Tag: research

Reinforcement learning is a relatively lesser-known area of artificial intelligence (AI) compared to highly popular subfields today, such as machine learning, deep learning, and natural language processing.

This post is divided into five parts; they are: • From a Full Transformer to a Decoder-Only Model • Building a Decoder-Only Model • Data Preparation for Self-Supervised Learning • Training the Model • Extensions The transformer model originated as a sequence-to-sequence (seq2seq) model that converts an input sequence into a context vector, which is …

Read more “Building a Decoder-Only Transformer Model for Text Generation”

This post is divided into six parts; they are: • Why Transformer is Better than Seq2Seq • Data Preparation and Tokenization • Design of a Transformer Model • Building the Transformer Model • Causal Mask and Padding Mask • Training and Evaluation Traditional seq2seq models with recurrent neural networks have two main limitations: • Sequential …

Read more “Building a Transformer Model for Language Translation”

In regression models , failure occurs when the model produces inaccurate predictions — that is, when error metrics like MAE or RMSE are high — or when the model, once deployed, fails to generalize well to new data that differs from the examples it was trained or tested on.

In this article, you will learn: • Why standard scaling methods are sometimes insufficient and when to use advanced techniques.

Deploying machine learning models can seem complex, but modern tools can streamline the process.

This post is divided into four parts; they are: • Why Attnetion Matters: Limitations of Basic Seq2Seq Models • Implementing Seq2Seq Model with Attention • Training and Evaluating the Model • Using the Model Traditional seq2seq models use an encoder-decoder architecture where the encoder compresses the input sequence into a single context vector, which the …

Read more “Building a Seq2Seq Model with Attention for Language Translation”

If you’ve worked with data in Python, chances are you’ve used Pandas many times.

In this article, you will learn: • the purpose and benefits of image augmentation techniques in computer vision for improving model generalization and diversity.

Machine learning projects can be as exciting as they are challenging.