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Pytorch Deep Learning

From Zero To Hero (BERT & T5)

Description

Pytorch&Hugginface Deep Learning Course(Colab Hands-On)

Welcome to Pytorch Deep Learning From Zero To Hero Series.


If you have already mastered the basic syntax of python and don't know what to do next, this course will be a rocket booster to skyrocket your programming skill to a business applicable level.


In this course, you will be able to master implementing deep neural network from the very beginning(simple perceptron) to BERT transfer learning/Google's T5 by using pytorch and huggingface yourself by colab. Each Section will have one assignment for you to think and code yourself.


The Agenda is below.   

Agenda:

  1. Introduction

  2. Google Colaboratory

  3. Neuron

  4. Perceptron

  5. Make Your Perceptron Trainable

  6. Normalize Data

  7. Activation Function

  8. Loss Function

  9. Gradient Descent

  10. Elegant Pytorch Gradient Descent

  11. Final Project

  12. Final Project Explained

  13. Multi Layer Perceptron(MLP)

  14. One Hot Encoding

  15. Prepare data for MLP

  16. Define MLP

  17. Train & Evaluate MLP

  18. Final Project for MLP

  19. FCNN Explained

  20. FCNN LOVE Letters Classification using MLP

  21. Final Project For FCNN

  22. CNN Explained

  23. CNN Prepare data(Fashion MNIST)

  24. CNN Define Model

  25. CNN Train&Evaluate Model

  26. CNNInference

  27. Final Project For CNN

  28. RNN Explained

  29. RNN Prepare data

  30. RNN Define Model

  31. RNN Train Model

  32. RNN Inference

  33. BERT Sesame Street

  34. BERT Prepare Data IMDB

  35. BERT Model definition

  36. BERT Model Training

  37. BERT Model Evaluation

  38. BERT Model Prediction

  39. BERT Final Project

  40. T5 Prepare Data

  41. T5 Model definition

  42. T5 Model Training

  43. T5 Model Evaluation

  44. T5 Model Prediction

  45. T5 Final Project


Let's start our journey together.


Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren’t special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one– and preferably only one –obvious way to do it.
Although that way may not be obvious at first unless you’re Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it’s a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea — let’s do more of those!

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