MIT 6S191: Recurrent Neural Networks Transformers and Attention
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This MIT 6S191 course provides an introduction to deep learning with a focus on recurrent neural networks, transformers, and attention. Lecturer Ava Amini will cover topics such as sequence modeling, neurons with recurrence, RNNs from scratch, design criteria for sequential modeling, backpropagation through time, long short term memory (LSTM), RNN applications, attention fundamentals, learning attention with neural networks, and scaling attention and applications. With all lectures, slides, and lab materials available online, this course is perfect for anyone looking to gain a better understanding of deep learning. Subscribe now to stay up to date with new deep learning lectures at MIT!
Neural Networks
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StatQuest's Neural Networks playlist is the perfect way to learn about this powerful Machine Learning technique. From the basics of Neural Networks to image classification with Convolutional Neural Networks, this playlist has it all. Get ready to become an expert in Neural Networks with StatQuest!
Multilayer Perceptron In 3 Hours Back Propagation In Neural Networks Great Learning
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Learn to build a multi-layer perceptron in just 3 hours with this great learning tutorial. It covers the fundamentals of back propagation in neural networks and provides a comprehensive overview of the MLP architecture. Get ready to dive into the world of deep learning!
Fundamentals of CNNs and RNNs
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This course covers the fundamentals of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), two powerful tools used in computer vision and natural language processing. Learn the concepts of CNNs, the two major operators (convolution and pooling), and the structure of CNNs. Also, understand the concept and structure of RNNs, and the two variants of RNNs, LSTMs and GRUs. By the end of this course, you will have the skills required for computer vision and natural language processing.
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