Deep Learning - Computer Vision for Beginners Using PyTorch from Coursera

This course offers a comprehensive introduction to PyTorch and deep learning for computer vision, with sections on Python fundamentals for those new to the language or needing a refresher. The curriculum begins with PyTorch basics, followed by instructions on accessing free GPU resources and coding on GPU.

Students will explore PyTorch’s AutoGrad feature and use it to implement gradient descent. The course covers creating deep learning models and convolutional neural networks (CNNs), and applying these skills to real-world datasets. Additionally, students will learn to use key Python libraries such as NumPy, Pandas, and Matplotlib, and will undertake a mini project to build a hangman game in Python.

By the end of the course, participants will be equipped to perform computer vision tasks using deep learning. This course is ideal for software developers, machine learning practitioners, and data scientists. Basic Python knowledge is beneficial but not required.

What's inside

Syllabus

Welcome Aboard

In this module, we will introduce you to the course, outlining what you can expect and why learning PyTorch is beneficial for diving into deep learning and computer vision. We’ll provide a brief overview of the course structure and demonstrate the power of PyTorch through a quick demo.

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Uses PyTorch, which is widely adopted in both industry and academia for building and deploying deep learning models, especially in the field of computer vision

Covers CNNs, which are fundamental for various computer vision tasks, including image recognition, object detection, and image segmentation, making it highly relevant for practical applications

Includes optional modules on Python fundamentals, NumPy, Pandas, and Matplotlib, which are essential tools for data science and machine learning, providing a solid foundation for beginners

Explores the LeNet architecture, offering insights into the historical development of CNNs and their practical applications, which can enrich understanding of modern architectures

Teaches how to access free GPU resources and code on GPU, which is essential for training deep learning models efficiently, especially for those without access to local GPU hardware

Features a mini project to build a hangman game in Python, which allows learners to apply their Python skills in a practical and engaging way, reinforcing their understanding

Reviews summary

Pytorch & computer vision for beginners

According to students, this course offers a solid introduction to Deep Learning and Computer Vision using PyTorch. Learners appreciated the clear explanations of core concepts like tensors and AutoGrad. Many found the hands-on coding exercises, especially in building CNNs, to be particularly helpful and practical. The course is widely seen as well-structured for beginners in the field, although some mentioned the pace can be fast at times, potentially requiring prior coding familiarity beyond absolute basics, despite the optional Python modules which are useful for a refresher but might not suffice for complete novices.

Python refreshers are included (optional).

"The optional Python basics modules were a useful refresher, though I skipped most as I already knew them."

"Good that the Python part is optional; it saved me time focusing on the DL/CV specifics."

"While there are Python basics, I think someone completely new to programming might still struggle."

Pace is mostly good, sometimes fast.

"For a beginner in DL/CV, the pace was mostly good, though some sections moved quite quickly."

"While labeled for beginners, some prior coding experience, especially in Python, felt necessary to keep up."

"Sometimes I had to rewatch sections to fully grasp the concepts before moving on."

Practical coding labs are very helpful.

"The hands-on coding exercises and labs were the most valuable part of the course for me. Practical application solidifies learning."

"Working through the examples and building the models alongside the instructor was essential."

"I enjoyed the practical projects and found they reinforced the theoretical concepts well."

Instructor explains concepts clearly.

"The explanations provided for complex concepts like AutoGrad were very clear and easy to follow."

"I really appreciated how the instructor broke down the topics into digestible parts."

"Lectures were well-presented and helped me understand the material without getting lost."

Provides a strong intro to PyTorch/CNN.

"This course gave me a solid foundation in PyTorch for computer vision, exactly what I needed to get started."

"I feel like I have a good grasp of the basics of tensors, AutoGrad, and building my first CNN after completing this."

"Learned the fundamentals effectively and now feel confident to explore more complex topics on my own."

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 Deep Learning - Computer Vision for Beginners Using PyTorch with these activities:

Review Linear Algebra Fundamentals

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Reinforce your understanding of linear algebra concepts, which are foundational for understanding tensors and matrix operations in PyTorch.

Browse courses on Linear Algebra

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Review matrix operations such as addition, multiplication, and transposition.
Study vector spaces, linear independence, and basis vectors.
Practice solving systems of linear equations.

Read 'Deep Learning' by Goodfellow, Bengio, and Courville

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Gain a deeper understanding of the theoretical foundations of deep learning and neural networks.

View Deep Learning on Amazon

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Read the chapters related to convolutional neural networks and optimization algorithms.
Take notes on key concepts and mathematical formulations.
Attempt the exercises at the end of each chapter to test your understanding.

Read 'Programming PyTorch for Deep Learning' by Ian Pointer

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Gain practical experience with PyTorch by following the examples and exercises in this book.

View Programming PyTorch for Deep Learning: Creating... on Amazon

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Work through the examples in the book, focusing on the sections related to computer vision.
Experiment with different hyperparameters and architectures.
Adapt the examples to your own projects and datasets.

Four other activities

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Write a Blog Post on PyTorch AutoGrad

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Deepen your understanding of AutoGrad by explaining it to others in a blog post.

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Research AutoGrad and its underlying mechanisms.
Write a clear and concise explanation of AutoGrad, including examples.
Include diagrams or visualizations to illustrate the concepts.
Publish the blog post on a platform like Medium or your personal website.

Implement CNNs on Different Datasets

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Solidify your understanding of CNNs by implementing them on various datasets beyond CIFAR10.

Browse courses on CNN

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Choose a dataset like MNIST, Fashion-MNIST, or a custom image dataset.
Preprocess the data and prepare it for training.
Implement a CNN architecture using PyTorch.
Train and evaluate the model, adjusting hyperparameters as needed.

Follow PyTorch Tutorials on Transfer Learning

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Learn how to leverage pre-trained models for computer vision tasks using transfer learning techniques.

Browse courses on Transfer Learning

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Find tutorials on the PyTorch website or other reputable sources.
Follow the tutorials step-by-step, understanding the code and concepts.
Experiment with different pre-trained models and datasets.
Apply transfer learning to your own image classification projects.

Build an Image Classifier for a Specific Domain

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Apply your knowledge to build a real-world image classifier, reinforcing your skills in data preprocessing, model building, and training.

Browse courses on Image Classification

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Choose a specific domain, such as classifying different types of flowers or animals.
Gather and preprocess a dataset for your chosen domain.
Design and implement a CNN architecture using PyTorch.
Train and evaluate the model, optimizing its performance.
Deploy the model using a framework like Flask or Streamlit.

Career center

Learners who complete Deep Learning - Computer Vision for Beginners Using PyTorch will develop knowledge and skills that may be useful to these careers:

Deep Learning Engineer

A Deep Learning Engineer is at the forefront of creating next-generation intelligent systems, which includes developing and implementing complex artificial neural networks. This course, with its detailed coverage of PyTorch and deep learning, including the essential aspects of creating convolutional neural networks, helps a deep learning engineer train complex models. The practical experience with gradient descent, AutoGrad and GPU usage makes this course essential to anyone wishing to enter, or advance in, this career field. The ability to work with real world datasets, also taught in this course, is essential to success for a deep learning engineer.

See salaries and explore the career path for Deep Learning Engineer

Computer Vision Engineer

A Computer Vision Engineer develops algorithms that enable machines to 'see' and interpret images or videos, such as in robotics, autonomous vehicles, and medical imaging. This course helps build a foundation in deep learning techniques, essential for many computer vision tasks, by teaching how to create and train convolutional neural networks using PyTorch. The course's focus on real-world datasets and practical implementation enables a computer vision engineer to apply these concepts directly to projects. Moreover, skills in Python, NumPy, Pandas, and Matplotlib, taught by this course, are valuable for data preprocessing and analysis during development.

See salaries and explore the career path for Computer Vision Engineer

Machine Learning Engineer

A Machine Learning Engineer builds and deploys machine learning models, which are often used in areas such as image processing, predictive analytics, and natural language understanding. This course provides a strong foundation in deep learning using PyTorch, including how to use AutoGrad to implement gradient descent and how to design and train deep neural networks, including convolutional neural networks. A machine learning engineer can leverage the practical skills gained from coding on GPUs, and working with real-world datasets in this course. The Python knowledge and libraries taught also facilitate data manipulation and model evaluation.

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Image Processing Specialist

An Image Processing Specialist uses computational techniques to enhance and modify digital images, often for applications in medical imaging, security, and entertainment. This course directly informs this career path by teaching deep learning techniques for computer vision with PyTorch, with a particular focus on convolutional neural networks. This knowledge will enable an image processing specialist to implement advanced image analysis and manipulation algorithms. Furthermore, the skills in Python, NumPy, Pandas, and Matplotlib are helpful for data manipulation and visualization in image processing tasks.

See salaries and explore the career path for Image Processing Specialist

Autonomous Vehicle Engineer

An Autonomous Vehicle Engineer designs the systems for vehicles to drive without human input. This course helps build a solid foundation in computer vision, a critical component of self-driving technology, through instruction on creating convolutional neural networks in PyTorch. An autonomous vehicle engineer needs to be able to work with real-world datasets, and this course provides that opportunity. The Python skills, and knowledge of using libraries such as NumPy and Pandas, also taught in this course, are valuable for the analysis of sensor data.

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Artificial Intelligence Specialist

An Artificial Intelligence Specialist works with various technologies to create intelligent systems that solve complex problems. This course provides instruction in deep learning, a core component of many AI applications. Skills in PyTorch, building neural networks, and using convolutional neural networks learned in this course are directly applicable to the creation of AI-based systems. A strong grasp of Python, NumPy, Pandas, and Matplotlib, which this course also covers, is additionally helpful for data analysis and visualization, enhancing model development and understanding.

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Academic Researcher

An Academic Researcher conducts research, usually within universities, with a view to publishing academic papers. This course may be useful for those conducting research in computer vision or machine learning as it provides a solid introduction into PyTorch and deep learning using convolutional neural networks. This course provides experience using AutoGrad, as well as in creating custom modules. Additionally, the Python, NumPy, and Pandas tools also taught are frequently used in many research projects; this course would be instrumental for anyone wishing to conduct research in the fields of deep learning and computer vision, especially those who need to work with datasets.

See salaries and explore the career path for Academic Researcher

Robotics Engineer

A Robotics Engineer designs, builds, and programs robots, using skills from multiple engineering fields, including computer science. This course may be useful because it teaches the deep learning techniques necessary for robots to interpret data coming from cameras and other sensors. The ability to create and train convolutional neural networks with PyTorch, as taught in this course, is vital in developing robots that can navigate and interact with their environment. A robotics engineer often needs to perform data analysis, for which a foundation in Python, NumPy, and Pandas, provided by this course, will be instrumental.

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Research Scientist

A Research Scientist conducts scientific studies in order to add to the existing body of knowledge. This course may be useful to a research scientists wishing to add deep learning to their research toolkit. The course instruction on PyTorch and deep learning, along with AutoGrad and the creation of convolutional neural networks, provides the tools necessary to conduct research using these techniques. Moreover, the Python, NumPy, and Pandas tools, also taught in this course, are useful for data analysis, which is often essential for research work.

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Remote Sensing Analyst

A Remote Sensing Analyst processes data obtained from satellites or aircraft, often using image analysis techniques. This course introduces deep learning principles, including convolutional neural networks, useful for interpreting remote sensing imagery. Although the course does not directly focus on remote sensing, the skills in Python, NumPy, and Pandas, also taught in the course, can be used for remote sensing data processing and analysis. Furthermore, the PyTorch instruction aids in building and training models for image interpretation from satellite data.

See salaries and explore the career path for Remote Sensing Analyst

Data Scientist

A Data Scientist analyzes large datasets to uncover trends and insights, often using machine learning and statistical techniques. While this course focuses on deep learning and computer vision, it may be useful to a data scientist as it introduces convolutional neural networks and deep learning with PyTorch. This course also introduces fundamental Python skills and libraries like NumPy, Pandas, and Matplotlib. Though the course is not primarily focused on data science, these tools help in data handling and visualization, which data scientists use daily to prepare, analyze, and present results.

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Bioinformatics Analyst

A Bioinformatics Analyst applies computer science and statistics to biological data, often using images of cells or other biological structures. While this course focuses on computer vision and image processing for general purposes, it may be useful because the underlying principles and methods related to convolutional neural networks and deep learning, taught in this course, are also applicable to processing biological images. Further, the skills in Python and its data analysis libraries, taught in this course, help with manipulating large biological datasets and extracting meaningful insights.

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Software Developer

A Software Developer designs and implements applications, and this course may be useful as it introduces the fundamentals of deep learning. This course provides skills in PyTorch, and instruction on how to create convolutional neural networks. A software developer may find that the instruction this course provides on machine learning makes them more valuable in the workplace. In addition, the Python skills, along with NumPy, Pandas, and Matplotlib, are useful for a wide variety of software development tasks, particularly when developing tools that involve large amounts of data.

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Game Developer

A Game Developer creates video games, which sometimes requires the use of advanced machine learning and deep learning techniques. This course introduces the fundamental concepts of deep learning, and provides instruction in PyTorch. The skills in Python, NumPy, and Pandas also taught in this course can be helpful when creating simulation and data analysis tools to aid game development. The mini project to create a Hangman game in Python may also be helpful to a game developer.

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Data Visualization Specialist

A Data Visualization Specialist transforms data into understandable visual formats, using tools to create graphs and charts. This course may be helpful to a data visualization specialist because it provides instruction in Python and Matplotlib, which are used for creating data-based visuals. Although the course's main focus is deep learning, it still offers a foundation in the data manipulation and visualization tools that a data visualization specialist may use in their daily work. These tools are needed to generate presentations that are informative and well-designed. The course's knowledge of NumPy and Pandas also helps with pre-processing data.

See salaries and explore the career path for Data Visualization Specialist

Deep Learning - Computer Vision for Beginners Using PyTorch

What's inside

Syllabus

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Reviews summary

Pytorch & computer vision for beginners

Activities

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