PyTorch Basics for Machine Learning from edX

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This course is the first part in a two part course and will teach you the fundamentals of Pytorch while providing the necessary prerequisites you need before you build deep learning models.

We will start off with PyTorch's tensors in one dimension and two dimensions , you will learn the tensor types an operations, PyTorchs Automatic Differentiation package and integration with Pandas and Numpy. This is followed by an in-depth overview of the dataset object and transformations; this is the first step in building Pipelines in PyTorch.

In module two we will learn how to train a linear regression model. You will review the fundamentals of training your model including concepts such as loss, cost and gradient descent. You will learn the fundamentals of PyTorch including how to make a prediction using PyTorch's linear class and custom modules. Then determine loss and cost with PyTorch. Finally you will implement gradient descent via first principles.

In module three you will train a linear regression model via PyTorch's build in functionality, developing an understanding of the key components of PyTorch. This will include how to effectively train PyTorch's custom modules using the optimizer object, allowing you an effective way to train any model. We will introduce the data loader allowing you more flexibility when working with massive datasets . You will learn to save your model and training in applications such as cross validation for hyperparameter selection, early stopping and checkpoints.

In module three you will learn how to extend your model to multiple input and output dimensions in applications such as multiple linear regression and multiple output linear regression. You will learn the fundamentals of the linear object, including how it interacts with data with different dimensions and number of samples. Finally you will learn how to train these models in PyTorch.

In module four you will review linear classifiers, logistic regression and the issue of using different loss functions. You will learn how to implement logistic regression in PyTorch several ways, including using custom modules and using the sequential method. You will test your skills in a final project.

What's inside

Learning objectives

Build a machine learning pipeline in pytorch
Train models in pytorch.
Load large datasets

Train machine learning applications with pytorch
Have the prerequisite knowledge to apply to deep learning andhow to incorporate and python libraries such as numpy and pandas with pytorch

Build a machine learning pipeline in pytorch
Train models in pytorch.
Load large datasets
Train machine learning applications with pytorch
Have the prerequisite knowledge to apply to deep learning andhow to incorporate and python libraries such as numpy and pandas with pytorch

Syllabus

Module 1

Tensors 1D

Two-Dimensional Tensors

Derivatives In PyTorch

Dataset

Module 2

Prediction Linear Regression

Training Linear Regression

Loss

Gradient Descent

Cost

Training PyTorch

Module 3

Mini-Batch Gradient Descent

Optimization in PyTorch

Training and Validation

Early stopping

Module 4

Multiple Linear Regression Prediction

Multiple Linear Regression Training

Linear regression multiple outputs

Multiple Output Linear Regression Training

Module 5

Linear Classifier and Logistic Regression

Logistic Regression Prediction

Bernoulli Distribution Maximum Likelihood Estimation

Logistic Regression Cross Entropy

Final Assignment

Final Project

Final Exam

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops a foundational skill in PyTorch, which is standard in data science

Taught by Joseph Santarcangelo, who are recognized for their work in data science

Covers essential concepts of machine learning pipelines, model training, and data loading in PyTorch

Includes several hands-on exercises that allow learners to develop skills at their own pace

Explores the integration of Python libraries such as Numpy and Pandas with PyTorch

Requires learners to have some prior knowledge of Python and basic data science concepts

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in PyTorch Basics for Machine Learning with these activities:

Review fundamental concepts of probability and statistics

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Strengthens your foundation in probability and statistics, which are essential for understanding the theoretical underpinnings of machine learning models.

Browse courses on Probability

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Review key concepts such as probability distributions, random variables, and statistical inference.
Solve practice problems to test your understanding.

Dive deeper into PyTorch's automatic differentiation

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Familiarizes you with PyTorch's powerful differentiation capabilities, laying the groundwork for understanding how models learn.

Browse courses on Automatic Differentiation

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Find tutorials or courses specifically focused on automatic differentiation in PyTorch.
Work through examples and exercises to grasp the concepts practically.
Apply these techniques to a simple model to observe their impact on training.

Build a visual representation of a PyTorch pipeline

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Enhances your understanding of how data flows through a PyTorch pipeline, reinforcing the concepts of data preparation and model training.

Browse courses on Data Preprocessing

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Choose a specific PyTorch pipeline, such as image classification or natural language processing.
Create a diagram or flowchart that represents the stages of the pipeline.
Include details such as data sources, transformations, model architecture, and evaluation metrics.
Annotate the diagram with explanations of each stage and their purpose.

Three other activities

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Master linear regression training in PyTorch

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Provides hands-on practice in training linear regression models in PyTorch, honing your understanding of the training process and loss minimization.

Browse courses on Linear Regression

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Implement a linear regression model from scratch using PyTorch.
Experiment with different loss functions, optimizers, and learning rates.
Monitor the training process and evaluate the performance of your model.
Fine-tune your model's hyperparameters to achieve optimal results.

Collaborate on a mini-project using PyTorch

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Fosters collaboration and knowledge exchange by working on a real-world project with fellow students, applying your PyTorch skills in a practical setting.

Browse courses on Collaborative Learning

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Identify a suitable project idea that aligns with the course content.
Form a team of peers with complementary skills and interests.
Divide responsibilities and work together to implement the project using PyTorch.
Present your project to the class, sharing your findings and insights.

Design a PyTorch-based solution for a machine learning problem

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Challenges you to apply the knowledge and skills acquired in the course to solve a real-world machine learning problem, demonstrating your proficiency in PyTorch and problem-solving.

Browse courses on Machine Learning Solutions

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Identify a specific machine learning problem that interests you.
Gather and prepare the necessary data to train your model.
Design and implement a PyTorch-based solution, including data preprocessing, model training, and evaluation.
Present your solution in the form of a report or presentation, including your results, analysis, and potential improvements.

Career center

Learners who complete PyTorch Basics for Machine Learning will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their skills in data analysis and PyTorch to prepare and interpret large datasets, often through the use of machine learning and other statistical techniques. This knowledge can allow Data Scientists to more accurately and efficiently perform tasks such as predicting future trends and identifying potential risks.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers use their skills in PyTorch and other machine learning techniques to develop and implement predictive models. PyTorch is a popular framework for machine learning, and this course provides a solid foundation in its use. This makes the course a great stepping stone for those interested in a career as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts use their skills in data analysis and PyTorch to prepare and interpret large datasets. PyTorch is a popular framework for machine learning, and this course provides a solid foundation in its use. This makes the course a great stepping stone for those interested in a career as a Data Analyst.

See salaries and explore the career path for Data Analyst

Software Developer

Software Developers use their skills in programming to develop and implement software solutions. PyTorch is a popular framework for machine learning, and this course provides a solid foundation in its use. Software Developers who are interested in working with machine learning will find this course very helpful.

See salaries and explore the career path for Software Developer

Quantitative Analyst

Quantitative Analysts use their skills in mathematics and statistics to develop and implement financial models. PyTorch is a popular framework for machine learning, and this course provides a solid foundation in its use. This makes the course a great stepping stone for those interested in a career as a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Research Analyst

Research Analysts use their skills in data analysis and interpretation to conduct research and make recommendations. PyTorch is a popular framework for machine learning, and this course provides a solid foundation in its use. This makes the course a great stepping stone for those interested in a career as a Research Analyst.

See salaries and explore the career path for Research Analyst

Product Manager

Product Managers use their skills in business and technology to develop and launch new products. PyTorch is a popular framework for machine learning, and this course provides a solid foundation in its use. Product Managers who are interested in working with machine learning will find this course very helpful.

See salaries and explore the career path for Product Manager

Project Manager

Project Managers use their skills in planning and execution to manage projects from start to finish. PyTorch is a popular framework for machine learning, and this course provides a solid foundation in its use. Project Managers who are interested in working with machine learning will find this course very helpful.

See salaries and explore the career path for Project Manager

Business Analyst

Business Analysts use their skills in data analysis and interpretation to help businesses make better decisions. PyTorch is a popular framework for machine learning, and this course provides a solid foundation in its use. Business Analysts who are interested in working with machine learning will find this course very helpful.

See salaries and explore the career path for Business Analyst

Data Engineer

Data Engineers use their skills in data management and engineering to build and maintain data pipelines. PyTorch is a popular framework for machine learning, and this course provides a solid foundation in its use. Data Engineers who are interested in working with machine learning will find this course very helpful.

See salaries and explore the career path for Data Engineer

Systems Engineer

Systems Engineers use their skills in systems analysis and design to develop and implement complex systems. PyTorch is a popular framework for machine learning, and this course provides a solid foundation in its use. Systems Engineers who are interested in working with machine learning will find this course very helpful.

See salaries and explore the career path for Systems Engineer

Operations Research Analyst

Operations Research Analysts use their skills in mathematics and statistics to develop and implement solutions to complex problems. PyTorch is a popular framework for machine learning, and this course provides a solid foundation in its use. Operations Research Analysts who are interested in working with machine learning will find this course very helpful.

See salaries and explore the career path for Operations Research Analyst

Computer Scientist

Computer Scientists use their skills in computer science to develop and implement software solutions. PyTorch is a popular framework for machine learning, and this course provides a solid foundation in its use. Computer Scientists who are interested in working with machine learning will find this course very helpful.

See salaries and explore the career path for Computer Scientist

Statistician

Statisticians use their skills in statistics to collect and analyze data. PyTorch is a popular framework for machine learning, and this course provides a solid foundation in its use. Statisticians who are interested in working with machine learning will find this course very helpful.

See salaries and explore the career path for Statistician

Actuary

Actuaries use their skills in mathematics and statistics to assess and manage risk. PyTorch is a popular framework for machine learning, and this course provides a solid foundation in its use. Actuaries who are interested in working with machine learning will find this course helpful.

See salaries and explore the career path for Actuary