Mastering Advanced AI Architectures: A Deep Dive into Deep Learning

What you will learn:

Build and optimize cutting-edge deep learning models (CNNs, RNNs, Transformers, GANs, Diffusion Models)
Master reinforcement learning techniques (Q-Learning, DQNs, Policy Gradients)
Deploy AI models effectively using Flask, FastAPI, Docker, and cloud platforms
Analyze and interpret AI models responsibly with XAI techniques (SHAP, LIME, Attention)
Understand emerging trends in AI (multimodal systems, generative AI, AGI)

Description

Join our comprehensive course on cutting-edge AI technologies!

Unlock the power of advanced deep learning techniques with this in-depth specialization. Designed for aspiring AI professionals, this program goes beyond theoretical concepts, providing extensive hands-on experience through weekly coding labs.

You'll build a strong foundation in neural networks, mastering activation functions, loss functions, and optimization strategies, all reinforced by practical application. Progress through convolutional neural networks (CNNs), implementing classic architectures like LeNet, VGG, and ResNet, and applying them to image classification, object detection, and transfer learning tasks.

Next, you'll delve into the world of sequence modeling, constructing recurrent neural networks (RNNs), LSTMs, GRUs, and attention mechanisms, with labs focused on time-series forecasting, text generation, and visualizing attention processes. Explore the fascinating realm of transformers and natural language processing (NLP), implementing self-attention, experimenting with mini-transformers, and working with pretrained models like BERT and GPT, including labs on addressing bias and fairness in NLP systems.

The second half of the course introduces generative models, focusing on practical labs using autoencoders, variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models for creative AI applications. You'll then master reinforcement learning techniques, coding Q-learning, Deep Q-Networks (DQNs), and policy gradient methods to train agents in challenging environments like CartPole.

Finally, we address the crucial aspects of deployment, explainability, and ethical AI. Labs cover deploying models using Flask/FastAPI and Docker, employing SHAP/LIME for explainability, measuring fairness metrics, and working with multimodal AI systems. Prepare to confidently design, train, deploy, and evaluate modern AI systems in various real-world applications upon course completion.

Enroll now and transform your AI skills!

Curriculum

Week 1 - Neural Network Foundations

This introductory week covers the fundamental concepts of deep learning and neural networks. Lectures 1.1 and 1.2 lay the groundwork with introductions to deep learning and neural network basics, respectively. Lecture 1.3 focuses on training deep models. The hands-on lab reinforces these concepts with practical exercises. This section provides a solid base for understanding neural networks and their mechanics.

Week 2 - Optimization and Regularization

Week 2 tackles the challenges in training deep models, exploring regularization techniques to improve model performance. Lectures cover regularization methods (2.2), advanced optimization algorithms (2.3), and techniques like batch normalization and layer normalization (2.4). Lecture 2.1 introduces the challenges. The accompanying lab provides opportunities to implement these techniques.

Week 3 - Mastering Convolutional Neural Networks (CNNs)

This week dives deep into convolutional neural networks (CNNs). Lecture 3.1 introduces CNN fundamentals, followed by an exploration of various CNN architectures (3.2) and the powerful technique of transfer learning (3.3). Lecture 3.4 showcases practical applications. Hands-on lab exercises ensure solid understanding and skill development.

Week 4 - Recurrent Neural Networks (RNNs) and Sequence Modeling

Week 4 focuses on sequence modeling using RNNs. Lectures cover introductory sequence models (4.1), RNN basics (4.2), LSTMs and GRUs (4.3), and the crucial attention mechanism (4.4). The practical lab further cements the understanding of these sequence-based models.

Week 5 - Transformers and Natural Language Processing (NLP)

This week introduces the transformative world of transformers and their applications in NLP. Lectures cover the transformer architecture (5.1), prominent models like BERT and GPT (5.2), NLP applications (5.3), and ethical considerations (5.4) within NLP and LLMs. The practical lab provides hands-on experience with these advanced models.

Week 6 - Generative Model Techniques

Week 6 delves into generative models, covering autoencoders (including VAEs) (6.1), GANs (6.2), an introduction to diffusion models (6.3), and the applications of these models (6.4). The accompanying hands-on lab provides practical experience with these generative models.

Week 7 - Reinforcement Learning (RL) and Deep RL

Reinforcement learning takes center stage in this week. Lectures cover RL foundations (7.1), Q-learning and DQNs (7.2), policy gradient methods (7.3), and applications of RL (7.4). A practical lab reinforces these concepts by building and implementing RL agents.

Week 8 - Ethical AI, Deployment, and the Future

The final week addresses crucial aspects of ethical and practical AI development. Lectures cover AI deployment (8.1), explainable AI (XAI) using methods like SHAP and LIME (8.2), ethical AI and responsible AI practices (8.3), and a look into the future of deep learning (8.4). The accompanying lab emphasizes practical deployment, explanation, and ethical considerations.

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