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Softmax Activation Function: Everything You Need to Know
Have you ever trained a neural network to solve the problem of multiclass classification? If yes, you know that the raw outputs of the neural network are often very difficult to interpret. The softmax activation function simplifies this for you by making the neural network’s outputs easier to interpret!
13 min read
Cross-Entropy Loss: Make Predictions with Confidence
In classification problems, the model predicts the class label of an input. In such problems, you need metrics beyond accuracy. While accuracy tells the model whether or not a particular prediction is correct, cross-entropy loss gives information on how correct a particular prediction is. When training a classifier neural network, minimizing the cross-entropy loss during training is equivalent to helping the model learn to predict the correct labels with higher confidence.
12 min read
Introduction to K-Means Clustering
Under unsupervised learning, all the objects in the same group (cluster) should be more similar to each other than to those in other clusters; data points from different clusters should be as different as possible. Clustering allows you to find and organize data into groups that have been formed organically, rather than defining groups before looking at the data.
10 min read
Build Better Deep Learning Models with Batch and Layer Normalization
Batch and layer normalization are two strategies for training neural networks faster, without having to be overly cautious with initialization and other regularization techniques.
13 min read
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