GPT (Generative Pre-trained Transformer) – It is a family of language models developed by OpenAI that can be used for various natural language processing (NLP) tasks such as language translation, summarization, and more.

CycleGAN – It is a generative model that can be used for image translation tasks without paired training data. It is based on the GAN architecture and can learn the mapping between two domains.

StyleGAN – It is a generative model developed by NVIDIA that can be used to generate realistic images. It is based on the GAN architecture and can be trained on large datasets.

Pix2Pix – It is another generative model based on the GAN architecture that can be used for image-to-image translation tasks such as colorization, sketch-to-image, and more.

BERT (Bidirectional Encoder Representations from Transformers) – It is another language model developed by Google that can be used for various NLP tasks. It is pre-trained on a large corpus of text and can be fine-tuned for specific tasks.

R – With built-in statistical functions, R was made specifically for machine learning applications. This language also has an extensive library of data visualization tools, which makes it easy to create charts, graphs, and other visualizations of data. Moreover, R has an interactive console that allows users to explore data and experiment with different analyses and models in real time, enabling Data Scientists to test different models and approaches. R - as well as Python - can be easily integrated with other tools and languages, such as Python, SQL, and Hadoop.

Python – Python is considered the most popular programming language in the Data Science area mostly because of its quite straightforward and easy-to-read syntax. Still, the benefits of using it in building Machine Learning solutions are numerous. This language has a large and active community that develops and maintains a wide range of libraries and frameworks specifically for Machine Learning and Artificial Intelligence, which provide pre-built algorithms and tools for building and training models. Python is a versatile and flexible language; it can be used in scientific computing and web development, which makes it a great choice for building ML models, often requiring a mix of programming, data analysis, and visualization.

Keras – Keras is a neural network Application Programming Interface (API) for Python tightly integrated with TensorFlow. It is used to build machine learning models.

TensorFlow – TensorFlow is an open-source machine learning library developed by Google Brain Team, used to build and train machine learning models, including deep neural networks. It provides high-level APIs such as Keras that make it easy to build and train models without needing to write complex code, visualization tools, and pre-built models and datasets.

PyTorch – PyTorch is also an open-source machine learning library, yet it is was developed by Facebook's AI Research team. PyTorch allows Data Scientists to prototype and deploys machine learning models using Python. It is used in various applications such as computer vision, natural language processing, and reinforcement learning.