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Introduction to Deep Learning & Neural Networks

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Learn Deep Learning

Why Learn Deep Learning?

Neural Networks

Linear Classifiers Optimization and Gradient Descent Neural Networks Backpropagation Algorithm Build a Neural Network With Pytorch Quiz Yourself on Neural Networks

Training Neural Networks

Optimization Popular Optimization Algorithms Activation Functions Training in Pytorch Quiz Yourself on Training Neural Networks

Convolutional Neural Networks

The Principles of the Convolution Convolution in Practice Build a Convolutional Network Batch Normalization and Dropout Skip Connections CNN Architectures Quiz Yourself on CNNs

Recurrent Neural Network

A Simple RNN Cell LSTM: Long Short Term Memory Cells Writing a Custom LSTM Cell in Pytorch Connect LSTM Cells Across Time and Space Quiz Yourself on RNNs

Autoencoders

Generative Learning Basics of Autoencoders Variational Autoencoder: Theory Variational Autoencoder: Practice Quiz Yourself on Autoencoders

Generative Adversarial Networks

Generator and Discriminator Generative Adversarial Networks in Detail Develop a GAN with Pytorch Quiz Yourself on GANs

Attention and Transformers

Sequence to Sequence Models Attention Key Concepts of Transformers Self-Attention Multi-Head Self-Attention Transformers Building Blocks The Transformer's Encoder The Transformer's Decoder Build a Transformer Encoder Quiz Yourself on Transformers

Graph Neural Network

Basics of Graphs Mathematics for Graphs Graph Convolutional Networks Implementation of a GCN Quiz Yourself on GNNs

Conclusion

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Final Remarks

Final Remarks

Here's a quick summary of what we learned from the "Introduction to Deep Learning & Neural Networks" module.

Congratulations! You have successfully completed the “Introduction to Deep Learning & Neural Networks” module.

Summary

First, we examined the basic ...