Building and Training Neural Networks with PyTorch from Coursera

Embark on a journey through the intricacies of neural networks using PyTorch, a powerful framework favored by professionals and researchers alike. The course begins with an in-depth exploration of classification models, where you'll learn to tackle different types of classification problems, utilize confusion matrices, and interpret ROC curves. As you progress, you'll engage in hands-on exercises to prepare data, build dataset classes, and construct network classes tailored for multi-class classification.

Moving forward, the course delves into Convolutional Neural Networks (CNNs) for image and audio classification. You'll discover the architecture of CNNs, implement image preprocessing techniques, and develop both binary and multi-class image classification models. Additionally, the course covers advanced topics like layer calculations and the application of CNNs in audio classification, ensuring you gain a holistic understanding of these powerful models.

The journey continues with a focus on object detection, where you'll explore accuracy metrics, labeling formats, and the YOLO (You Only Look Once) algorithm. Practical coding exercises will guide you through the setup, data preparation, model training, and inference processes. Furthermore, you'll delve into neural style transfer, pre-trained networks, transfer learning, and recurrent neural networks (RNNs), including hands-on coding with LSTM networks.

This course is designed for data scientists, AI professionals, and developers eager to master neural networks using PyTorch. Prerequisites include experience with Python and a foundational understanding of machine learning and deep learning concepts.

What's inside

Syllabus

Classification Models

In this module, we will delve into the realm of classification models, focusing on their types, evaluation metrics, and implementation. You will learn about key concepts such as the confusion matrix and ROC curve, and engage in practical exercises to build and evaluate multi-class classification models.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Uses PyTorch, a framework favored by professionals and researchers, which makes it highly relevant for those looking to build industry-standard neural networks

Explores convolutional neural networks (CNNs) for image and audio classification, which are essential skills for AI professionals working with multimedia data

Covers object detection using YOLO, a popular algorithm, and utilizes GPU resources, which is essential for building robust object detection models

Delves into neural style transfer, pre-trained networks, transfer learning, and recurrent neural networks (RNNs), expanding the learner's knowledge of advanced neural network techniques

Requires experience with Python and a foundational understanding of machine learning and deep learning concepts, so beginners may need to acquire these skills first

Teaches Long Short-Term Memory (LSTM) networks through practical coding exercises, which are essential for those working with sequential data

Reviews summary

Practical pytorch neural networks

According to learners, this course offers a largely positive experience, particularly excelling in providing practical, hands-on coding exercises that help solidify understanding of neural network implementation in PyTorch. Students appreciate the breadth of topics covered, including CNNs for image and audio, object detection with YOLO, style transfer, and RNNs/LSTMs. Many found the course material relevant and helpful for building practical skills. However, some reviewers noted that the course assumes a strong background in Python and machine learning fundamentals, potentially making it challenging for those who only meet the stated prerequisites. A few also mentioned that the pace can be quick in certain modules, and some code examples might require minor updates due to evolving library versions.

Code may need minor updates for libraries.

"I had to tweak some code examples to work with newer PyTorch versions and libraries."

"Minor errors found in some provided scripts that required debugging."

"Requires paying attention to library version compatibility as PyTorch updates frequently."

"The code provides a good starting point, but expect to do some minor adjustments."

Covers wide range of relevant NN topics.

"The course covers relevant topics like CNNs, RNNs, object detection, style transfer, and transfer learning."

"Appreciated the modules on implementing YOLO and neural style transfer."

"Provides a good overview of various important network architectures and applications."

"I found the breadth of topics very useful for exploring different areas of neural networks."

Strong hands-on coding reinforces learning.

"The hands-on coding examples were very helpful in understanding the concepts."

"Building the network classes really solidified my understanding through practice."

"Liked implementing YOLO from scratch, it was challenging but rewarding."

"The practical labs and demos reinforced the concepts well for me."

Some sections feel rushed or lack depth.

"Some sections felt a bit rushed, particularly the theory behind certain models."

"Could use more in-depth coverage on complex topics or optimization techniques."

"The focus is primarily on implementation, with less time spent on the underlying theory."

"Certain topics like audio classification felt too brief and could be expanded."

Requires more background than stated.

"This course assumes a strong Python and ML/DL background, not just foundational knowledge."

"I needed to supplement with other resources to fully grasp the basic concepts covered quickly."

"Felt a bit lost at times because the course moves fast if you don't have solid pre-requisites."

"Ensure you are comfortable with Python and have some deep learning experience before starting."

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 Building and Training Neural Networks with PyTorch with these activities:

Review Foundational Machine Learning Concepts

Show steps

Reinforce your understanding of core machine learning concepts to better grasp the nuances of neural networks.

Browse courses on Machine Learning Fundamentals

Show steps

Review key concepts like supervised and unsupervised learning.
Practice implementing basic machine learning algorithms.
Familiarize yourself with model evaluation metrics.

Brush Up on Python Programming

Show steps

Strengthen your Python skills, particularly in data manipulation and scientific computing, to facilitate smoother coding in PyTorch.

Browse courses on NumPy

Show steps

Practice using NumPy for numerical operations.
Review Pandas for data manipulation and analysis.
Familiarize yourself with Matplotlib for data visualization.

Read 'Deep Learning with PyTorch'

Show steps

Supplement your learning with a dedicated PyTorch resource to gain a deeper understanding of the framework.

View Deep Learning with PyTorch: Build, train, and... on Amazon

Show steps

Read the chapters relevant to the current module.
Experiment with the code examples provided in the book.
Compare the book's approach with the course's methods.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Implement Classification Models from Scratch

Show steps

Solidify your understanding of classification models by implementing them from scratch using PyTorch.

Show steps

Choose a classification dataset (e.g., MNIST, CIFAR-10).
Implement a neural network architecture for classification.
Train and evaluate your model using PyTorch.

Build an Image Classifier

Show steps

Apply your knowledge of CNNs to build a practical image classifier using PyTorch.

Show steps

Select an image dataset (e.g., Kaggle datasets).
Preprocess the images and create a PyTorch dataset.
Design and train a CNN model for image classification.
Evaluate the model's performance and fine-tune as needed.

Write a Blog Post on Neural Style Transfer

Show steps

Deepen your understanding of neural style transfer by explaining the concepts and implementation in a blog post.

Show steps

Research the underlying principles of neural style transfer.
Implement a style transfer algorithm using PyTorch.
Document your findings and create a clear, informative blog post.

Read 'Programming PyTorch for Deep Learning'

Show steps

Expand your knowledge with a practical guide to implementing deep learning models using PyTorch.

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

Show steps

Read the chapters relevant to your interests.
Experiment with the code examples provided in the book.
Apply the book's techniques to your own projects.

Career center

Learners who complete Building and Training Neural Networks with PyTorch will develop knowledge and skills that may be useful to these careers:

Deep Learning Engineer

A Deep Learning Engineer specializes in designing, implementing, and deploying deep learning models. This course, which builds a strong foundation in neural networks through PyTorch, is extremely helpful for those in this role. The course emphasizes hands-on exercises in a variety of key areas for a deep learning engineer, such as classification models, CNNs for image and audio, object detection with YOLO, and recurrent neural networks with LSTMs. Deep learning engineers, who need to work with the latest techniques, will find this course valuable for learning how to implement cutting-edge models.

See salaries and explore the career path for Deep Learning Engineer

Data Scientist

A Data Scientist uses statistical methods and machine learning to analyze data and extract insights. The course, which emphasizes hands-on experience, is designed for data scientists who wish to master neural networks using PyTorch, and its syllabus touches on the topics most relevant to the role. Data scientists use models to make predictions using the same techniques covered in this course. The course's work with classification models, convolutional neural networks, and recurrent neural networks provide the foundation required for modeling and analysis. Practical experience with PyTorch will be very helpful in a data science role.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer develops, implements, and maintains machine learning models, often using frameworks like PyTorch. This course helps build a foundation in neural networks, a crucial component of many machine learning models. The course's focus on implementing various neural network architectures, such as CNNs, object detection algorithms, and recurrent neural networks, directly contributes to the skill set of a machine learning engineer. Practical coding exercises, especially the work with dataset classes, is directly applicable to the work done by a machine learning engineer. This course helps prepare an individual for implementing complex neural networks.

See salaries and explore the career path for Machine Learning Engineer

Artificial Intelligence Specialist

An Artificial Intelligence Specialist builds and deploys AI solutions, often involving complex neural networks. This course, designed for AI professionals, provides an in-depth exploration of these networks using the PyTorch framework. The course focuses on several crucial topics for an AI specialist, such as object detection, style transfer, and recurrent neural networks. The course curriculum, which includes hands-on coding exercises, will help an AI specialist to design and implement the models needed to succeed in the field. This course is invaluable for those who wish to master neural networks for use in AI applications.

See salaries and explore the career path for Artificial Intelligence Specialist

Computer Vision Engineer

A Computer Vision Engineer works with images and videos to enable machines to 'see' and interpret visual information. The course directly addresses key techniques used by a computer vision engineer, such as image classification, object detection, and style transfer. This course helps individuals learn how to preprocess images, implement CNNs, and use object detection algorithms like YOLO using PyTorch. The practical work done with image datasets and models directly translates to the kind of tasks a computer vision engineer works on daily. This course serves as an introduction to working with image-based models.

See salaries and explore the career path for Computer Vision Engineer

Image Processing Specialist

An Image Processing Specialist works with image manipulation, enhancement, and analysis. This course covers several topics that are crucial for image processing specialists, such as image preprocessing, CNNs for image classification, neural style transfer, and pre-trained models. This course may help a specialist learn the latest techniques for image processing. The course's approach of teaching how to implement neural networks with PyTorch may be useful in the field of image processing.

See salaries and explore the career path for Image Processing Specialist

Audio Processing Engineer

An Audio Processing Engineer develops algorithms and systems for processing, analyzing, and modifying audio signals. This course offers a module specifically on using CNNs for audio classification. The engineer will be able to learn about exploratory data analysis and how to build audio classification models. This course may help provide foundational knowledge in audio data processing. The course's focus on implementing and evaluating audio models makes is useful for an engineer in this field.

See salaries and explore the career path for Audio Processing Engineer

Robotics Engineer

A Robotics Engineer designs, builds, and maintains robots and automated systems. The course work with image recognition and object detection is crucial to the role of a robotics engineer, who may be required to implement perception capabilities. This course helps build valuable skills with CNNs and object detection algorithms. The work with pre-trained networks and transfer learning can be very useful in the robotics field, where it is common to use these kinds of models. This course can help robotics engineers implement sophisticated intelligent systems.

See salaries and explore the career path for Robotics Engineer

Pattern Recognition Specialist

A Pattern Recognition Specialist develops and implements algorithms for identifying patterns in data. The course emphasis on neural networks, including classification models, CNNs, and RNNs, provides practical experience valuable to a pattern recognition specialist. The course offers hands-on experience with the PyTorch framework. The work with model training and evaluation is key to pattern recognition. This course may be helpful for those who wish to work as a pattern recognition specialist.

See salaries and explore the career path for Pattern Recognition Specialist

Research Scientist

A Research Scientist, often holding an advanced degree, conducts research in a specific area, often involving cutting-edge technologies like neural networks. This course, which emphasizes a deep understanding of neural network architectures, including CNNs, object detection algorithms, and RNNs, provides a solid foundation for such research. The course's focus on transfer learning and pre-trained models can be advantageous for a research scientist working in deep learning. Those in this role will find the detailed work on PyTorch helpful for use in advanced research. The course may be useful for those intending to pursue work in this role.

See salaries and explore the career path for Research Scientist

Algorithm Developer

An Algorithm Developer creates and refines algorithms for solving problems. The course provides hands-on experience implementing neural network algorithms, such as object detection with YOLO and recurrent neural networks such as LSTMs. An algorithm developer would be able to use the lessons presented in this course to implement these kinds of algorithms, which are used in a variety of domains. The course's focus on implementation makes it useful to an algorithm developer who needs to work with these kinds of models. It may be useful for an algorithm developer who is looking to improve their skills.

See salaries and explore the career path for Algorithm Developer

Data Analyst

A Data Analyst interprets data and presents findings to inform business decisions. This course may be useful to a data analyst who needs to explore data using machine learning models. The course introduces classification models, which can be used to categorize data and improve insight. Data analysts who need to work with images and audio will benefit from the course modules that introduce the basics of models for these data types. The course work with metrics like confusion matrices and ROC curves is a core skill for a data analyst who wishes to work with predictive models.

See salaries and explore the career path for Data Analyst

Software Developer

A Software Developer designs, develops, and tests software applications. This course helps a software developer who wishes to implement machine learning capabilities in their projects. The course work with building dataset classes and implementing neural networks using PyTorch will be relevant to a software developer who has to integrate AI modules into applications. The practical hands-on coding nature of the course, which teaches implementation of a variety of neural networks, may help a software developer to implement AI functionality.

See salaries and explore the career path for Software Developer

Signal Processing Engineer

A Signal Processing Engineer focuses on analyzing and modifying signals, in order to extract useful information from them. This course may be useful to a signal processing engineer who has to use neural networks. The course's work with audio data and CNNs may be helpful for use in signal processing tasks. The course is a potential introduction to handling complex audio data. The training in PyTorch, as well as the hands-on exercises, may be useful in this field.

See salaries and explore the career path for Signal Processing Engineer

Quantitative Analyst

A Quantitative Analyst, often working in finance, develops and implements mathematical and statistical models. The course may be useful to a quantitative analyst who wishes to experiment with neural network models, particularly recurrent neural networks and LSTMs. Quantitative analysts may use models like these to predict time series or other sequential data. The course's approach of teaching implementation with PyTorch may be helpful to an analyst in this field. This course may provide an introduction to using neural models for data analysis.

See salaries and explore the career path for Quantitative Analyst

Building and Training Neural Networks with PyTorch

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Practical pytorch neural networks

Activities

Career center

Reading list

Share

Similar courses