Advanced Deep Learning Architectures Specialization. This course provides comprehensive and advanced training in deep learning architectures, including neural networks, propagation models, and GPU-optimized training . Deep learning architectures are the driving force behind all major advances in AI, playing a fundamental role in the development of image recognition systems and large language models. This specialization provides a detailed and in-depth examination of the architectures that power modern AI. In this course, learners are taught how to build neural networks from scratch, build transformer models piece by piece, train generative systems, and master GPU infrastructure to scale these systems in operational environments. The concepts presented are reinforced through step-by-step coding demonstrations so that individuals can run programs on their own systems and practice at their own pace. In this specialization, each lesson includes practical training videos that are developed gradually. In the first part, a complete implementation of a neural network from scratch and the construction of an image classification system with convolutional neural networks are carried out. In the second part, the transformer structure is implemented along with multi-head attention mechanisms, positional coding, and efficiency enhancement mechanisms. In the third part, the training of a GAN generator is combined with the construction of a text-based conditional propagation system and the implementation of a multi-architecture application project with GPU . Finally, this course provides participants with the necessary expertise to design, optimize, and deploy advanced AI models.

What you will learn

• Building neural networks from the ground up : full implementation of forward pass , backpropagation , and training loops.
• Design and optimization of convolutional neural networks (CNN) : Development of CNN architectures for image classification, object recognition, and similarity learning.
• Implementing Transformer Models : Building transformer encoder-decoder models with Multi -Head Attention mechanism and positional coding.
• Training Generative Models : Training VAE , GAN , and Conditional Diffusion models for image generation.
• Graphics Infrastructure Management : Mastering training deep learning systems using GPU -accelerated pipelines .

This course is suitable for people who:

• Artificial Intelligence Engineers : Professionals seeking to advance their technical knowledge in the field of deep learning.
• Machine learning activists : People who are willing to go beyond simply calling APIs and understand models fundamentally.
• Researchers and investigators : Those who need a deep and precise mathematical and practical understanding of neural network design.
• Advanced level students : People who want to master modern AI architectures.