neural networks

Akhil PandeyResearch Assistant, DATA Lab, CSCI

https://akhilpandey95.com

brief introduction

introduction

backpropagation

Multi Layer Perceptron

RNN, Long Short Term Memory Networks

applications of lstm

the terminology

1. input layer

2. hidden layer

3. output layer

4. activation function

5. loss function

6. network weights

7. biases

introduction

backpropagation

Multi Layer Perceptron

RNN, Long Short Term Memory Networks

applications of lstm

understanding backpropagation

...cont

introduction

backpropagation

Multi Layer Perceptron

RNN, Long Short Term Memory Networks

applications of lstm

understanding MLP

MLP backpropagation

introduction

backpropagation

Multi Layer Perceptron

RNN, Long Short Term Memory Networks

applications of lstm

understanding RNN

Source: Christopher Colah (http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

understanding LSTM

Source: Christopher Colah (http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

….cont

Source: Shi Yan (https://medium.com/mlreview/understanding-lstm-and-its-diagrams-37e2f46f1714)

LSTM backpropagation

Source: Aidan Gomez (https://medium.com/@aidangomez/let-s-do-this-f9b699de31d9)

interesting references

● Christopher olah’s blog posts (http://colah.github.io/)

● 3blue1brown neural networks playlist (https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)

● Python neural network tutorial (https://www.python-course.eu/neural_networks.php)

● Chapter 2 | Neural Networks and Deep Learning by Micheal Nielson (http://neuralnetworksanddeeplearning.com/chap2.html)

● Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville (http://www.deeplearningbook.org/)

● The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome Friedman

any questions ?

thank you