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Building Your First PyTorch Solution

Janani Ravi
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This course is no longer available. Find something similar by browsing:
PyTorch Deep Learning Machine Learning Regression Analysis Classification Dropout Activation Functions

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Read about what's good
what should give you pause
and possible dealbreakers
Develops practical skills that are directly applicable in the field of deep learning, including building and debugging models and using optimizers
Emphasizes the practical aspects of PyTorch, enabling learners to quickly apply their knowledge to real-world projects
Covers the fundamentals of PyTorch, making it accessible to beginners in deep learning
Provides step-by-step guidance through building regression and classification models, offering a hands-on learning experience
Leverages industry-standard libraries and tools, ensuring that learners gain skills that are relevant to the field
Taught by experienced instructors, Janani Ravi, who brings expertise in the field of deep learning

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Career center

Learners who complete Building Your First PyTorch Solution will develop knowledge and skills that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineers are experts in the design and development of deep learning models. They use their knowledge of deep learning algorithms and techniques to develop models that can solve complex problems in areas such as computer vision, natural language processing, and speech recognition. This course can help you build the skills you need to become a Deep Learning Engineer by providing you with a hands-on introduction to PyTorch. You will learn how to build and train deep learning models, which are essential for many deep learning applications.
See salaries and explore the career path for Deep Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, building, and deploying machine learning models. They use their knowledge of machine learning algorithms and techniques to develop models that can solve real-world problems. This course can help you build the skills you need to become a Machine Learning Engineer by providing you with a hands-on introduction to PyTorch. You will learn how to build and train deep learning models, which are essential for many machine learning applications.
See salaries and explore the career path for Machine Learning Engineer
Data Scientist
Data Scientists are experts in the collection, analysis, and interpretation of data. They use their skills to extract meaningful insights from data and help organizations make better decisions. This course provides a strong foundation in PyTorch, a popular deep learning framework, which is becoming increasingly important for Data Scientists. By learning how to build and train deep learning models, you can increase your value as a Data Scientist and open up new career opportunities.
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Data Analyst
Data Analysts collect, analyze, and interpret data to help organizations make better decisions. They use their skills in data analysis techniques and tools to extract meaningful insights from data. This course can help you build the skills you need to become a Data Analyst by providing you with a hands-on introduction to PyTorch. You will learn how to build and train deep learning models, which can be used to automate data analysis tasks and improve the accuracy of data analysis results.
See salaries and explore the career path for Data Analyst
Software Engineer
Software Engineers design, develop, and maintain software systems. They use their knowledge of programming languages and software development techniques to create software that meets the needs of users. This course can help you build the skills you need to become a Software Engineer by providing you with a hands-on introduction to PyTorch. You will learn how to build and train deep learning models, which can add value to software products.
See salaries and explore the career path for Software Engineer
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They use their knowledge of financial markets and quantitative analysis techniques to develop models that can predict future market behavior. This course can help you build the skills you need to become a Quantitative Analyst by providing you with a hands-on introduction to PyTorch. You will learn how to build and train deep learning models, which can be used to automate financial data analysis tasks and improve the accuracy of financial analysis results.
See salaries and explore the career path for Quantitative Analyst
Product Manager
Product Managers are responsible for the development and launch of new products. They use their knowledge of market research, product development, and marketing to create products that meet the needs of customers. This course can help you build the skills you need to become a Product Manager by providing you with a hands-on introduction to PyTorch. You will learn how to build and train deep learning models, which can be used to automate product development tasks and improve the accuracy of product development decisions.
See salaries and explore the career path for Product Manager
Research Scientist
Research Scientists conduct research in a variety of fields, including computer science, engineering, and medicine. They use their knowledge of scientific methods and research techniques to develop new technologies and solve complex problems. This course can help you build the skills you need to become a Research Scientist by providing you with a hands-on introduction to PyTorch. You will learn how to build and train deep learning models, which can be used to automate scientific research tasks and improve the accuracy of scientific research results.
See salaries and explore the career path for Research Scientist
Business Analyst
Business Analysts analyze business processes and develop solutions to improve efficiency and effectiveness. They use their knowledge of business analysis techniques and tools to identify problems and develop solutions that meet the needs of stakeholders. This course can help you build the skills you need to become a Business Analyst by providing you with a hands-on introduction to PyTorch. You will learn how to build and train deep learning models, which can be used to automate business analysis tasks and improve the accuracy of business analysis results.
See salaries and explore the career path for Business Analyst
Consultant
Consultants provide advice and guidance to organizations on a variety of topics, including business strategy, operations, and technology. They use their knowledge of business consulting techniques and tools to help organizations improve their performance. This course can help you build the skills you need to become a Consultant by providing you with a hands-on introduction to PyTorch. You will learn how to build and train deep learning models, which can be used to automate consulting tasks and improve the accuracy of consulting results.
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Teacher
Teachers develop and deliver lesson plans to help students learn. They use their knowledge of teaching methods and curriculum to create engaging and effective learning experiences. This course can help you build the skills you need to become a Teacher by providing you with a hands-on introduction to PyTorch. You will learn how to build and train deep learning models, which can be used to automate teaching tasks and improve the accuracy of teaching results.
See salaries and explore the career path for Teacher
Technical Writer
Technical Writers create and maintain technical documentation, such as user manuals, white papers, and training materials. They use their knowledge of technical writing techniques and tools to create clear and concise documentation that helps users understand and use products and services. This course can help you build the skills you need to become a Technical Writer by providing you with a hands-on introduction to PyTorch. You will learn how to build and train deep learning models, which can be used to automate technical writing tasks and improve the accuracy of technical writing results.
See salaries and explore the career path for Technical Writer
Project Manager
Project Managers plan, execute, and close projects. They use their knowledge of project management techniques and tools to ensure that projects are completed on time, within budget, and to the required quality standards. This course can help you build the skills you need to become a Project Manager by providing you with a hands-on introduction to PyTorch. You will learn how to build and train deep learning models, which can be used to automate project management tasks and improve the accuracy of project management results.
See salaries and explore the career path for Project Manager
Salesperson
Salespeople sell products and services to customers. They use their knowledge of sales techniques and tools to identify customer needs and develop solutions that meet those needs. This course can help you build the skills you need to become a Salesperson by providing you with a hands-on introduction to PyTorch. You will learn how to build and train deep learning models, which can be used to automate sales tasks and improve the accuracy of sales results.
See salaries and explore the career path for Salesperson
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products and services. They use their knowledge of marketing principles and techniques to create campaigns that reach target audiences and achieve marketing objectives. This course may be useful to you as a Marketing Manager by providing you with a hands-on introduction to PyTorch. You will learn how to build and train deep learning models, which can be used to automate marketing tasks and improve the accuracy of marketing results.
See salaries and explore the career path for Marketing Manager

Reading list

We've selected 50 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 Building Your First PyTorch Solution.
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Hands-On Machine Learning with Scikit-Learn, Keras,...
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Hands-On Machine Learning with Scikit-Learn, Keras,...
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Deep Learning
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The definitive mathematical canon of modern deep learning, establishing foundational theory across numerical computation, backpropagation optimization, autoencoders, and generative modeling.
Deep Learning (Adaptive Computation and Machine...
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Deep Learning (Adaptive Computation and Machine...
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Pattern Recognition and Machine Learning
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A timeless mathematical masterpiece that provides an uncompromising Bayesian and probabilistic foundation for linear classifiers, graphical models, kernel methods, and sampling algorithms.
Pattern Recognition and Machine Learning...
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Pattern Recognition and Machine Learning...
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Pattern Recognition and Machine Learning (text...
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Deep Learning with Python
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Authored by the creator of Keras, this foundational text blends conceptual clarity with hands-on Python implementations, teaching intuitive neural architecture design and gradient-based training.
Deep Learning with Python, Second Edition
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Deep Learning with Python
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(Español) Deep Learning con Python
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(Polski) Deep Learning Praca z jezykiem Python i biblioteka...
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(Deutsch) Deep Learning mit Python und Keras: Das Praxis...
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(Italiano) Deep Learning con Python: Imparare a implementare...
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The Elements of Statistical Learning
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The definitive mathematical and theoretical canon of statistical learning, establishing the rigorous analytical foundations of regularized regression, tree ensembles, and high-dimensional inference.
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Build a Large Language Model (from Scratch)
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Guides deep learning practitioners step-by-step through implementing transformer self-attention, token embeddings, and multi-layer perceptrons in pure PyTorch without relying on high-level wrappers.
Build a Large Language Model (from Scratch)
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Build a Large Language Model (from Scratch)
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Introduction to Linear Regression Analysis
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Provides a comprehensive and current introduction to the foundations of linear regression analysis. It is widely used as a textbook in academic institutions and is suitable for gaining a broad understanding. The latest edition includes new material on generalized regression techniques and updated examples.
Introduction to Linear Regression Analysis
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Machine Learning Engineering
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A distilled practitioner field guide outlining best practices for the entire machine learning lifecycle, from problem formulation and feature engineering to model deployment and monitoring.
Machine Learning Engineering
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Machine Learning Engineering
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Natural Language Processing with Transformers,...
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Provides an essential hands-on manual for fine-tuning pre-trained transformer representations, covering transfer learning workflows, tokenizer optimizations, and production pipeline deployment.
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Designing Machine Learning Systems
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Delivers an end-to-end systems architecture guide for building reliable production ML systems, addressing data engineering trade-offs, real-time feature computation, and model drift mitigation.
Designing Machine Learning Systems: An Iterative...
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Designing Machine Learning Systems
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Hands-On Large Language Models
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Illuminates modern generative neural architectures through celebrated visual diagrams and code workflows, covering contrastive embeddings, semantic search, and retrieval-augmented generation.
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Mathematics for Machine Learning
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Bridges linear algebra, multivariable calculus, vector geometry, and probability directly to fundamental machine learning algorithms like PCA, Gaussian mixture models, and support vector machines.
Mathematics for Machine Learning
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Deep Learning with PyTorch
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An updated edition of the popular 'Deep Learning with PyTorch', this book includes new content on transformers and generative AI models, reflecting contemporary advancements in the field. It builds upon the foundational knowledge of the first edition, making it valuable for those seeking to stay current with PyTorch and deep learning.
Deep Learning with PyTorch: Build, train, and tune...
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Deep Learning with PyTorch: Build, train, and tune...
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LLM Engineer's Handbook
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Examines production-grade engineering patterns for training and serving large language models, addressing distributed GPU training, synthetic data pipelines, and quantitative model evaluations.
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Applied Predictive Modeling
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Focuses on the practical aspects of building predictive models, including classification, with a strong emphasis on data preprocessing, model tuning, and evaluation. It's a valuable resource for understanding the end-to-end modeling process and is well-suited for practitioners and applied courses.
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Applied Predictive Modeling
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Dive into Deep Learning
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Introduction to Machine Learning with Python
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Deep Learning for Coders with fastai and PyTorch
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Takes a top-down approach, focusing on practical applications of deep learning using the fastai library, which is built on PyTorch. It quickly gets readers building models for computer vision, natural language processing, and tabular data, while also covering underlying concepts. It's highly recommended for those who want to get hands-on with PyTorch quickly and see it applied to real-world problems.
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Machine Learning with PyTorch and Scikit-Learn
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Regression and other stories
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Provides a practical and engaging introduction to regression analysis and other statistical methods, focusing on real-world data and research questions. It's suitable for a broad audience and emphasizes the importance of data visualization and model checking.
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Regression and other stories
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Generative Deep Learning
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Generative Deep Learning: Teaching Machines to...
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Generative Deep Learning: Teaching Machines To...
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Neural Networks Theory
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MP Applied Linear Regression Models-Revised Edition...
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A solid reference book that covers a wide range of theoretical aspects in regression analysis. While extensive, some users note the code examples might be in a less common dialect. It valuable resource for deepening understanding and is often used at the undergraduate and graduate levels.
Applied Linear Regression Models- 4th Edition with...
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Introduction to Machine Learning, second edition
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This textbook provides a solid introduction to the field of machine learning, covering various algorithms and concepts relevant to classification. The fourth edition includes recent advances in deep learning, making it a relevant resource for both foundational and contemporary topics.
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Introduction to Machine Learning, second edition
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LLM Systems in Production
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Applied Regression Analysis and Generalized Linear...
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Offers in-depth coverage of regression analysis and generalized linear models with a focus on applications, particularly in the social sciences. It balances theoretical concepts with practical examples and is suitable for both students and researchers. The latest edition includes new chapters on mixed-effects models.
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Machine Learning in Production
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Machine Learning in Production
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Regression Analysis by Example
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Emphasizes the practical application of regression analysis through numerous examples. It's an excellent resource for understanding how to perform regression and interpret the results, making it suitable for both broad understanding and practical application. The focus is on exploratory data analysis rather than just theory.
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DevOps for AI-Native Platforms
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The Art of Chunking in AI Systems
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Focuses on token segmentation, attention context preservation, and hierarchical chunking, giving deep learning engineers targeted techniques to maximize transformer retrieval accuracy.
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The Art of Chunking in AI Systems
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Local Models That Don't Feel Dumb
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Local Models That Don't Feel Dumb
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Neural Networks and Deep Learning
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This textbook provides a broad introduction to neural networks and deep learning, with significant coverage of their application to classification problems. It's a good resource for understanding the fundamentals of deep learning-based classification.
Neural Networks and Deep Learning
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Neural Networks and Deep Learning
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Natural Language Understanding with Python
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Provides a comprehensive guide to deep learning using the PyTorch framework. It covers a wide range of topics, including activation functions.
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Mastering PyTorch
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Delves into more advanced PyTorch techniques for building and deploying complex deep learning models, including CNNs, RNNs, transformers, and generative models. It covers topics like optimizing training with multiple GPUs and deploying models to production, making it suitable for those looking to deepen their understanding and apply PyTorch in a professional setting.
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Building Transformer Models with PyTorch 2.0
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Regression Modeling Strategies
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Focuses on developing a comprehensive strategy for regression modeling, covering various types of regression models and emphasizing the importance of model validation and interpretation. It's a valuable resource for practitioners and researchers building complex regression models.
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Boosting
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A seminal work on boosting, a key ensemble method widely used in classification. provides a deep dive into the theory and algorithms behind boosting, making it essential reading for those specializing in this area.
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Boosting
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Machine Learning with TensorFlow, Second Edition
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Provides a comprehensive guide to machine learning using the TensorFlow framework. It covers a wide range of topics, including activation functions.
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Deep Learning for Vision Systems
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PyTorch Computer Vision Cookbook
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PyTorch Computer Vision Cookbook
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Reinforcement Learning, second edition
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Machine Learning
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Machine Learning: ECML 2001: 12th European...
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Deep Learning for Natural Language Processing
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Connects computational linguistics theory to modern neural NLP architectures, demonstrating how vector embeddings, recurrent networks, and self-attention capture semantic nuances.
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Machine Learning Q and AI
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Bridges technical interview theory and practical research insights, answering 30 crucial conceptual questions about neural architectures, gradient dynamics, and model evaluation.
Machine Learning Q and AI: 30 Essential Questions...
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Machine Learning Q and AI: 30 Essential Questions...
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Ensemble Methods in Data Mining
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Focuses specifically on ensemble methods, which are powerful techniques for improving classification accuracy by combining multiple models. It's a good resource for those looking to deepen their understanding of these advanced classification strategies.
Ensemble Methods in Data Mining
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Ensemble Methods in Data Mining
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Generative AI with Python and PyTorch, Second...
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Focuses on building generative AI applications using Python and PyTorch. It covers modern topics like LLMs, Transformers, GANs, and diffusion models with hands-on projects. It's highly relevant for those interested in the latest advancements in generative AI and their implementation in PyTorch.
Generative AI with Python and PyTorch: Navigating...
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Generative Deep Learning
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The second edition of 'Generative Deep Learning' includes updates on the latest generative AI models and techniques. While it uses TensorFlow and Keras for some examples, the concepts are directly applicable to PyTorch, and the book provides a strong theoretical and practical foundation in this rapidly evolving area.
Generative Deep Learning: Teaching Machines To...
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Generative Deep Learning: Teaching Machines to...
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The Master Algorithm
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Time
2 hours
Via
Pluralsight
Instructor
Janani Ravi
This course belongs to a collection called Building Deep Learning Solutions with PyTorch. It's comprised of eight courses:
  1. Foundations of PyTorch
  2. Building Your First PyTorch Solution (viewing)
  3. Using PyTorch in the Cloud: PyTorch Playbook
  4. Image Classification with PyTorch
  5. Style Transfer with PyTorch
  6. Natural Language Processing with PyTorch
  7. Expediting Deep Learning with Transfer Learning: PyTorch Playbook
  8. Deploying PyTorch Models in Production: PyTorch Playbook
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Develops practical skills that are directly applicable in the field of deep learning, including building and debugging models and using optimizers
Emphasizes the practical aspects of PyTorch, enabling learners to quickly apply their knowledge to real-world projects
Covers the fundamentals of PyTorch, making it accessible to beginners in deep learning
Provides step-by-step guidance through building regression and classification models, offering a hands-on learning experience
Leverages industry-standard libraries and tools, ensuring that learners gain skills that are relevant to the field
Taught by experienced instructors, Janani Ravi, who brings expertise in the field of deep learning
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