Neural Network with Python & TensorFlow

Neural Network with Python & TensorFlow

• 1.
  Neural Network Count Chu
• 2.
  Topic • Simple Classifier –Learning Rate • Neural Node – Error Function – Learning Rate – Sigmoid Function • Neural Network – 3-Layer – N-Layer – Back-Propagating Errors – Slop of Error Function – Gradient Descent • Implement – DIY with Python – TensorFlow – Gradient Descent • Resources ‒ TensorFlow ‒ http://countchu2.blogspot.com ‒ http://blog.csdn.net/countchu/article
• 3.
  Training A SimpleClassifier
• 4.
  Apply Learning Ratein Classifier
• 5.
  Neural Node Transform theClassifier Problem into a Neural Network.
• 6.
  Error Function E =t - y E = (t1 - y1) + (t2 - y2) y = f (w * x) y = w * x
• 7.
  Apply Learning Ratein a Neural Node
• 8.
  Sigmoid Function y =Sigmoid (Sum (w * x))
• 9.
  Output in 3-LayerNeural Network
• 10.
  Output in N-LayerNeural Network
• 11.
  Back-Propagating Errors inNeural Network e = t - y
• 12.
  Slop of ErrorFunction in Neural Network
• 13.
  Gradient Descent inNeural Network
• 14.
  How to implement3-Layer Neural Network
• 15.
  DIY with Python
• 16.
  DIY with Python
• 17.
  Tensor Flow –Gradient Descent