Text to speech (TTS) is an AI task that involves audio generation from text. TTS is a technology that converts text into spoken audio using natural sounding voices. TTS can read aloud any text data, such as PDFs, websites, books, emails, etc., and provide an auditory format for accessing written content. TTS can be helpful for anyone who needs to listen to text data for various reasons, such as accessibility, convenience, multitasking, learning, entertainment, etc. TTS uses different techniques and models to generate speech from text data, such as:

Concatenative synthesis: Combining pre-recorded segments of human speech based on the phonetic units of the text.

Parametric synthesis: Generating speech signals from acoustic parameters derived from the text using statistical models.

Neural synthesis: Using deep neural networks to learn the mapping between text and speech features and produce high-quality speech signals.

Expressive synthesis: Adding emotions or styles to the speech output to make it more natural and engaging.Reference::Text-to-Speech AI: Lifelike Speech Synthesis | Google Cloud,Text-to-speech synthesis - Wikipedia

Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that is particularly well-suited for image recognition and processing tasks. They are made up of multiple layers, including convolutional layers, pooling layers, and fully connected layers. The convolutional layer is the core building block of a CNN, and it is where the majority of computation occurs. It requires a few components, which are input data, a filter, and a feature map. The filter is a small matrix of weights that slides over the input data and performs element-wise multiplication and summation, resulting in a feature map that represents the activation of a certain feature in the input. By applying multiple filters, the CNN can detect different patterns in the image, such as edges, shapes, colors, textures, etc. The pooling layer is used to reduce the spatial dimensionality of the feature maps, while preserving the most important information. The fully connected layer is the final layer of a CNN, and it is where the classification or regression task is performed based on the extracted features. CNNs can learn to detect complex patterns in images by adjusting their weights during training using backpropagation and gradient descent algorithms.Reference::Convolutional neural network - Wikipedia,What are Convolutional Neural Networks? | IBM,Convolutional Neural Network (CNN) in Machine Learning

Unsupervised learning is a type of machine learning that is used to understand relationships within data and is not focused on making predictions or classifications. Unsupervised learning algorithms work with unlabeled data, which means the data does not have predefined categories or outcomes. The goal of unsupervised learning is to discover hidden patterns, structures, or features in the data that can provide valuable insights or reduce complexity. Some of the common techniques and applications of unsupervised learning are:

Clustering: Grouping similar data points together based on their attributes or distances. For example, clustering can be used to segment customers based on their preferences, behavior, or demographics.

Dimensionality reduction: Reducing the number of variables or features in a dataset while preserving the essential information. For example, dimensionality reduction can be used to compress images, remove noise, or visualize high-dimensional data in lower dimensions.

Anomaly detection: Identifying outliers or abnormal data points that deviate from the normal distribution or behavior of the data. For example, anomaly detection can be used to detect fraud, network intrusion, or system failure.

Association rule mining: Finding rules that describe how variables or items are related or co-occur in a dataset. For example, association rule mining can be used to discover frequent itemsets in market basket analysis or recommend products based on purchase history.Reference::Unsupervised learning - Wikipedia,What is Unsupervised Learning? | IBM

Fine-tuning is the process of updating the model parameters on a new task and dataset, using a pre-trained large language model as the starting point. Fine-tuning allows the model to adapt to the specific context and domain of the new task, and improve its performance and accuracy.Fine-tuning can be used to customize the model's capabilities for specific tasks such as text classification, named entity recognition, and machine translation82. Fine-tuning is also known as transfer learning or task-based learning.Reference:A Complete Guide to Fine Tuning Large Language Models,Finetuning Large Language Models - DeepLearning.AI

A graphics processing unit (GPU) is a specialized hardware component that can perform parallel computations on large amounts of data. GPUs are widely used in deep learning to accelerate the training of deep neural networks, as they can execute many matrix operations and tensor operations simultaneously.GPUs can significantly reduce the training time and improve the performance of deep learning models compared to using CPUs alone678.Reference:Hardware Recommendations for Machine Learning / AI,New hardware offers faster computation for artificial intelligence ...,The Best Hardware for Machine Learning - ReHack,Hardware for Deep Learning Inference: How to Choose the Best One for ...