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‎.ipynb_checkpoints/06-checkpoint.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "d2b493b7-ef5e-4612-b094-52d84aac0ff3",
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"metadata": {},
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"source": [
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"## What is a machine learning research paper?\n",
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"\n",
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"A machine learning research paper is a scientific paper that details findings of a research group on a specific area.\n",
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"\n",
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"The contents of a machine learning research paper can vary from paper to paper but they generally follow the structure:\n",
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"\n",
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"| **Section** | **Contents** |\n",
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"| ----- | ----- | \n",
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"| **Abstract** | An overview/summary of the paper's main findings/contributions. |\n",
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"| **Introduction** | What's the paper's main problem and details of previous methods used to try and solve it. |\n",
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"| **Method** | How did the researchers go about conducting their research? For example, what model(s), data sources, training setups were used? |\n",
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"| **Results** | What are the outcomes of the paper? If a new type of model or training setup was used, how did the results of findings compare to previous works? (this is where **experiment tracking** comes in handy) |\n",
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"| **Conclusion** | What are the limitations of the suggested methods? What are some next steps for the research community? |\n",
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"| **References** | What resources/other papers did the researchers look at to build their own body of work? |\n",
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"| **Appendix** | Are there any extra resources/findings to look at that weren't included in any of the above sections? |"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d8bc9bda-c7e2-407d-99d5-ad387f49b625",
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"id": "36b201f6-5ca0-4a27-80ba-52002d17fd8d",
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"metadata": {},
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"outputs": [],
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"source": []

‎.ipynb_checkpoints/07-checkpoint.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "d2b493b7-ef5e-4612-b094-52d84aac0ff3",
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"metadata": {},
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"source": [
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"## What is a machine learning research paper?\n",
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"\n",
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"A machine learning research paper is a scientific paper that details findings of a research group on a specific area.\n",
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"\n",
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"The contents of a machine learning research paper can vary from paper to paper but they generally follow the structure:\n",
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"\n",
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"| **Section** | **Contents** |\n",
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"| ----- | ----- | \n",
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"| **Abstract** | An overview/summary of the paper's main findings/contributions. |\n",
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"| **Introduction** | What's the paper's main problem and details of previous methods used to try and solve it. |\n",
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"| **Method** | How did the researchers go about conducting their research? For example, what model(s), data sources, training setups were used? |\n",
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"| **Results** | What are the outcomes of the paper? If a new type of model or training setup was used, how did the results of findings compare to previous works? (this is where **experiment tracking** comes in handy) |\n",
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"| **Conclusion** | What are the limitations of the suggested methods? What are some next steps for the research community? |\n",
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"| **References** | What resources/other papers did the researchers look at to build their own body of work? |\n",
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"| **Appendix** | Are there any extra resources/findings to look at that weren't included in any of the above sections? |"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d8bc9bda-c7e2-407d-99d5-ad387f49b625",
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"metadata": {},
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"outputs": [],
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎06.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "d2b493b7-ef5e-4612-b094-52d84aac0ff3",
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"metadata": {},
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"source": [
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"## What is a machine learning research paper?\n",
9-
"\n",
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"A machine learning research paper is a scientific paper that details findings of a research group on a specific area.\n",
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"\n",
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"The contents of a machine learning research paper can vary from paper to paper but they generally follow the structure:\n",
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"\n",
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"| **Section** | **Contents** |\n",
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"| ----- | ----- | \n",
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"| **Abstract** | An overview/summary of the paper's main findings/contributions. |\n",
17-
"| **Introduction** | What's the paper's main problem and details of previous methods used to try and solve it. |\n",
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"| **Method** | How did the researchers go about conducting their research? For example, what model(s), data sources, training setups were used? |\n",
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"| **Results** | What are the outcomes of the paper? If a new type of model or training setup was used, how did the results of findings compare to previous works? (this is where **experiment tracking** comes in handy) |\n",
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"| **Conclusion** | What are the limitations of the suggested methods? What are some next steps for the research community? |\n",
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"| **References** | What resources/other papers did the researchers look at to build their own body of work? |\n",
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"| **Appendix** | Are there any extra resources/findings to look at that weren't included in any of the above sections? |"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d8bc9bda-c7e2-407d-99d5-ad387f49b625",
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"id": "36b201f6-5ca0-4a27-80ba-52002d17fd8d",
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"metadata": {},
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"outputs": [],
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"source": []

‎07.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "d2b493b7-ef5e-4612-b094-52d84aac0ff3",
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"metadata": {},
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"source": [
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"## What is a machine learning research paper?\n",
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"\n",
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"A machine learning research paper is a scientific paper that details findings of a research group on a specific area.\n",
11+
"\n",
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"The contents of a machine learning research paper can vary from paper to paper but they generally follow the structure:\n",
13+
"\n",
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"| **Section** | **Contents** |\n",
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"| ----- | ----- | \n",
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"| **Abstract** | An overview/summary of the paper's main findings/contributions. |\n",
17+
"| **Introduction** | What's the paper's main problem and details of previous methods used to try and solve it. |\n",
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"| **Method** | How did the researchers go about conducting their research? For example, what model(s), data sources, training setups were used? |\n",
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"| **Results** | What are the outcomes of the paper? If a new type of model or training setup was used, how did the results of findings compare to previous works? (this is where **experiment tracking** comes in handy) |\n",
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"| **Conclusion** | What are the limitations of the suggested methods? What are some next steps for the research community? |\n",
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"| **References** | What resources/other papers did the researchers look at to build their own body of work? |\n",
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"| **Appendix** | Are there any extra resources/findings to look at that weren't included in any of the above sections? |"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d8bc9bda-c7e2-407d-99d5-ad387f49b625",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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}

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