This week, we will delve into the fascinating world of computer science and programming using Scratch, a visual programming language designed for beginners. Through hands-on projects, you will grasp fundamental concepts such as variables, loops, and conditional statements, empowering you to create your own interactive applications.
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MIT Introduction to Deep Learning (2023) | 6.S191
An introduction to deep learning covering fundamental concepts, techniques, and applications in the context of MIT's 6.S191 course.
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MIT 6.S191 (2023): Recurrent Neural Networks, Transformers, and Attention
Explore advanced architectures including Recurrent Neural Networks (RNNs), Transformers, and Attention mechanisms in deep learning.
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MIT 6.S191 (2023): Convolutional Neural Networks
Understand the principles and applications of Convolutional Neural Networks (CNNs), a cornerstone of computer vision and deep learning.
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MIT 6.S191 (2023): Deep Generative Modeling
Explore deep generative models and their applications in generating new data and understanding complex distributions.
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MIT 6.S191 (2023): Robust and Trustworthy Deep Learning
Learn about techniques to make deep learning models robust and trustworthy, addressing issues such as model reliability and security.
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MIT 6.S191 (2023): Reinforcement Learning
Delve into Reinforcement Learning (RL) concepts, including algorithms and their applications in decision-making and optimization tasks.
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MIT 6.S191 (2023): Deep Learning New Frontiers
Explore the latest advancements and emerging trends in deep learning, focusing on cutting-edge research and applications.
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MIT 6.S191 (2023): Text-to-Image Generation
Understand the techniques behind Text-to-Image Generation, including how text descriptions can be converted into realistic images.
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MIT 6.S191 (2023): The Modern Era of Statistics
Examine the intersection of modern statistics with deep learning, focusing on statistical methods and their applications in the current era.
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MIT 6.S191 (2023): The Future of Robot Learning
Explore the future directions in robot learning, including advancements in robotic perception, control, and autonomous decision-making.