The book "Multimodal Deep Learning" provides an overview of the latest advancements in multimodal deep learning, focusing on the integration of different modalities such as text and images. It begins with an introduction to multimodal deep learning, explaining the importance of combining multiple information channels to understand complex environments, similar to how humans use their five senses. The book then delves into the state-of-the-art approaches in Natural Language Processing (NLP) and Computer Vision (CV), highlighting key concepts like word embeddings, encoder-decoder architectures, attention mechanisms, and transformers. The second part of the book explores various multimodal architectures, including models that transform one modality into another (e.g., Image2Text and Text2Image), models that enhance representation learning between modalities (e.g., Images supporting Language Models and Text supporting Vision Models), and models that handle both modalities simultaneously. It also covers the challenges and solutions for handling additional modalities, such as video and speech, and discusses the development of general-purpose multi-modal models. Finally, the book concludes with a discussion on the applications of multimodal deep learning, including generative art, where image generation models like DALL-E are used to create art pieces. The book emphasizes the importance of collaboration among students and the use of modern tools like Markdown, R, and GitHub for content creation and collaboration.The book "Multimodal Deep Learning" provides an overview of the latest advancements in multimodal deep learning, focusing on the integration of different modalities such as text and images. It begins with an introduction to multimodal deep learning, explaining the importance of combining multiple information channels to understand complex environments, similar to how humans use their five senses. The book then delves into the state-of-the-art approaches in Natural Language Processing (NLP) and Computer Vision (CV), highlighting key concepts like word embeddings, encoder-decoder architectures, attention mechanisms, and transformers. The second part of the book explores various multimodal architectures, including models that transform one modality into another (e.g., Image2Text and Text2Image), models that enhance representation learning between modalities (e.g., Images supporting Language Models and Text supporting Vision Models), and models that handle both modalities simultaneously. It also covers the challenges and solutions for handling additional modalities, such as video and speech, and discusses the development of general-purpose multi-modal models. Finally, the book concludes with a discussion on the applications of multimodal deep learning, including generative art, where image generation models like DALL-E are used to create art pieces. The book emphasizes the importance of collaboration among students and the use of modern tools like Markdown, R, and GitHub for content creation and collaboration.