With an increase in the number of layers, CNN captures high-level features which help differentiate between various classes of images. It is an observed fact that initial layers predominantly capture edges, the orientation of image and colours in the image which are low-level features. The features learned at each convolutional layer significantly vary. CNN models learn features of the training images with various filters applied at each layer.

Regression and Classification | Supervised Machine LearningĬonvolutional neural networks are very powerful in image classification and recognition tasks.

ML | Label Encoding of datasets in Python.

ML | One Hot Encoding to treat Categorical data parameters.

Introduction to Hill Climbing | Artificial Intelligence.

Best Python libraries for Machine Learning.

Activation functions in Neural Networks.

Elbow Method for optimal value of k in KMeans.

Decision Tree Introduction with example.

Linear Regression (Python Implementation).

Removing stop words with NLTK in Python.

ISRO CS Syllabus for Scientist/Engineer Exam.

ISRO CS Original Papers and Official Keys.

GATE CS Original Papers and Official Keys.

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