Machine Learning

Funtamental Machine Learning principles and concepts that are extended into Deep Neural Networks

Machine Learning

Regularization techniques for training deep neural networks

Discover what is regularization, why it is necessary in deep neural networks and explore the most frequently used strategies: L1, L2, dropout, stohastic depth, early stopping and more

Machine Learning

Explainable AI (XAI): A survey of recents methods, applications and frameworks

What is Explainable Artificial Intelligence (XAI), what are the most popular methods, where and how can it be applied

Machine Learning

A journey into Optimization algorithms for Deep Neural Networks

An overview of the most popular optimization algorithms for training deep neural networks. From stohastic gradient descent to Adam, AdaBelief and second-order optimization

Machine Learning

In-layer normalization techniques for training very deep neural networks

How can we efficiently train very deep neural network architectures? What are the best in-layer normalization options? We gathered all you need about normalization in transformers, recurrent neural nets, convolutional neural networks.

Machine Learning

Explain Neural Arithmetic Logic Units (NALU)

What is behind the NALU Deepmind paper

Machine Learning

Deep Learning- The future or another AI buzzword

How deep learning is changing the world

Software · Convolutional Neural Networks · Machine Learning

Neural Network from scratch-part 2

How to buld a Convolutional neural network library using C++ and OpenCL

Software · Machine Learning

Neural Network from scratch-part 1

How to buld a neural network library using C++ and OpenCL

Natural Language Processing · Unsupervised Learning · Machine Learning

Document clustering

Use unsupervised learning to cluster documents based on their content