Mystery Vault Archives

Mystery Vault

Beginner’s Guide To Transfer Learning – How and When to Use?

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.

28/08/2021 1:00 pm

Mystery Vault

Understanding Direct Domain Adaptation in Deep Learning

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.

28/08/2021 10:00 am

Mystery Vault

How to Visualize a Random Forest with Fitted Parameters?

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

27/08/2021 11:00 am

Mystery Vault

All You Need To Know About PyTorch’s New PipeTransformer

It combines Freeze Algorithm, AutoPipe, AutoDP, and AutoCache modules to increase the training speed significantly.

26/08/2021 2:00 pm

Mystery Vault

Comprehensive Guide to Different Pooling Layers in Deep Learning

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

26/08/2021 10:00 am

Mystery Vault

When to Use One-Hot Encoding in Deep Learning?

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.

25/08/2021 4:00 pm

Mystery Vault

Using Background Subtraction Methods in Image Processing

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’.

25/08/2021 3:00 pm

Mystery Vault

Collaborative Filtering Vs Content-Based Filtering for Recommender Systems

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.

25/08/2021 11:00 am

Mystery Vault

Why Decompose a Time Series, and How?

Components of time series are level, trend, season and residual/noise. breaking a time series into its component is decompose a time series.

24/08/2021 5:00 pm

Mystery Vault

Detecting Orientation of Objects in Image using PCA and OpenCV

The Principal Component Analysis is a popular unsupervised learning algorithm that is widely known for dimensionality reduction. It increases the interpretability and also reduces the loss of information while reducing the dimensionality.

24/08/2021 2:00 pm

Mystery Vault

Facial Motion Capture for Animation Using First Order Motion Model

First Order Motion Model is an Open source library that allows you to create 3D animated videos using facial capture videos and still images. The image animation consists of generating a video sequence so that an object in a source image is animated according to the motion of the driving video.

23/08/2021 1:00 pm

Mystery Vault

Hands-On Guide to Bi-LSTM With Attention

Adding Attention layer in any LSTM or Bi-LSTM can improve the performance of the model and also helps in making prediction in a accurate sequence. very helpful in NLP modeling with big data

22/08/2021 4:00 pm

Categories

Category: Mystery Vault

Beginner’s Guide To Transfer Learning – How and When to Use?

Understanding Direct Domain Adaptation in Deep Learning

How to Visualize a Random Forest with Fitted Parameters?

All You Need To Know About PyTorch’s New PipeTransformer

Comprehensive Guide to Different Pooling Layers in Deep Learning

When to Use One-Hot Encoding in Deep Learning?

Using Background Subtraction Methods in Image Processing

Collaborative Filtering Vs Content-Based Filtering for Recommender Systems

Why Decompose a Time Series, and How?

Detecting Orientation of Objects in Image using PCA and OpenCV

Facial Motion Capture for Animation Using First Order Motion Model

Hands-On Guide to Bi-LSTM With Attention

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