Introduction to Deep Learning

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Introduction to Deep Learning

Introduction to Deep Learning

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Introduction
Generating Images from Natural Language

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What is Deep Learning?
Why Deep Learning and Why Now?
The Perceptron
Importance of Activation Functions
Building Neural Networks with Perceptrons
Multi Output Perceptron
Empirical Loss
Gradient Descent : Algorithm
Neural Networks in Practice : Optimization
Putting it all together
Mini-batches while training
Regularization I : Dropout

This course is designed to introduce learners to the basics of deep learning. Deep learning is a subset of machine learning that involves training neural networks to learn from data and make predictions or decisions. In this course, you will learn about the fundamental concepts of deep learning, including neural networks, backpropagation, activation functions, and gradient descent.

Plan Name	Price
3 Months:	Free

Alexander Amini

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In addition to being a co-founder and Chief Scientific Officer of Themis AI, Inc. with Prof. Daniela Rus, Alexander Amini is a postdoctoral associate at MIT. At MIT, he earned his Bachelor of Science (2017), Master of Science (2018), and PhD in Computer Science, with a minor in Mathematics.

Courses by this teacher

Name	Level	Release Date
Introduction to Deep Learning		04/26/2023
Deep Reinforcement Learning		06/21/2023

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