knowledge Transfer

By the end of this project, you will be able to apply word embeddings for text classification, use LSTM as feature extractors in natural language processing (NLP), and perform binary text classification using PyTorch.

we need to create a matrix of one embedding for each word in the training dataset. We can do that by enumerating all unique words in the Tokenizer.word_index and locating the embedding weight vector from the loaded GloVe embedding.

The quality of the images will not be the same from each source. Some images might be of very high quality while others might be just plain bad. In such scenarios, we can blur the image. This helps make our deep learning model more robust. Transforms provide a class for randomly change the brightness, contrast, and saturation of an image.

Once you are done annotating your image dataset in the Pascal VOC format, you must convert your data into the TFRecord format. Because to use your own dataset in TensorFlow Object Detection API, you must convert it into the TFRecord file format.

The intuition behind slowly reducing the learning rate is that maybe during the initial steps of learning you can afford to take much bigger steps but then as learning approaches convergence then having a slower learning rate allows you to take smaller steps.

How easy this looks compared to when we had to construct a model through the class way of doing it. Sequential class lives in the neural network package and this is a class that we are building by or we’re building an instance of this class by passing in other modules in a sequential.

For Data analysis continuous data is often discretized or separated into “bins”.Suppose you have a list of people and their age and you want to group them into discrete age buckets.

Using the correlation coefficient you can find out how these two variables are related and to what degree. Please note that this is only a part of the whole dataset.

Z-score re-scale and center(Normalize) the data and look for data points which are too far from zero(center). Data points far from zero will be treated as the outliers. In most of the cases, a threshold of 3 or -3 is used i.e if the Z-score value is greater than or less than 3 or -3 respectively, that data point will be identified as outliers.

Many machine learning models are designed with the assumption that each feature values close to zero or all features vary on comparable scales. The gradient-based model assumes standardized data.