Anomaly Detection with Autoencoders in TensorFlow 2.0

In this detailed guide, I will explain how Deep Learning can be used in the field of Anomaly Detection and how to implement an Anomaly Detection Model in TensorFlow 2.0

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Predictive Behaviour Modeling with Neural Networks

In this tutorial, I want to show you how deep neural networks can be used to predict the future behavior of people. This is usually referred to as Predictive Behaviour Modeling.

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Data Preprocessing Steps for Deep Learning in Python

In this detailed guide, I will present to you the essential steps of Data Preprocessing in the field of Deep Learning and Data Science and how to implement them in Python.

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Deep AutoEncoder in TensorFlow 2.0

In this article, I will present to you a famous neural network architecture knows as a Deep Autoencoder and how to implement it in TensorFlow 2.0

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Loss Functions in Deep Learning

This in-depth article addresses the questions of why we need loss functions in deep learning and what loss functions should be used for which tasks.

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An Introduction to Stochastic-, Batch-, and Minibatch Gradient Descent

This is a detailed guide that should answer the questions of why and when we need Stochastic, Batch, and Mini Batch Gradient Descent when implementing Deep Neural Networks..

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Activation Functions in Neural Networks

In this detailed guide, I will explain everything there is to know about activation functions in deep learning. Especially what activation functions are and why we must use them when implementing neural networks.

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Artificial Intelligence Market Size

Artificial Intelligence is on the rise. The pace of growth for artificial intelligence within the consumer, enterprise, government, and defense sectors continues. In this article, we will analyze the current size of the AI market and make forecasts for the future.

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Optimization Algorithms in Deep Learning

In this article, I will present you the most sophisticated optimization algorithms in Deep Learning that allow neural networks to learn faster and achieve better performance. These algorithms are: Stochastic Gradient Descent with Momentum, AdaGrad, RMSProp, and Adam Optimizer.

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Regularization in Deep Learning - L1, L2, and Dropout

In this article, we will address the most popular regularization techniques which are called L1, L2, and dropout.

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Batch Normalization in Deep Learning

I this article I will present you the theory and practical implementation of a very useful and effective technique called Batch Normalization, which can significantly accelerate the training of a neural network model.

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Evaluation Metrics in Data Science and Machine Learning

In this article, we are going to discuss the most popular performance evaluation metrics in all Data Science, Machine Learning and Deep Learning..

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Data Science Project Lifecycle

Ever wondered how a Data Science / Deep Learning project looks like in an industrial environment? In this article, I will present you the typical lifecycle of such a project.

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Overfitting and Underfitting in Deep Learning

In this article, we will address the phenomenon of overfitting and underfitting in deep learning.

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What is Deep Learning?

This is a beginner's guide to Deep Learning and Neural networks. In the following article, we are going to discuss the meaning of Deep Learning and Neural Networks. In particular, we will focus on how Deep Learning works in practice.

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Machine Learning vs. Deep Learning vs. Artificial Intelligence

In this article we are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning. Furthermore we will adress the question why Deep Learning as a young emerging field is far supirior to tradional Machine Learning.

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