Training Neural Networks: Activation Functions, Backpropagation, and TensorFlow Implementation Explore how neural networks are trained with gradient descent, softmax, and backpropagation using TensorFlow. Understand activation functions and multiclass classification techniques.
Introduction to Machine Learning Regression with Multiple Input Variables Logistic Regression: From Sigmoid to Regularization Understanding Neural Networks: From Biology to TensorFlow Training Neural Networks: Activation Functions, Backpropagation, and TensorFlow Implementation Practical Advice for Applying Machine Learning Decision Trees and Ensembles: From Splits to Random Forests and XGBoost Unsupervised Learning: K-means Clustering and Anomaly Detection 2.Neural network training
TensorFlow Implementation
Train a Neural Network in TensorFlow
Training Details
1. Create the model
2. Loss and cost functions
3. Gradient descent
Alternatives to the sigmoid activation
ReLU (Rectified Linear Unit)
Choosing activation functions
Hidden Layer
Why do we need activation functions?
Multiclass
target y can take on more than two possible values
Softmax
Cost
Neural network with softmax output
Improved implementation of softmax
Classification with multiple outputs
Advanced Optimization
Gradient Descent
Adam Algorithm Intuition
Additional Layer Types
Back propagation
What is a derivative?
Computation graph
Larger neural network example
