Comprehensive Impact of Renewable Energy Sources: Strategies for Integration and Sustainability
03/11/2024AI Ethics and Governance
03/11/2024Artificial Intelligence and Deep Learning
£4,500.00
Overview:
This advanced training program explores recent innovations in Artificial Intelligence (AI) and Deep Learning (DL). Targeted at professionals looking to leverage state-of-the-art AI techniques, the course emphasizes hands-on experience with neural networks, transformer models, and Generative AI techniques using frameworks like PyTorch and Hugging Face. The program also covers best practices for responsible AI deployment, interpretability, and the ethical implications of AI. By the end, participants will have the knowledge and skills to develop, deploy, and optimize AI-driven solutions tailored to complex, real-world applications.
Program Objectives:
At the end of this program, participants will be able to:
- Understand the evolution of AI, including Generative AI, transformer-based models, and recent advancements.
- Master the principles and architectures of Deep Learning, with a focus on CNNs, RNNs, and transformers.
- Design, build, and train neural networks using PyTorch and Hugging Face for various applications.
- Develop solutions for NLP, image processing, and other business use cases using modern AI architectures.
- Evaluate and optimize AI models with a focus on real-world performance and fairness.
- Deploy AI solutions on cloud platforms for scalable business integration.
- Incorporate ethical considerations, interpretability, and user-centered design into AI workflows.
Target Audience:
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- Data Scientists and Machine Learning Engineers
- Software Developers, IT Professionals, and Product Managers
- Business Analysts and Leaders focused on AI-driven decision-making
- AI Enthusiasts, Researchers, and Academics
- Professionals transitioning to AI or looking to expand into Deep Learning applications
Program Outline:
Day 1: Introduction to Artificial Intelligence and Current Trends
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This module provides foundational knowledge on AI’s recent advancements, current trends, and applications in industry.
- Scope and Applications of Artificial Intelligence in Modern Business.
- Evolution of AI – From Symbolic AI to Generative AI and Foundational Models.
- Key Concepts in AI – Machine Learning, Deep Learning, and Generative AI.
- Ethical AI Development – Interpretability, Transparency, and Bias.
- Hands-On Exercise: Setting up an AI environment with PyTorch and Hugging Face.
- Reflection & Review: Group discussion on the societal and ethical role of AI.
Day 2: Fundamentals of Deep Learning and Model Optimization
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A deep dive into the core concepts of Deep Learning, covering essential architectures and optimization strategies.
- Introduction to Deep Learning Concepts and Architectures.
- Fundamentals of Neural Networks – Neurons, Layers, and Activation Functions.
- Optimization Techniques – Backpropagation, Gradient Descent, Overfitting Solutions.
- Introduction to Regularization, Dropout, and Advanced Optimization Strategies.
- Hands-On Exercise: Building and training a basic neural network in PyTorch.
- Reflection & Review: Practical insights on the role of optimization in deep learning.
Day 3: Deep Learning with PyTorch and Hugging Face Transformers
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This module introduces hands-on PyTorch model building and the integration of Hugging Face transformers for NLP.
- Designing Neural Networks with PyTorch – Sequential and Functional APIs.
- Transfer Learning and Fine-Tuning Pre-trained Models.
- Hugging Face Transformers – BERT, GPT, and other state-of-the-art models.
- Model Compilation, Training, and Evaluation in PyTorch and Hugging Face.
- Hands-On Exercise: Building and fine-tuning a transformer model for NLP applications.
- Reflection & Review: Discussing model performance, scalability, and use cases.
Day 4: Advanced Deep Learning Architectures and Applications
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A comprehensive look at advanced architectures, with practical applications in image and sequence-based tasks.
- Convolutional Neural Networks (CNNs) for Image Processing and Object Detection.
- Recurrent Neural Networks (RNNs), LSTMs, and Attention Mechanisms for Sequential Data.
- Transformer Architectures for NLP, Image Generation, and Multimodal Applications.
- Hands-On Exercise: Building and training CNN and transformer models in PyTorch for image and NLP tasks.
- Reflection & Review: Analyzing architecture choice, task-specific performance, and optimization.
Day 5: Deployment, Scalability, and Ethical Considerations
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This final module focuses on deploying AI models, managing ethical implications, and future trends.
- Deployment Strategies for AI on Cloud Platforms (AWS, Google Cloud, Azure).
- Scalable AI Deployment for Business Integration and Real-Time Applications.
- Ethical AI and Fairness – Ensuring Transparency, Reducing Bias, and Improving Interpretability.
- Emerging Trends and the Future of AI – Large Language Models, Multimodal AI, and Responsible Innovation.
- Capstone Project: Developing and deploying a scalable AI application with PyTorch and Hugging Face.
- Reflection & Review: Presentations and group discussion on maximizing AI’s impact in business while upholding ethical standards.