Write a Paper in EasyLaTeX: From a New Project to a PDF
Use this short gradient-descent writing example to learn how EasyLaTeX combines a title, abstract, sections, equations, tables, figures, and references. It demonstrates document structure; your actual paper still needs your research and submission template.
1. Create a Project and Select the Main File
Sign in to the EasyLaTeX editor, create an empty project, and open or create main.tex. Select it in the top main-file dropdown. In Editor Settings, choose pdfLaTeX. Compiler selection is stored per user in the project, so collaborators should each check their setting.
The file tree manages resources, the middle area edits source, and the output panel shows the PDF:
2. Set Up the Preamble
Put this at the beginning of the file. It is a preamble, not yet a complete document:
\documentclass[a4paper,11pt]{article}
\usepackage{amsmath,amssymb}
\usepackage{graphicx}
\usepackage{booktabs}
\usepackage[a4paper,margin=1in]{geometry}
\usepackage[numbers]{natbib}
\usepackage{hyperref}
\title{Gradient Descent: A Writing Example}
\author{Your Name}
\date{\today}
These English examples use article. For a publisher or university template, retain its class and requirements rather than replacing its setup wholesale. The example uses A4; follow the required paper size for your submission.
3. Add the Title, Abstract, and Sections
After the preamble, write \begin{document} once, followed by \maketitle and the abstract. Keep one matching \end{document} at the very end.
\begin{abstract}
This document demonstrates the structure of a short paper on gradient descent.
It includes notation, an update equation, an illustrative comparison table,
an optional figure, and references.
\end{abstract}
Add the outline within that same document body:
\section{Introduction}
\section{Mathematical Fundamentals}
\subsection{Gradient and Update Rule}
\section{Gradient Descent Variants}
\section{Learning Rate}
\section{Conclusion}
The class manages headings and numbering. Insert the following fragments inside the body; do not add more document classes or document boundaries. A merged example appears at the end.
4. Add Equations
Use \(...\) for inline math and \[...\] for an unnumbered display. For numbering and references, use equation, \label, and \eqref:
\begin{equation}
\theta_{t+1}=\theta_t-\eta\nabla J(\theta_t)
\label{eq:update}
\end{equation}
Equation~\eqref{eq:update} defines the update used here.
Use align for multiple aligned lines, with & at alignment points and \\ between rows:
\begin{align}
J(\theta_0,\theta_1)
&=\frac{1}{2m}\sum_{i=1}^{m}(h_{\theta}(x_i)-y_i)^2,\\
\frac{\partial J}{\partial\theta_0}
&=\frac{1}{m}\sum_{i=1}^{m}(h_{\theta}(x_i)-y_i).
\end{align}
5. Add a Table
table supplies the float and caption; tabular arranges cells. This is a conceptual comparison, not experimental measurements:
\begin{table}[tbp]
\centering
\caption{Illustrative comparison of gradient estimates}
\label{tab:variants}
\begin{tabular}{lll}
\toprule
Variant & Samples per update & Typical sampling fluctuation \\
\midrule
Batch & Full dataset & No mini-batch sampling \\
Stochastic & One example & Higher \\
Mini-batch & A selected batch & Between the two \\
\bottomrule
\end{tabular}
\end{table}
Separate cells with &. Place the label after the caption and refer to it with \ref{tab:variants}.
6. Upload and Reference an Image
Upload loss-curve.png through the file tree. This example displays a placeholder if it is absent and uses the image once it is available:
\begin{figure}[tbp]
\centering
\IfFileExists{loss-curve.png}{%
\includegraphics[width=0.7\linewidth]{loss-curve.png}%
}{%
\fbox{\rule{0pt}{30mm}\rule{0.65\linewidth}{0pt}}%
}
\caption{Loss-curve illustration; replace with your own figure}
\label{fig:loss}
\end{figure}
graphicx supplies image insertion. Relative widths follow the available line width. Match paths and capitalization, and replace the placeholder with your actual research figure.
7. Add References
For a short list, use thebibliography and cite with \cite{goodfellow2016}. This does not require .bib or an external bibliography processor, although cross-references can still need repeat LaTeX passes managed by the platform:
\begin{thebibliography}{9}
\bibitem{goodfellow2016} Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
\emph{Deep Learning}. MIT Press, 2016.
\bibitem{bottou2012} Leon Bottou.
Stochastic Gradient Descent Tricks. In \emph{Neural Networks: Tricks of the Trade}, 2012.
\bibitem{kingma2015} Diederik P. Kingma and Jimmy Ba.
Adam: A Method for Stochastic Optimization. ICLR, 2015.
\end{thebibliography}
For reusable databases and style changes, see BibTeX and biblatex.
8. Compile, Check, and Download
The compile action saves the current edit first. Wait for the current job, then inspect equations, figures, tables, references, and layout. Open Bug for errors and the Original Log; use AI Fix when appropriate. See compilation troubleshooting.
The toolbar download menu offers Download PDF file for reading and submission, and Download LaTeX project for source and resources. Download the final PDF after successful compilation:
Complete main.tex
Use this standalone structure example in a test project. A missing image produces a placeholder. Replace the main file with it rather than appending it after an existing \end{document}:
\documentclass[a4paper,11pt]{article}
\usepackage{amsmath,amssymb}
\usepackage{graphicx}
\usepackage{booktabs}
\usepackage[a4paper,margin=1in]{geometry}
\usepackage[numbers]{natbib}
\usepackage{hyperref}
\title{Gradient Descent: A Writing Example}
\author{Your Name}
\date{\today}
\begin{document}
\maketitle
\begin{abstract}
This document demonstrates the structure of a short paper on gradient descent.
It includes notation, an update equation, an illustrative comparison table,
an optional figure, and references.
\end{abstract}
\section{Introduction}
Background is discussed in~\cite{goodfellow2016,bottou2012,kingma2015}.
\section{Mathematical Fundamentals}
Let $J$ be a differentiable objective and $\eta>0$ a step size.
\begin{equation}
\theta_{t+1}=\theta_t-\eta\nabla J(\theta_t)
\label{eq:update}
\end{equation}
Equation~\eqref{eq:update} defines the update used here.
\begin{align}
J(\theta_0,\theta_1)
&=\frac{1}{2m}\sum_{i=1}^{m}(h_{\theta}(x_i)-y_i)^2,\\
\frac{\partial J}{\partial\theta_0}
&=\frac{1}{m}\sum_{i=1}^{m}(h_{\theta}(x_i)-y_i).
\end{align}
\section{Gradient Descent Variants}
\begin{table}[tbp]
\centering
\caption{Illustrative comparison of gradient estimates}
\label{tab:variants}
\begin{tabular}{lll}
\toprule
Variant & Samples per update & Typical sampling fluctuation \\
\midrule
Batch & Full dataset & No mini-batch sampling \\
Stochastic & One example & Higher \\
Mini-batch & A selected batch & Between the two \\
\bottomrule
\end{tabular}
\end{table}
Table~\ref{tab:variants} is a qualitative illustration, not measured performance.
\section{Learning Rate}
Upload your own loss-curve image to replace the placeholder in Figure~\ref{fig:loss}.
\begin{figure}[tbp]
\centering
\IfFileExists{loss-curve.png}{%
\includegraphics[width=0.7\linewidth]{loss-curve.png}%
}{%
\fbox{\rule{0pt}{30mm}\rule{0.65\linewidth}{0pt}}%
}
\caption{Loss-curve illustration; replace with your own figure}
\label{fig:loss}
\end{figure}
\section{Conclusion}
Check the assumptions, results, and references for your actual paper.
\begin{thebibliography}{9}
\bibitem{goodfellow2016} Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
\emph{Deep Learning}. MIT Press, 2016.
\bibitem{bottou2012} Leon Bottou.
Stochastic Gradient Descent Tricks. In \emph{Neural Networks: Tricks of the Trade}, 2012.
\bibitem{kingma2015} Diederik P. Kingma and Jimmy Ba.
Adam: A Method for Stochastic Optimization. ICLR, 2015.
\end{thebibliography}
\end{document}
Replace the demonstration with your own argument, data, and figures, then follow the required template.