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Deep Learning Interview Questions
* It is computationally efficient compared to stochastic gradient descent.
* It improves generalization by finding flat minima.
* It improves convergence by using mini-batches. We can approximate the gradient of the entire training set, which might help to avoid local minima.
TensorFlow has numerous advantages, and some of them are as follows :
 
* Open-source
* Trains using CPU and GPU
* Has a large community
* High amount of flexibility and platform independence
* Supports auto differentiation and its features
* Handles threads and asynchronous computation easily
A Restricted Boltzmann Machine, or RBM for short, is an undirected graphical model that is popularly used in Deep Learning today. It is an algorithm that is used to perform:
 
* Dimensionality reduction
* Regression
* Classification
* Collaborative filtering
* Topic modeling
Leaky ReLU, also called LReL, is used to manage a function to allow the passing of small-sized negative values if the input value to the network is less than zero.
With the use of sequential processing, programmers were up against :
 
* The usage of high processing power
* The difficulty of parallel execution
* This caused the rise of the transformer architecture. Here, there is a mechanism called attention mechanism, which is used to map all of the dependencies between * sentences, thereby making huge progress in the case of NLP models.
The procedure of developing an assumption structure involves three specific actions.
 
* The first step contains algorithm development. This particular process is lengthy.
* The second step contains algorithm analyzing, which represents the in-process methodology.
* The third step is about implementing the general algorithm in the final procedure. The entire framework is interlinked and required for throughout the process.
An epoch is a terminology used in deep learning that refers to the number of passes the deep learning algorithm has made across the full training dataset. Batches are commonly used to group data sets (especially when the amount of data is very large). The term "iteration" refers to the process of running one batch through the model.
 
The number of epochs equals the number of iterations if the batch size is the entire training dataset. This is frequently not the case for practical reasons. Several epochs are used in the creation of many models.
 
There is a general relation which is given by :
 
d * e = i * b
 
where,
 
d - is the dataset size
 
e - is the number of epochs
 
i - is the number of iterations
 
b - is the batch size
Yes, if the problem is represented by a linear equation, deep networks can be built using a linear function as the activation function for each layer. A problem that is a composition of linear functions, on the other hand, is a linear function, and there is nothing spectacular that can be accomplished by implementing a deep network because adding more nodes to the network will not boost the machine learning model's predictive capacity.
Backpropagation in Recurrent Neural Networks differ from that of Artificial Neural Networks in the sense that each node in Recurrent Neural Networks has an additional loop as shown in the following image:
Recurrent Neural Network
This loop, in essence, incorporates a temporal component into the network. This allows for the capture of sequential information from data, which is impossible with a generic artificial neural network.
Following are the applications of autoencoders :
 
Image Denoising : Denoising images is a skill that autoencoders excel at. A noisy image is one that has been corrupted or has a little amount of noise (that is, random variation of brightness or color information in images) in it. Image denoising is used to gain accurate information about the image's content.

Dimensionality Reduction : The input is converted into a reduced representation by the autoencoders, which is stored in the middle layer called code. This is where the information from the input has been compressed, and each node may now be treated as a variable by extracting this layer from the model. As a result, we can deduce that by removing the decoder, an autoencoder can be utilised for dimensionality reduction, with the coding layer as the output.

Feature Extraction : The encoding section of Autoencoders aids in the learning of crucial hidden features present in the input data, lowering the reconstruction error. During encoding, a new collection of original feature combinations is created.

Image Colorization : Converting a black-and-white image to a coloured one is one of the applications of autoencoders. We can also convert a colourful image to grayscale.

Data Compression : Autoencoders can be used for data compression. Yet they are rarely used for data compression because of the following reasons:

* Lossy compression : The autoencoder's output is not identical to the input, but it is a near but degraded representation. They are not the best option for lossless compression.

* Data-specific : Autoencoders can only compress data that is identical to the data on which they were trained. They differ from traditional data compression algorithms like jpeg or gzip in that they learn features relevant to the provided training data. As a result, we can't anticipate a landscape photo to be compressed by an autoencoder trained on handwritten digits.