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Deep Learning with PyTorch

Joseph Santarcangelo

This course advances from fundamental machine learning concepts to more complex models and techniques in deep learning using PyTorch.

This comprehensive course covers techniques such as Softmax regression, shallow and deep neural networks, and specialized architectures, such as convolutional neural networks.

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This course advances from fundamental machine learning concepts to more complex models and techniques in deep learning using PyTorch.

This comprehensive course covers techniques such as Softmax regression, shallow and deep neural networks, and specialized architectures, such as convolutional neural networks.

In this course, you will explore Softmax regression and understand its application in multi-class classification problems. You will learn to train a neural network model and explore Overfitting and Underfitting, multi-class neural networks, backpropagation, and vanishing gradient. You will implement Sigmoid, Tanh, and Relu activation functions in Pytorch.

In addition, you will explore deep neural networks in Pytorch using nn Module list and convolution neural networks with multiple input and output channels.

You will engage in hands-on exercises to understand and implement these advanced techniques effectively. In addition, at the end of the course, you will gain valuable experience in a final project on a convolutional neural network (CNN) using PyTorch.

This course is suitable for all aspiring AI engineers who want to gain advanced knowledge on deep learning using PyTorch. It requires some basic knowledge of Python programming and basic mathematical concepts such as gradients and matrices.

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What's inside

Syllabus

Logistic Regression Cross Entropy Loss
In this module, you will understand problem with mean squared error, and discuss maximum likelihood estimation. And then we'll see how to go from maximum likelihood estimation to calculating cross entropy loss, then Train the model PyTorch. You will apply your learnings in labs and test your concepts in quizzes.
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Softmax Regression
In this module, you will learn how to use Lines to classify data and understand the working of the Softmax function. The module also covers the argmax function and its utilization. You will create a custom module for Softmax using the nn.module package in PyTorch and use a Softmax classifier to create a model for performing classifications. You will apply your learnings in labs and test your concepts in quizzes.
Shallow Neural Networks
In this module, you will create a neural network with a hidden layer using nn.Module and nn.Sequential. You will learn to train a neural network model and how neurons can improve a model. The model will also explain how to construct networks with multiple dimensional input in PyTorch. In addition, you will explore Overfitting and Underfitting, multi-class neural networks, back propagation and vanishing gradient. Finally, you will implement Sigmoid, Tanh and Relu activation functions in Pytorch. You will apply your learnings in labs and test your concepts in quizzes.
Deep Networks
This module provides an overview of deep neural network in Pytorch. You will learn to implement deep neural network in Pytorch using nn Module list. The module includes concepts like Dropout, layers, and weights. It will also discuss the problem of not initializing the Weights in a Neural Network model correctly and how to fix it. The module will also explore different initialization methods in Pytorch, gradient descent, and batch normalization. You will apply your learnings in labs and test your concepts in quizzes.
Convolutional Neural Networks
This module describes convolution and how to determine the size of the activation map. The module also covers activation functions and max pooling. In addition, the modaule discusses convolution with multiple input and output channels. It summarizes Convolutional Neural Network Constructor, Forward Step, and training in PyTorch. You will learn concepts like graphics processing units (GPUs), CUDA, residual network, and Resnet18. You will apply your learnings in labs and test your concepts in quizzes.
Final Project
In this module, you can complete a peer-reviewed final project to demonstrate and prove the skills you gained in the previous modules

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Covers Softmax regression, neural networks, and convolutional neural networks, which are fundamental techniques for building various AI applications
Includes a final project on a convolutional neural network (CNN) using PyTorch, providing practical experience for learners to showcase their skills
Explores deep neural networks using nn Module list, which is a core component for building complex models in PyTorch
Examines backpropagation and vanishing gradients, which are essential concepts for training deep neural networks effectively
Requires some basic knowledge of Python programming and basic mathematical concepts such as gradients and matrices, which may be a barrier for some learners
Discusses the problem of not initializing the weights in a neural network model correctly and how to fix it, which is a practical consideration for learners

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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 with PyTorch with these activities:
Review Linear Algebra Fundamentals
Solidify your understanding of linear algebra concepts, which are crucial for understanding the mathematical foundations of deep learning and neural networks.
Browse courses on Linear Algebra
Show steps
  • Review matrix operations such as addition, multiplication, and transposition.
  • Study vector spaces, linear independence, and basis vectors.
  • Practice solving systems of linear equations.
Brush Up on Calculus Concepts
Strengthen your calculus knowledge, particularly derivatives and the chain rule, as these are essential for understanding backpropagation and gradient descent in neural networks.
Browse courses on Calculus
Show steps
  • Review the concept of derivatives and their applications.
  • Understand the chain rule and how to apply it.
  • Study gradient descent and optimization techniques.
Read 'Programming PyTorch for Deep Learning' by Ian Pointer
Use a practical guide to learn how to implement deep learning models using PyTorch.
View Programming PyTorch for Deep Learning: Creating... on Amazon
Show steps
  • Read the chapters relevant to the current module.
  • Run the code examples provided in the book.
  • Modify the code examples to experiment with different parameters and architectures.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Read 'Deep Learning' by Goodfellow, Bengio, and Courville
Supplement your learning with a comprehensive textbook that covers the theoretical underpinnings of deep learning.
View Deep Learning on Amazon
Show steps
  • Read the chapters relevant to the current module.
  • Take notes on key concepts and equations.
  • Work through the examples provided in the book.
Implement Neural Networks from Scratch
Reinforce your understanding of neural network architectures by implementing them from scratch using only basic PyTorch functionalities.
Show steps
  • Implement a simple neural network with one hidden layer.
  • Implement backpropagation and gradient descent.
  • Experiment with different activation functions and learning rates.
Create a Blog Post Explaining Backpropagation
Solidify your understanding of backpropagation by explaining it in a clear and concise manner in a blog post.
Show steps
  • Research and understand the backpropagation algorithm thoroughly.
  • Write a clear and concise explanation of the algorithm.
  • Include diagrams and examples to illustrate the concepts.
  • Publish the blog post on a platform like Medium or your personal website.
Build a CNN to Classify Images
Apply your knowledge of convolutional neural networks by building a CNN to classify images from a standard dataset like CIFAR-10 or MNIST.
Show steps
  • Choose a dataset and load it into PyTorch.
  • Design a CNN architecture with convolutional and pooling layers.
  • Train the CNN on the dataset.
  • Evaluate the performance of the CNN on a test set.

Career center

Learners who complete Deep Learning with PyTorch will develop knowledge and skills that may be useful to these careers:
Deep Learning Engineer
A deep learning engineer specializes in creating and deploying deep learning models. This encompasses areas like image recognition, natural language understanding, and time series analysis. This course is a perfect fit for the objectives of a deep learning engineer. Given its focus on using PyTorch to build both basic and advanced models, including convolutional neural networks, this course provides the exact experience that will help an engineer in this position be successful. The hands-on labs and final project in the course help build the practical skills required for this role. The specific content of this course directly aligns with the daily work of a deep learning engineer.
See salaries and explore the career path for Deep Learning Engineer
Artificial Intelligence Engineer
An artificial intelligence engineer develops and implements AI solutions. These could include computer vision systems, natural language processing, or predictive analytics. This course provides a valuable foundation for an artificial intelligence engineer. In particular, its focus on deep learning, especially with PyTorch, is highly relevant. This course's coverage of neural networks, convolutional neural networks, and techniques like backpropagation and activation functions are crucial for anyone looking to work in artificial intelligence. The hands-on experience and final project in the course are particularly useful to gain practical skills for artificial intelligence engineering.
See salaries and explore the career path for Artificial Intelligence Engineer
Machine Learning Engineer
A machine learning engineer develops and implements machine learning models. These models are used in a wide range of applications, from predicting customer behavior to improving medical diagnoses. This course, which covers deep learning techniques using PyTorch, is directly relevant to a machine learning engineer's role. Specifically, the course covers concepts like Softmax regression, neural networks, and convolutional neural networks. Learning about backpropagation and activation functions is essential, as is hands-on experience with PyTorch. This course provides the tools necessary to build more complex models, essential for an aspiring machine learning engineer.
See salaries and explore the career path for Machine Learning Engineer
Computer Vision Engineer
A computer vision engineer focuses on developing systems that can interpret and understand visual information. This role applies to areas such as image recognition, object tracking, and autonomous driving. A computer vision engineer would benefit greatly from this course. The course’s focus on convolutional neural networks, as well as deep neural networks, is essential for anyone who wants to work in this field. Understanding the topics covered in this course, such as activation functions and techniques for training deep neural networks, is very important for the daily tasks of a computer vision engineer. The hands-on exercises in this course will be particularly beneficial.
See salaries and explore the career path for Computer Vision Engineer
Research Scientist
A research scientist conducts studies in any number of fields. For example, they may research new algorithms, or they may investigate questions related to the natural world, using computational approaches. This course is particularly useful for a research scientist who works in artificial intelligence, machine learning, or computer vision. The course covers many important topics like deep neural networks, convolutional neural networks, and backpropagation. A background in the methods covered by this course is essential for many research-oriented positions. Additionally, in learning to implement these techniques using PyTorch, the research scientist is better positioned to conduct their research in a practical way. An advanced degree is typically required for this role.
See salaries and explore the career path for Research Scientist
Image Processing Specialist
An image processing specialist works with digital images. This role involves enhancing, analyzing, and manipulating images for a variety of applications. This course is particularly helpful for an image processing specialist, as the course covers convolutional neural networks in detail. These are useful for many image processing tasks. The course's focus on PyTorch provides hands-on experience in implementing image processing algorithms. An image processing specialist who takes this course will be better prepared to handle more complicated image-related tasks.
See salaries and explore the career path for Image Processing Specialist
Natural Language Processing Engineer
A natural language processing engineer develops systems that enable computers to understand, process, and generate human language. This area includes applications like chatbots, text analysis, and language translation. The skills in this course may be useful for a natural language processing engineer. The course's exploration of deep neural networks and convolutional neural networks provides a foundation for work in this area. Learning how to construct and train models using Pytorch may allow the NLP engineer to implement advanced solutions in this domain. The skills and techniques discussed in the course may help an engineer in this role.
See salaries and explore the career path for Natural Language Processing Engineer
Research Fellow
A research fellow engages in advanced research, often in an academic setting. This role typically involves designing and conducting studies as well as analyzing data. This course may be useful for a research fellow, especially if they are interested in using machine learning to advance their work. The course provides an understanding of deep learning, neural networks, and the use of PyTorch. These skills may apply to a wide variety of research topics, from image analysis to financial modeling. The hands-on exercises and final project may be particularly useful to a research fellow in acquiring the practical skills they need for their research. An advanced degree is typically required for this role.
See salaries and explore the career path for Research Fellow
Data Scientist
A data scientist uses statistical and machine learning techniques to analyze data and to provide insights and predictions. This role often requires working with large datasets and complex models, and machine learning is a central tool for this role. This course may be useful for a data scientist, as the course covers topics like Softmax regression, neural networks, and deep learning. While this role is not only focused on deep learning, the course’s content on neural network structures, backpropagation, and the implementation of activation functions could be helpful. The course's final project will also provide relevant hands-on experience for a data scientist.
See salaries and explore the career path for Data Scientist
Bioinformatics Analyst
A bioinformatics analyst applies computational methods to solve biological problems. These tasks may involve analyzing genomic data, protein structures, or other biological information. This course may be helpful for a bioinformatics analyst interested in using deep learning techniques, as the course covers neural networks and convolutional neural networks, which may be used to address some problems in the field. In particular, learning to implement these models using PyTorch may help the analyst in this role bring their machine learning approaches to life. The hands-on experience in the course may help the bioinformatics analyst be better prepared to work with these complex models.
See salaries and explore the career path for Bioinformatics Analyst
Robotics Engineer
A robotics engineer designs, develops, and tests robots and robotic systems. These systems often rely on advanced AI and machine learning, especially for areas like navigation, object recognition, and decision-making. This course may be beneficial to the work of a robotics engineer. Specifically, this course's focus on deep learning, especially convolutional neural networks, helps build a foundation for robotic vision systems. The hands-on exercises and final project of this course may also help develop practical skills for a robotics engineer. This course may help the robotics engineer gain more expertise in implementing AI systems that can enhance robotic functionality.
See salaries and explore the career path for Robotics Engineer
Algorithm Developer
An algorithm developer designs and implements algorithms to solve complex problems. These problems may range from optimizing system performance to developing solutions for artificial intelligence. This course may be useful for an algorithm developer who needs to work in machine learning. The course's coverage of deep neural networks, backpropagation, and convolutional neural networks may help enhance the skills of an algorithm developer in this domain. The hands-on experience gained in this course may also be useful for practically implementing new algorithms.
See salaries and explore the career path for Algorithm Developer
Quantitative Analyst
A quantitative analyst, also known as a quant, uses mathematical and statistical methods to solve problems in financial markets. This typically involves building sophisticated models for things like risk management, trading strategies, or price forecasting. This course may be useful for a quant who wants to use advanced machine learning techniques. The course covers deep learning concepts, such as neural networks, making it relevant to this field. A quant may be able to use these techniques for complex modeling and prediction. The final project of this course may help a quant add advanced techniques to their repertoire.
See salaries and explore the career path for Quantitative Analyst
Data Analyst
A data analyst examines data to identify trends and to provide insights that help businesses make better decisions. While data analysts primarily work with descriptive statistics, they increasingly implement machine learning methods. This course may be useful for a data analyst looking to expand their capabilities. It covers important topics like Softmax regression and neural networks. By understanding these techniques using Pytorch, a data analyst may be able to develop more complex models and get more advanced insights. The practical skills that come from the labs and final project may be beneficial.
See salaries and explore the career path for Data Analyst
Software Engineer
A software engineer designs, develops, and maintains software systems. While software engineering work does not often include the development of machine learning models, a background in artificial intelligence techniques may be useful to certain types of software engineers. This course may be helpful to a software engineer who is interested in artificial intelligence. This course's content on neural network architectures, activation functions, and training methods using PyTorch may be beneficial, enabling software engineers to integrate AI functionalities into their softwares. The hands-on approach that the course takes may also enhance the practical problem solving abilities of a software engineer.
See salaries and explore the career path for Software Engineer

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Deep Learning with PyTorch.
Cover image
Deep Learning
Save
Provides a comprehensive introduction to deep learning, covering a wide range of topics from basic concepts to advanced architectures. It is often used as a textbook in university courses and is considered a valuable resource for both beginners and experienced practitioners. This book adds significant depth to the course material, providing a strong theoretical foundation. It is particularly useful as a reference for understanding the underlying principles of the algorithms and techniques covered in the course.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Programming PyTorch for Deep Learning
Save
Provides a practical guide to using PyTorch for deep learning. It covers the fundamentals of PyTorch and demonstrates how to build and train various deep learning models. This book is particularly useful for those who prefer a hands-on approach to learning. It provides clear examples and code snippets that can be easily adapted to different projects. It serves as a valuable reference for implementing deep learning models in PyTorch.
Programming PyTorch for Deep Learning: Creating and...
Paperback
$$
Programming PyTorch for Deep Learning: Creating and...
Kindle Edition
$$

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Effort
6 weeks of study, 2-3 hours/week
Level
Intermediate
Via
Coursera
Institution
IBM
Instructor
Joseph Santarcangelo
Session
February 3, 2025 - March 24, 2025
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова magyar nyelv Bahasa Indonesia ไทย қазақша اُردُو, فارسی বাংলা tiếng Việt پښتو آذربایجان دیلی
Good to know
Covers Softmax regression, neural networks, and convolutional neural networks, which are fundamental techniques for building various AI applications
Includes a final project on a convolutional neural network (CNN) using PyTorch, providing practical experience for learners to showcase their skills
Explores deep neural networks using nn Module list, which is a core component for building complex models in PyTorch
Examines backpropagation and vanishing gradients, which are essential concepts for training deep neural networks effectively
Requires some basic knowledge of Python programming and basic mathematical concepts such as gradients and matrices, which may be a barrier for some learners
Discusses the problem of not initializing the weights in a neural network model correctly and how to fix it, which is a practical consideration for learners
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