/Deep-Learning-Tensorflow-Basic

Seminar Materials

Primary LanguageJupyter Notebook

Deep Learning and Tensorflow Basic

This materials contain basic knowledge of deep learning and tensorflow.

  • 딥러닝_이론부터_실습까지_6시간 : 6시간~8시간 발표 분량의 요약 자료
  • Deep_Learning_Basic 1, 2: 전체 자료

1. deep_learing_basic:

presentation file of this seminar. It covers deep learning and tensorflow basic knowledge.

  • Introduction: Artificial Intelligence, Machine Learning, and Deep Learning
  • Regression and Classification
  • Artificial Neural Network
  • Tensorflow Practice #1 : Linear Regression and ANN
  • Tensorflow Basic : Basic Knowledge of Tensorflow
  • Convolutional Neural Netowrk
  • Tensorflow Practice #2 : ConvNet (MNIST)
  • Recurrent Neural Network
  • Tensorflow Practice #3 : Recurrenct models (S&P500)
  • How to avoid overfitting: Regularization, Validation data, Dropout
  • Learning much faster: Feature scaling, mini-batch, batch-normalization

2. Basic: Tensorflow Practice #1

  • Linear Regression
  • Artificial Neural Network
  • Artificial Nerual Network with mini-batch learning
  • Compute Gradient (including gradient cliiping)
  • Data Reader in Tensorflow

3. cnn: Tensorflow Practice #2

  • Convolutional Neural Network with MNIST dataset

4. rnn: Tensorflow Practice #3

  • Recurrent Neural Network with S&P 500