Deep Learning - Page 4 of 6

10 Tips for Training Deep Learning Models

Deep learning models have made significant impact in fields ranging from computer vision to natural language processing. However, training these models can be a daunting task that requires a lot of knowledge and expertise. In this blog, we will see 10 tips for training sustainable deep learning models. 1 – Start with a small dataset:…

3 Important Neural Network Architectures Explained

1. Perceptron The perceptron is the most basic of all neural networks, being a fundamental building block of more complex neural network. If simple connects an input cell and an output cell. 2. Feed-Forward Network The feed-forward network is a collection of perceptions’. In which there are three fundamental types of layers – input layers,…

Important Deep learning Concept Explained Part – 2

Converge Algorithm that converges will eventually reach an optimal answer, even if very slowly. An algorithm that doesn’t converge may never reach an optimal answer. Learning Rate Rate at which optimizers change weights and biases. High learning rate generally trains faster but risks not converging whereas a lower rate trains slower. Numerical instability Issues with…

Important Deep learning Concept Explained Part – 1

Neuron Node is a NN, typically taking in multiple input values and generating one output value by applying an activation function (nonlinear transformation) to weighted sum of input values. Weights Edges is a NN, the goal of training is to determine the optimal weight for each feature; if weight = 0, corresponding feature does not…

Top 8 Deep Learning Algorithms

Convolutional Neural Networks CNN’s popularly known as ConvNets majority consists of several layers and are specifically used for image processing and detection of objects. It was developed in 1998 by Yann LeCun. CNNs have wide usage in identifying the image of the satellites, medical image processing, series forecasting, and anomaly detection. CNNs process the data…

Loss Functions and optimizers and its type?

With modelling, there’s a particular goal that the model needs to achieve. It’s just as important to achieve the best possible values of the model parameters as it is to find out what each parameter means in terms of that goal. The loss function (cost function) is minimized, therefore getting unknown values for weight and…

Deep Learning interview questions Part -2

1 – What are autoencoders? Explain the different layers of autoencoders. Autoencoders are neural networks that are trained to reconstruct an input data into a desired output data. They can be thought of as the opposite of a traditional classifier, which is trained to classify inputs into pre-defined classes. Autoencoders can be seen as a…

Deep Learning interview questions Part -1

1 – What is data normalization? What’s the need for it? Data normalization is a process of transforming data from one format to another in order to improve the quality of the data and make it more usable for analysis. In this process data is organized and formatted in such a way that it’s easier…

What is Tanh activation function?

The Tanh Activation function is a scaled and shifted version of the hyperbolic tangent function, a mathematical function frequently encountered in trigonometry and calculus. The Tanh function squashes input values within the range of -1 to 1, making it a useful choice for activation functions in neural networks. Defining the Tanh Function Mathematically The mathematical…

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Category: Deep Learning