Advanced (yet simple) prompt engineering methods that are proven to work well in various domains. Continue reading Advanced (yet simple) Prompt Engineering techniques for Large Language Models
ChatGPT is not the only AI that can converse naturally with humans. In this article, we introduce alternatives for ChatGPT with their unique advantages. Continue reading Conversational AI: ChatGPT alternatives and their advantages
A straightforward technique that significantly enhances ChatGPT’s hint generation capabilities, placing it on par with expert human tutors for programming education. Continue reading Yes, we can now make ChatGPT as good as human tutors for programming education
Applying Machine Learning and Deep Learning to solve the Knowledge Tracing problem in the context of Programming classrooms. Continue reading Case study: Machine Learning and Deep Learning for Knowledge Tracing in Programming Education
Let us discuss the state-of-the-art methods for transforming every kind of input data into fixed-length vectors of continuous values, including Word2Vec, Doc2Vec, Image2Vec, Node2Vec, Edge2Vec, Code2Vec, and Data2Vec. Continue reading Transforming everything to vectors with Deep Learning: from Word2Vec, Node2Vec, to Code2Vec and Data2Vec
Let us review a list of pretrained language models, including BERT, Transformer-XL, XLNet, RoBERTa, DistilBERT, ALBERT, BART, ELECTRA, ConvBERT, DeBERTa, and BigBird. Continue reading A review of pre-trained language models: from BERT, RoBERTa, to ELECTRA, DeBERTa, BigBird, and more
Q Learning, Deep Q Learning, Double Q Learning, and Dueling Q Learning explained. TensorFlow code provided. Continue reading Reinforcement learning: Q Learning, Deep Q Learning introduction with Tensorflow
A critical view of Deep Learning. Continue reading Some thoughts regarding Deep Learning’s achievements, hype, and challenges
The Transformer neural networks, explained in details. Continue reading The Transformer neural network architecture
Introduction to and comparison of Batch Norm, Weight Norm, Layer Norm, Instance Norm, and Group Norm. Continue reading Deep Learning normalization methods
Get your first intuition with Attention right with a minimal code-example. Continue reading Attention in Deep Learning, your starting point (with code)
Where and how you should start with LSTM? Continue reading LSTM: where to start?
Through various experiments, ELU is accepted by many researchers as a good successor of the original version (ReLU). Continue reading ELU activation: A comprehensive analysis
Batch Normalization (BatchNorm) is a very frequently used technique in Deep Learning, however, the reason why it works is often interpreted ambiguously. Continue reading Batch Normalization and why it works
Despise its simplicity, ReLU previously achieved the top performance over various tasks of modern Machine Learning. Continue reading Rectifier Linear Unit (ReLU)
The sigmoid and tanh activation functions were very frequently used in the past but have been losing popularity in the era of Deep learning. Continue reading Sigmoid, tanh activations and their loss of popularity