Deep Learning Specialization Advanced AI Architectures

Master advanced AI with Deep Learning, Transformers, GANs, RL & real-world deployment skills
What you'll learn
Design, train, and optimize advanced deep learning models including CNNs, RNNs, Transformers, GANs, and Diffusion Models for real-world applications.
Apply reinforcement learning techniques such as Q-Learning, Deep Q-Networks, and Policy Gradient methods
Deploy deep learning models into production environments using Flask, FastAPI, Docker, and cloud platforms (AWS, GCP, Azure)
Interpret and evaluate AI models responsibly using Explainable AI (XAI) methods like SHAP, LIME, and attention visualization
Analyze emerging AI trends including multimodal systems, generative AI, and the path toward Artificial General Intelligence (AGI)
Requirements
Basic Knowledge of Python
Foundational Understanding of Machine Learning
Linear Algebra & Probability Basics
Deep Learning Frameworks (Optional but Helpful)
Tools & Setup
Description
"This course contains the use of artificial intelligence in creating scripts, visuals, audio, and supporting content"The Deep Learning Specialization: Advanced AI is designed for learners who want to master state-of-the-art deep learning techniques while applying them in practical, hands-on labs every week. This course goes beyond theory — each section includes guided coding labs where you'll implement algorithms, experiment with models, and solve real-world problems.You'll begin with the foundations of neural networks, learning about activation functions, loss functions, and optimization techniques, supported by labs that show you how to build and train models from scratch. You'll then dive into Convolutional Neural Networks (CNNs), working with classic architectures like LeNet, VGG, and ResNet, and applying them in labs on image classification, object detection, and transfer learning.Next, you'll explore sequence models, building RNNs, LSTMs, GRUs, and attention mechanisms, with labs on time-series forecasting, text generation, and attention visualizations. Moving into transformers and NLP, you'll implement self-attention, experiment with mini-transformers, and work with pretrained models like BERT and GPT, plus labs that explore bias and fairness in NLP systems.In the second half, you'll experiment with generative models through labs on autoencoders, VAEs, GANs, and diffusion models for creative AI applications. You'll then apply reinforcement learning, coding Q-learning, DQNs, and policy gradient methods to train agents in environments like CartPole. Finally, you'll tackle deployment, explainability, and ethics, with labs on Flask/FastAPI + Docker deployment, SHAP/LIME explainability, fairness metrics, and multimodal AI demos.By the end of this specialization, you'll not only understand advanced deep learning architectures but will have practical experience from weekly labs to confidently design, train, deploy, and evaluate modern AI systems in real-world contexts.
Who this course is for
Aspiring Data Scientists and Machine Learning Engineers
AI Enthusiasts and Researchers
Software Developers and Engineers
Students and Professionals in STEM fields
Entrepreneurs and Innovators
Homepage

https://www.udemy.com/course/deep-learning-specialization-advanced-ai-architectures/

Rapidgator
nndml.Deep.Learning.Specialization.Advanced.AI.Architectures.part1.rar.html
nndml.Deep.Learning.Specialization.Advanced.AI.Architectures.part2.rar.html
nndml.Deep.Learning.Specialization.Advanced.AI.Architectures.part3.rar.html

nndml.Deep.Learning.Specialization.Advanced.AI.Architectures.part1.rar.html
nndml.Deep.Learning.Specialization.Advanced.AI.Architectures.part2.rar.html
nndml.Deep.Learning.Specialization.Advanced.AI.Architectures.part3.rar.html

FreeDL
nndml.Deep.Learning.Specialization.Advanced.AI.Architectures.part1.rar.html
nndml.Deep.Learning.Specialization.Advanced.AI.Architectures.part2.rar.html
nndml.Deep.Learning.Specialization.Advanced.AI.Architectures.part3.rar.html