- Implement recurrent neural networks (RNNs) for sequence data.
- Design and build deep learning models for various applications.
- Optimize deep learning models for performance and accuracy.
- Troubleshoot and address complex deep learning challenges.
- Implement model evaluation and validation techniques.
- Integrate deep learning models into real-world applications.
- Understand how to tune hyperparameters for optimal performance.
- Explore advanced deep learning techniques (transfer learning, generative models).
- Apply real world use cases for CNNs and RNNs.
- Leverage TensorFlow/PyTorch for efficient deep learning implementation
About the Course
Master the core frameworks driving modern artificial intelligence with our Deep Learning with TensorFlow/PyTorch Training Course. Tailored for data scientists, AI developers, and researchers, this hands-on program provides deep practical expertise in architecting, training, and deploying advanced neural networks. Guided by industry experts, you will build Convolutional Neural Networks (CNNs) for computer vision, Recurrent Neural Networks (RNNs) for sequence analysis, and master optimization techniques to deploy high-performing models into production environments.
Curriculum & Topics
15 Topics | 74 Sessions
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Workshop 1.1:
Fundamentals of deep learning with TensorFlow/PyTorch.
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Workshop 1.2:
Overview of neural networks, CNNs, and RNNs.
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Workshop 1.3:
Setting up a deep learning development environment.
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Workshop 1.4:
Introduction to TensorFlow and PyTorch frameworks.
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Workshop 1.5:
Best practices for deep learning.
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Workshop 2.1:
Implementing basic neural network architectures.
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Workshop 2.2:
Utilizing activation functions and loss functions.
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Workshop 2.3:
Designing and building multi-layer perceptrons (MLPs).
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Workshop 2.4:
Optimizing neural network training.
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Workshop 2.5:
Best practices for neural networks.
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Workshop 3.1:
Implementing CNNs for image classification.
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Workshop 3.2:
Utilizing convolutional and pooling layers.
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Workshop 3.3:
Designing and building image recognition models.
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Workshop 3.4:
Optimizing CNNs for image processing tasks.
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Workshop 3.5:
Best practices for CNNs.
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Workshop 4.1:
Implementing RNNs for sequence data.
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Workshop 4.2:
Utilizing LSTM and GRU architectures.
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Workshop 4.3:
Designing and building sequence models.
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Workshop 4.4:
Optimizing RNNs for natural language processing.
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Workshop 4.5:
Best practices for RNNs.
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Workshop 5.1:
Designing deep learning models for specific applications.
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Workshop 5.2:
Implementing model architectures for various tasks.
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Workshop 5.3:
Utilizing transfer learning and fine-tuning techniques.
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Workshop 5.4:
Optimizing model design for performance.
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Workshop 5.5:
Best practices for model design.
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Workshop 6.1:
Optimizing deep learning models for accuracy.
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Workshop 6.2:
Utilizing hyperparameter tuning techniques.
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Workshop 6.3:
Implementing model compression and acceleration.
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Workshop 6.4:
Designing scalable deep learning solutions.
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Workshop 6.5:
Best practices for model optimization.
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Workshop 7.1:
Debugging complex deep learning issues.
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Workshop 7.2:
Analyzing model performance and errors.
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Workshop 7.3:
Utilizing troubleshooting techniques for model improvement.
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Workshop 7.4:
Resolving common deep learning challenges.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing cross-validation techniques.
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Workshop 8.2:
Utilizing performance metrics for model evaluation.
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Workshop 8.3:
Designing and building model validation pipelines.
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Workshop 8.4:
Optimizing model evaluation strategies.
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Workshop 8.5:
Best practices for model evaluation.
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Workshop 9.1:
Integrating deep learning models into production systems.
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Workshop 9.2:
Utilizing APIs and deployment tools.
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Workshop 9.3:
Implementing real-time deep learning applications.
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Workshop 9.4:
Optimizing models for deployment environments.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Utilizing grid search and random search for tuning.
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Workshop 10.2:
Implementing Bayesian optimization for hyperparameter selection.
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Workshop 10.3:
Designing and building hyperparameter tuning pipelines.
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Workshop 10.4:
Optimizing hyperparameters for model performance.
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Workshop 10.5:
Best practices for hyperparameter tuning.
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Workshop 11.1:
Implementing transfer learning for model reuse.
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Workshop 11.2:
Utilizing generative models (GANs, VAEs).
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Workshop 11.3:
Designing and building advanced deep learning architectures.
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Workshop 11.4:
Optimizing advanced techniques for specific tasks.
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Workshop 11.5:
Best practices for advanced techniques.
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Workshop 12.1:
Implementing CNNs for medical image analysis.
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Workshop 12.2:
Utilizing RNNs for sentiment analysis.
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Workshop 12.3:
Implementing deep learning for autonomous driving.
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Workshop 12.4:
Utilizing deep learning for fraud detection.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing TensorFlow for deep learning implementation.
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Workshop 13.2:
Implementing PyTorch for model development.
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Workshop 13.3:
Designing and building deep learning pipelines with frameworks.
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Workshop 13.4:
Optimizing framework usage for efficient training.
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Workshop 13.5:
Best practices for framework implementation.
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Workshop 14.1:
Implementing model interpretability techniques.
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Workshop 14.2:
Utilizing visualization tools for model understanding.
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Workshop 14.3:
Designing and building interpretable deep learning models.
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Workshop 14.4:
Optimizing model transparency.
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Workshop 14.5:
Best practices for model interpretability.
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Workshop 15.1:
Emerging trends in deep learning.
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Workshop 15.2:
Utilizing automated machine learning (AutoML) for deep learning.
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Workshop 15.3:
Implementing federated learning for distributed models.
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Workshop 15.4:
Best practices for future deep learning.
