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.

Overview

Section 1: Week 1 - Foundations of Deep Learning & Neural Networks

Lecture 1 1.1 Introduction to Deep Learning

Lecture 2 1.2 Neural Networks Basics

Lecture 3 1.3 Training Deep Models

Lecture 4 Week 1 Hands-On Labs: Foundations of Deep Learning & Neural Networks

Section 2: Week 2 - Optimization & Regularization Techniques

Lecture 5 2.1 Challenges in Training Deep Models

Lecture 6 2.2 Regularization Methods

Lecture 7 2.3 Advanced Optimization Algorithms

Lecture 8 2.4 Batch Normalization & Layer Normalization

Lecture 9 Week 2 Hands-On Labs: Optimization & Regularization Techniques

Section 3: Week 3 - Convolutional Neural Networks (CNNs)

Lecture 10 3.1 CNN Fundamentals

Lecture 11 3.2 CNN Architectures

Lecture 12 3.3 Transfer Learning Fine-tuning pre-trained models

Lecture 13 3.4 Practical Applications – CNNs

Lecture 14 Week 3 Hands-On Labs: Convolutional Neural Networks (CNNs)

Section 4: Week 4 - Recurrent Neural Networks (RNNs) & Sequence Models

Lecture 15 4.1 Introduction to Sequence Models

Lecture 16 4.2 RNN Basics – Forward/Backpropagation Through Time

Lecture 17 4.3 LSTMs & GRUs

Lecture 18 4.4 Attention Mechanism

Lecture 19 Week 4 Hands-On Labs: RNNs & Sequence Models

Section 5: Week 5 - Transformers & Natural Language Processing (NLP)

Lecture 20 5.1 Transformer Architecture

Lecture 21 5.2 BERT, GPT, and Large Language Models (LLMs)

Lecture 22 5.3 Applications in NLP

Lecture 23 5.4 Ethical Considerations in NLP & LLMs

Lecture 24 Week 5 Hands-On Labs: Transformers & NLP

Section 6: Week 6 - Generative Models

Lecture 25 6.1 Autoencoders (Basic & VAE)

Lecture 26 6.2 Generative Adversarial Networks (GANs)

Lecture 27 6.3 Diffusion Models (Intro)

Lecture 28 6.4 Applications of Generative Models

Lecture 29 Week 6 Hands-On Labs: Generative Models

Section 7: Week 7 - Reinforcement Learning (RL) & Deep RL

Lecture 30 7.1 RL Foundations

Lecture 31 7.2 Q-Learning & Deep Q-Networks (DQN)

Lecture 32 7.3 Policy Gradient Methods (REINFORCE, Actor-Critic)

Lecture 33 7.4 Applications of Reinforcement Learning

Lecture 34 Week 7 Hands-On Labs: Reinforcement Learning & Deep RL

Section 8: Week 8 - Ethics, Deployment & Future of AI

Lecture 35 8.1 AI in Production

Lecture 36 8.2 Model Interpretability & Explainable AI (XAI)

Lecture 37 8.3 Ethical AI & Responsible AI

Lecture 38 8.4 The Future of Deep Learning

Lecture 39 Week 8 Hands-On Labs: Ethics, Deployment & Future of AI

Aspiring Data Scientists and Machine Learning Engineers,AI Enthusiasts and Researchers,Software Developers and Engineers,Students and Professionals in STEM fields,Entrepreneurs and Innovators