Machine Learning Archive

Natural Language Processing

Understanding Bag of Words Models

Explore the fundamentals of Bag of Words (BoW) models in natural language processing. Understand how words are represented using one-hot vectors and sparse vectors, and discover the strengths and limitations of BoW models in handling text data for neural networks and other NLP applications.

Ahmed Zakaria

DEEP LEARNING

How Neural Networks Learn: Understanding BackPropagation

Introduction Imagine a neural network as a relay race where each runner represents a layer of the network. The goal of the relay race is to deliver the baton (the data) from the start to the finish line (the output) as accurately as possible. Feedforward Pass (The Forward Run) 1- Start of the Race (Input […]

Ahmed Zakaria

DEEP LEARNING

Understanding Long Short-Term Memory (LSTM) Networks

LSTMs Long Short-Term Memory is a type of RNNs Recurrent Neural Network that can detain long-term dependencies in sequential data. LSTMs are able to process and analyze sequential data, such as time series, text, and speech. They use a memory cell and gates to control the flow of information, allowing them to selectively retain or discard information as needed and thus avoid the vanishing gradient problem that plagues traditional RNNs.

Nora Yehia

Natural Language Processing

Tokenization: The Cornerstone for NLP Tasks

Uncover the essence of tokenization in NLP with our exploration of tokenization, text processing and the process of building a vocabulary.

Ahmed Zakaria

DEEP LEARNING

Introducing Falcon 180B: The New Frontier in AI Technology

Falcon 180B is an impressive and high-performing LLM, holding the top spot on the Open LLM Leaderboard. However, when considering its implementation, you must account for its resource requirements and associated costs. It is a scaled-up version of Falcon 40B and builds on its innovations such as Multi-Query Attention for improved scalability. Amazon SageMaker facilitated the training […]

Nora Yehia

DEEP LEARNING

What are Recurrent Neural Networks?

Recurrent Neural Networks (RNNs) are a type of neural network in which the results of one phase are used as inputs for the next. Traditional neural networks have inputs and outputs that are independent of one another. RNNs maintain a memory of previous inputs by utilizing a hidden layer. This enables them to capture dependencies between sequential elements, making them effective for tasks involving time-series or sequential data.

Nora Yehia

MACHINE LEARNING

The ultimate guide for sound features and their applications

In this article we will try to go deeper into sound features describing the Mel-Spectrogram and MFCC features and their applications, after previously discussing the Spectrogram features at the guide for Spectrogram features. In this article we’ll cover the following topics: Why do we need features other than the Spectrogram features? Spectrogram features most important […]

Mahmoud Elzeiny

DEEP LEARNING

Speech Command Recognition: The Ultimate Guide

Speech command recognition systems have become integral to modern technology, enabling seamless interaction with devices through spoken commands. From virtual assistants like Siri and Alexa to automotive voice control systems, these systems play a crucial role in enhancing user experience and accessibility

Mahmoud Elzeiny

DEEP LEARNING

Mastering Sound: Frequency Features and Spectrograms

In this article, we delve into the detailed realm of Spectrogram features, a crucial component in sound analysis for machine learning applications. Focusing to shed light on the importance of Spectrogram features. From theoretical foundations to practical applications, our goal is to provide a comprehensive understanding for effectively harnessing their power.

Mahmoud Elzeiny