Deep Learning Tutorials | Ai Online Course

Introduction to Deep Learning

Have you ever imagined a car driving without a human? A machine will translate any language for you. A machine will diagnose your...

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Data Preprocessing for Deep Learning

Data processing is a crucial part of deep learning. We can’t feed the training data into the model without data preprocessin...

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Convolutional Neural Networks

A convolutional neural network(CNN) is a type of deep learning algorithm that has capable to learn the hierarchical features from...

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Recurrent Neural Networks

In traditional neural network systems, inputs and outputs are independent of each other. For some situations, previous outputs are...

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Long Short Term Memory(LSTM) Networks

Long Short-Term Memory, also referred to as LSTM in artificial neural networks, is a potent and sophisticated architecture created...

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Transformers

Simply put, transformers are a type of neural network that operates by learning context in sequential data. It uses the concept...

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Generative Adversarial Networks

Generating new data that has no exists in the world is so fascinating. With the help of deep learning, we can do it easily today b...

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Autoencoder

An autoencoder is a type of Unsupervised Learning in Deep Learning that is designed to learn the compact, lower-dimensional repres...

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Variational Autoencoders

Autoencoders are an unsupervised learning technique that can learn the efficient representation of input data and are used for var...

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Diffusion Architecture

Over recent years Generative Adversarial Networks(GANs), Variational Autoencoders(VAEs), and the family of flow-based generative n...

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Reinforcement Learning in Deep Learning

Reinforcement Learning (RL) is a machine learning type concerned with how agents learn to interact with an environment to maximize...

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Optimization Algorithms for Deep Learning

Optimization is a crucial part of deep learning that is used to update the parameters(weights and bias) of a neural network mode...

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Regularization Techniques

kThe performance of a deep learning model can be divided into three parts (Underfitting, Appropriate fitting, and Overfitting). Un...

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Model Tracking and Accuracy Analysis

Without performance analysis, anyone can’t grow his activities. In deep learning, model tracking, and accuracy analysis are...

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Hyperparameter Tuning Techniques

Hyperparameter is a type of parameter that is external to the model and controls the learning of the model. The value of the hyper...

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Transfer Learning

Put simply, transfer learning is the process where a model developed for a specific purpose is reused as the starting point for an...

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Deployment of Deep Learning Models with REST API

The Machine learning model deployment process involves making a model trained on a dataset accessible in systems or applications u...

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Deep Learning on Cloud Platforms

Deep learning uses neural networks to perform various cognitive and complex tasks. Compared to machine learning algorithms, it imp...

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Mathematical Foundations for Deep Learning

Learning Math is a requirement for mastering deep learning. This is due to the fact that deep learning relies heavily on mathemati...

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