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MIT Introduction to Deep Learning (2022) | 6.S191

Alexander Amini
315K subscribers
626K views 3 years ago
MIT Introduction to Deep Learning 6.S191: Lecture 1 Foundations of Deep Learning Lecturer: Alexander Amini For all lectures, slides, and lab materials: http://introtodeeplearning.com/ Lecture Outline 0:00​ - Introduction 6:35 ​ - Course information 9:51​ - Why deep learning? 12:30​ - The perceptron 14:31​ - Activation functions 17:03​ - Perceptron example 20:25​ - From perceptrons to neural networks 26:37​ - Applying neural networks 29:18​ - Loss functions 31:19​ - Training and gradient descent 35:46​ - Backpropagation 38:55​ - Setting the learning rate 41:37​ - Batched gradient descent 43:45​ - Regularization: dropout and early stopping 47:58​ - Summary Subscribe to stay up to date with new deep learning lectures at MIT, or follow us on @MITDeepLearning on Twitter and Instagram to stay fully-connected!! ...more
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MIT Introduction to Deep Learning (2022) | 6.S191
MIT Introduction to Deep Learning 6.S191: Lecture 1 Foundations of Deep Learning Lecturer: Alexander Amini For all lectures, slides, and lab materials: http://introtodeeplearning.com/ Lecture Outline 0:00​ - Introduction 6:35 ​ - Course information 9:51​ - Why deep learning? 12:30​ - The perceptron 14:31​ - Activation functions 17:03​ - Perceptron example 20:25​ - From perceptrons to neural networks 26:37​ - Applying neural networks 29:18​ - Loss functions 31:19​ - Training and gradient descent 35:46​ - Backpropagation 38:55​ - Setting the learning rate 41:37​ - Batched gradient descent 43:45​ - Regularization: dropout and early stopping 47:58​ - Summary Subscribe to stay up to date with new deep learning lectures at MIT, or follow us on @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

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Alexander Amini

315K subscribers

MIT 6.S191: Introduction to Deep Learning
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MIT Introduction to Deep Learning | 6.S191

Alexander Amini
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MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention

Alexander Amini
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MIT 6.S191: Convolutional Neural Networks

Alexander Amini
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MIT 6.S191: Deep Generative Modeling

Alexander Amini
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MIT 6.S191: Reinforcement Learning

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MIT 6.S191: Language Models and New Frontiers

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MIT 6.S191 (Google): Large Language Models

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MIT 6.S191 (Liquid AI): Large Language Models

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MIT 6.S191 (Comet ML): A Hipocratic Oath, for *your* AI

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MIT 6.S191 (Microsoft): AI for Biology

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MIT Introduction to Deep Learning (2024) | 6.S191

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MIT 6.S191 (2024): Recurrent Neural Networks, Transformers, and Attention

Alexander Amini
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MIT 6.S191 (2024): Convolutional Neural Networks

Alexander Amini
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MIT 6.S191 (2024): Deep Generative Modeling

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MIT 6.S191 (2024): Reinforcement Learning

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MIT 6.S191 (2024): Language Models and New Frontiers

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MIT 6.S191 (2024): Google - Generative AI for Media

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MIT 6.S191 (2024): Building AI Models in the Wild

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MIT Introduction to Deep Learning (2023) | 6.S191

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MIT 6.S191 (2023): Recurrent Neural Networks, Transformers, and Attention

Alexander Amini
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MIT 6.S191 (2023): Convolutional Neural Networks

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MIT 6.S191 (2023): Deep Generative Modeling

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MIT 6.S191 (2023): Robust and Trustworthy Deep Learning

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MIT 6.S191 (2023): Reinforcement Learning

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MIT 6.S191 (2023): Deep Learning New Frontiers

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MIT 6.S191 (2023): Text-to-Image Generation

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MIT 6.S191 (2023): The Modern Era of Statistics

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MIT 6.S191 (2023): The Future of Robot Learning

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MIT Introduction to Deep Learning (2022) | 6.S191

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MIT 6.S191 (2022): Recurrent Neural Networks and Transformers

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MIT 6.S191 (2022): Convolutional Neural Networks

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MIT 6.S191 (2022): Deep Generative Modeling

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MIT 6.S191 (2022): Reinforcement Learning

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MIT 6.S191 (2022): Deep Learning New Frontiers

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MIT 6.S191: LiDAR for Autonomous Driving

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MIT 6.S191: Automatic Speech Recognition

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MIT 6.S191: AI for Science

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MIT 6.S191: Uncertainty in Deep Learning

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MIT 6.S191 (2021): Introduction to Deep Learning

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MIT 6.S191 (2021): Recurrent Neural Networks

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MIT 6.S191 (2021): Convolutional Neural Networks

Alexander Amini
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MIT 6.S191 (2021): Deep Generative Modeling

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MIT 6.S191 (2021): Reinforcement Learning

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MIT 6.S191 (2021): Deep Learning New Frontiers

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MIT 6.S191: Evidential Deep Learning and Uncertainty

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MIT 6.S191: AI Bias and Fairness

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MIT 6.S191: Deep CPCFG for Information Extraction

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MIT 6.S191: Taming Dataset Bias via Domain Adaptation

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MIT 6.S191: Towards AI for 3D Content Creation

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MIT 6.S191: AI in Healthcare

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MIT 6.S191 (2020): Introduction to Deep Learning

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MIT 6.S191 (2020): Recurrent Neural Networks

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MIT 6.S191 (2020): Convolutional Neural Networks

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MIT 6.S191 (2020): Deep Generative Modeling

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MIT 6.S191 (2020): Reinforcement Learning

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MIT 6.S191 (2020): Deep Learning New Frontiers

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MIT 6.S191 (2020): Neurosymbolic AI

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MIT 6.S191 (2020): Generalizable Autonomy for Robot Manipulation

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MIT 6.S191 (2020): Neural Rendering

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MIT 6.S191 (2020): Machine Learning for Scent

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Barack Obama: Intro to Deep Learning | MIT 6.S191

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MIT 6.S191 (2019): Introduction to Deep Learning

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MIT 6.S191 (2019): Recurrent Neural Networks

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MIT 6.S191 (2019): Convolutional Neural Networks

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MIT 6.S191 (2019): Deep Generative Modeling

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MIT 6.S191 (2019): Deep Reinforcement Learning

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MIT 6.S191 (2019): Deep Learning Limitations and New Frontiers

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MIT 6.S191 (2019): Visualization for Machine Learning (Google Brain)

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MIT 6.S191 (2019): Biologically Inspired Neural Networks (IBM)

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MIT 6.S191 (2019): Image Domain Transfer (NVIDIA)

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MIT 6.S191 (2018): Introduction to Deep Learning

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MIT 6.S191 (2018): Sequence Modeling with Neural Networks

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MIT 6.S191 (2018): Convolutional Neural Networks

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MIT 6.S191 (2018): Deep Generative Modeling

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MIT 6.S191 (2018): Deep Reinforcement Learning

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MIT 6.S191 (2018): Deep Learning Limitations and New Frontiers

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MIT 6.S191 (2018): Issues in Image Classification

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MIT 6.S191 (2018): Faster ML Development with TensorFlow

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MIT 6.S191 (2018): Deep Learning - A Personal Perspective

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MIT 6.S191 (2018): Beyond Deep Learning: Learning+Reasoning

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MIT 6.S191 (2018): Computer Vision Meets Social Networks

Alexander Amini