In the era of Data Science where knowledge of programming languages like Python and R is essential to implement the fundamental algorithms and techniques related to Machine learning and Data analytics.
Scaling the target value is a good idea in regression modelling; scaling of the data makes it easy for a model to learn and understand the problem.
Long Short Term Memory in short LSTM is a special kind of RNN capable of learning long term sequences. They were introduced by Schmidhuber and Hochreiter in 1997. It is explicitly designed to avoid long term dependency problems. Remembering the long sequences for a long period of time is its way of working.
Transfer learning is a technique for predictive modelling on a different yet similar problem that can then be reused partly or wholly to accelerate its training and eventually improve the performance of the model for the problem. It is the reuse of a pre-trained model on a new problem. This technique is currently becoming very popular in deep learning because it can train deep neural networks with comparatively little data and in less time. Finding its use in the data science field as most real-world problems typically do not have millions of labelled data points to train such complex models. Features from a model that has learned to identify something can become useful to kick-start a model meant to identify another thing.
To fill the gap between Source data (train data) and Target data (Test data) a concept called domain adaptation is used. It is the ability to apply an algorithm that is trained on one or more source domains to a different target domain.
the decision tree was an example of a black-box algorithm in any random forest, that we can access to know how it works.by visualizing trees in random forest we can know about the model
It combines Freeze Algorithm, AutoPipe, AutoDP, and AutoCache modules to increase the training speed significantly.
pooling layers are used in CNN for consolidating the features learned by the convolutional layer feature map. it helps in the reduction of overfitting in training
One hot encoding can be defined as the essential process of converting the categorical data variables to be provided to machine and deep learning algorithms which in turn improve predictions as well as classification accuracy of a model. One Hot Encoding is a common way of preprocessing categorical features for machine learning models.
Background subtraction is a widely used approach to detect moving objects in a sequence of frames from static cameras. The base in this approach is that of detecting moving objects from the difference between the current frame and reference frame, which is often called ‘Background Image’ or ‘Background Model’.
Recommender systems are information filtering systems that help deal with the problem of information overload by filtering and segregating information and creating fragments out of large amounts of dynamically generated information according to user’s preferences, interests, or observed behavior about a particular item or items. A Recommender system has the ability to predict whether a particular user would prefer an item or not based on the user’s profile and its historical information.
Components of time series are level, trend, season and residual/noise. breaking a time series into its component is decompose a time series.
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