A mathematical guide on the theory behind Deep Reinforcement Learning

Self Learning AI-Agents Series — Table of Content

Fig. 1. AI agent learned how to run and overcome obstacles.

Markov Decision Processes — Table of Content


Not sure if good model… or just overfitting?

Source: Authors own image.

Table of Content

  1. Generalization in Deep Learning
  2. Overfitting
  3. Underfitting
  4. Variance Bias Tradeoff
  5. Identifying Overfitting and Underfitting during Training
  6. How to avoid Overfitting?
  7. How…


A Guide on the Concept of Loss Functions in Deep Learning — What they are, Why we need them…

Source: https://unsplash.com/photos/yNvVnPcurD8
  1. Why do we need Loss Functions in Deep Learning?
  2. Mean Squared Error Loss Function
  3. Cross-Entropy Loss Function
  4. Mean Absolute Percentage Error
  5. Take-Home-Message

1. Why do we need Loss Functions in Deep Learning?


Getting Started

A Guide on the Theory of Activation Functions in Neural Networks and why we need them in the first place.

Source: https://unsplash.com/photos/dQejX2ucPBs

Table of Content

  1. Recap: Forward Propagation
  2. Neural Network is a Function
  3. Why do we need Activation Functions?
  4. Different Kinds of…


Analysis of the Technology of our Future — Trends, Projections, Opportunities

1. Artificial Intelligence in the corporate Sector


Why do we need Stochastic, Batch, and Mini Batch Gradient Descent when implementing Deep Neural Networks?

Table of Content

  1. 1. Introduction: Let’s recap Gradient Descent
  2. 2. Common Problems when Training Neural Networks (local minima, saddle points, noisy gradients)
  3. 3. Batch-Gradient Descent


A Guide on the Theory and Practicality of the most important Regularization Techniques in Deep Learning

https://www.spacetelescope.org/images/heic0611b/

Table of Content

  1. Recap: Overfitting
  2. What is Regularization?
  3. L2 Regularization
  4. L1 Regularization
  5. Why do L1 and L2 Regularizations work?
  6. Dropout
  7. Take-Home-Message

1. Recap: Overfitting


A Guide on how to implement Neural Networks in TensorFlow 2.0 to detect anomalies.

Table of Content

  1. Introduction
  2. Anomaly Detection
  3. Uses Cases for Anomaly Detection Systems
  4. Anomaly Case Study: Financial Fraud
  5. How does an Autoencoder work?
  6. Anomaly Detection with AutoEncoder
  7. Fraud Detection in TensorFlow 2.0

1. Introduction


Learn the most important Basics of Deep Learning and Neural Networks in this detailed Tutorial.

If you liked the article and want to share your thoughts, ask questions or stay in touch feel free to connect with me via LinkedIn.

Table of Content

  1. What exactly is Deep Learning?
  2. Why is Deep Learning so popular these Days?
  3. Biological Neural Networks
  4. Artificial Neural Networks
  5. Neural Network Architecture
  6. Layer Connections
  7. Learning Process in a Neural Network
  8. Loss Functions
  9. Gradient Descent


Learn the Difference between the most popular Buzzwords in today's tech. World — AI, Machine Learning and Deep Learning

Evolution of Artificial Intelligence

Artem Oppermann

Deep Learning & AI Software Developer | MSc. Physics | Educator|

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