Part II - Architectures
"The thing that excites me most about deep learning is that it can handle complex data and learn from it, revealing patterns and structures that were previously inaccessible." — Geoffrey Hinton
Part II of DLVR is dedicated to exploring the core architectures that have driven deep learning’s evolution and success across various domains. This section begins with Convolutional Neural Networks (CNNs), introducing their foundational principles and their role in image processing and computer vision tasks. It then progresses to modern CNN architectures, where cutting-edge designs like ResNet and EfficientNet showcase advances in efficiency, accuracy, and scalability. The focus then shifts to Recurrent Neural Networks (RNNs), delving into their structure and application in handling sequential data such as time series and text. This is followed by an exploration of modern RNN architectures, such as LSTMs and GRUs, which address the limitations of traditional RNNs and extend their capabilities for long-range dependencies. The section bridges traditional architectures and attention mechanisms in self-attention on CNNs and RNNs, demonstrating how attention improves context capture in complex data. The journey continues with Transformer architectures, covering their revolutionary impact on natural language processing and their extension to vision and other domains. Part II concludes with chapters on generative modeling, exploring Generative Adversarial Networks (GANs) for realistic data generation, Probabilistic Diffusion Models for controllable synthesis, and Energy-Based Models (EBMs) for flexible and interpretable data modeling.
🧠 Chapters
Notes for Implementation and Practice
For Students
To make the most of Part II, start by building a solid understanding of CNNs in Chapter 5. Implement simple architectures in Rust to gain hands-on experience with image data. As you delve into Chapter 6 on modern CNN architectures, compare their innovations and performance improvements, experimenting with how architectural adjustments impact results. Transition to RNNs in Chapter 7, focusing on implementing basic models and analyzing their strengths and weaknesses in processing sequential data.
For Practitioners
In Chapters 8 and 9, explore modern RNN architectures and self-attention mechanisms. Implement LSTMs and GRUs to understand their advantage in handling long-range dependencies. In Transformer Architectures (Chapter 10), work on implementing attention mechanisms and practice building components like encoders and decoders. For generative modeling chapters, experiment with GANs for synthetic data generation, Probabilistic Diffusion Models for controllable synthesis, and EBMs for flexible data modeling. Throughout this part, draw connections between these architectures and their applications, solidifying your expertise in designing and understanding state-of-the-art deep learning models.
To make the most of Part II, start by building a solid understanding of CNNs, implementing simple architectures in Rust to gain hands-on experience with image data. As you delve into modern CNN architectures, compare their innovations and performance improvements, experimenting with how architectural adjustments impact results. When studying RNNs, focus on implementing basic models and analyzing their strengths and weaknesses in processing sequential data. Progressing to modern RNN architectures, explore how LSTMs and GRUs overcome issues like vanishing gradients and practice applying them to tasks like language modeling or stock price prediction. In self-attention mechanisms, implement attention layers alongside CNNs and RNNs, and observe how they enhance feature extraction and context understanding. As you reach the chapter on Transformer architectures, work through implementing attention mechanisms and practice building components like encoders and decoders, linking their applications in tasks like translation or image classification. For the generative modeling chapters, GANs offer an opportunity to create realistic synthetic data, so experiment with their adversarial training setup. In Probabilistic Diffusion Models, investigate how iterative synthesis processes enhance control over generation. Finally, for Energy-Based Models, explore their unique capability to handle diverse learning problems, implementing their energy-based formulations to solidify your understanding. Throughout this part, draw connections between these architectures and their applications, solidifying your expertise in designing and understanding state-of-the-art deep learning models.