Generative AI 9: Advanced Customizations and Techniques with Stable Diffusion
In this step, we’ll explore how to fine-tune Stable Diffusion models on custom datasets, enhance the quality of generated images using upscalers, and how to ...
Posts by Category
Artificial Intelligence
Generative AI 8: Diffusion Models and Stable Diffusion
Diffusion models are a class of generative models that create data by learning the reverse process of data corruption. In the case of text-to-image generatio...
Generative AI 7: Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a method that enhances generative models by incorporating retrieval mechanisms. It allows a model to retrieve relevan...
Generative AI 6: Advanced GAN Techniques
In this step, we will explore advanced variants of GANs that address some limitations of the basic GAN architecture, such as unstable training and lack of co...
Generative AI 5: Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs) are a type of generative model that consists of two neural networks: a generator and a discriminator. These two networ...
Generative AI 4: Variational Autoencoders (VAEs)
Variational Autoencoders (VAEs) are a type of generative model that learns a probability distribution over the latent space of the data and can generate new,...
Generative AI 3: Transformer Networks and Large Language Models (LLMs)
Transformers are the foundation of modern Large Language Models (LLMs) such as GPT-3, BERT, and T5. In this step, we will explore the transformer architectur...
Simple Neural Network in Python with Math behind it
Neural networks are one of the most powerful tools in machine learning, capable of recognizing patterns, classifying images, and even generating text. But wh...
Generative AI 2: Neural Networks
A neural network is a computational model inspired by the way biological neural networks work. It is composed of layers of neurons (nodes), which are connect...
Generative AI 1.4: Optimization Techniques
Optimization refers to the process of finding the best parameters for a model to minimize (or maximize) some objective function, typically the loss function ...
Generative AI 1.3: Probability and Statistics
In machine learning and generative AI, probability is used to model uncertainty and randomness, while statistics helps us make inferences about data. These c...
Generative AI 1.2: Calculus
Calculus plays a fundamental role in machine learning, especially in training neural networks. During training, the network adjusts its parameters (weights a...
Generative AI 1.1: Linear Algebra
Linear algebra is the foundation of machine learning and neural networks. In particular, vectors and matrices are used to represent and manipulate data, weig...
Flutter
Creating a Portfolio in Flutter
In this blog, we will learn to create flutter widgets for our portfolio. The portfolio consists of two pages - PortfolioPage and ProjectPage. We will learn t...
Unity
Arrange GameObjects randomly in Unity
In this blog, we will learn how to randomly arrange objects within the screen boundaries in Unity game engine. We will take Brick Breaker game as an example ...
Mathematics
Crash Course: Calculus
Calculus is a branch of mathematics focused on change (differentiation) and accumulation (integration). It is divided into differential calculus and integral...