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1
Intro
2
What is Deep Learning
3
Deep Learning Success: Vision
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Deep Learning Success: Audio
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Administrative Information
6
Final Class Project
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Class Support
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Course Staff
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Why Deep Learning
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The Perceptron: Forward Propagation
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Common Activation Functions
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Importance of Activation Functions
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The Perceptron: Example
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The Perceptron: Simplified
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Multi Output Perceptron
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Single Layer Neural Network
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Deep Neural Network
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Quantifying Loss
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Empirical Loss
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Binary Cross Entropy Loss
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Mean Squared Error Loss
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Loss Optimization
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Computing Gradients: Backpropagation
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Training Neural Networks is Difficult
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Setting the Learning Rate
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Adaptive Learning Rates
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Adaptive Learning Rate Algorithms
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Stochastic Gradient Descent
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The Problem of Overfitting
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Regularization 2: Early Stopping
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Core Foundation Review
Description:
Explore the foundations of deep learning in this introductory lecture from MIT's 6.S191 course. Delve into key concepts including perceptrons, neural networks, activation functions, loss quantification, backpropagation, and optimization techniques. Learn about the challenges of training neural networks, adaptive learning rates, and strategies to prevent overfitting. Gain insights into deep learning's successes in vision and audio applications, and understand why this field has become so influential. Access additional lectures covering topics such as sequence modeling, computer vision, generative models, reinforcement learning, and industry perspectives from leading tech companies.

Introduction to Deep Learning - MIT 2018

Alexander Amini
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