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Janani Ravi
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

Reading list

We haven't picked any books for this reading list yet.
Focuses on the exciting field of generative AI using deep learning, with examples often implemented using PyTorch. It covers models like GANs, VAEs, and Transformers, which are highly relevant contemporary topics. While not exclusively a PyTorch book, it's valuable for those interested in applying PyTorch to create new content.
Provides a hands-on introduction to PyTorch, focusing on practical examples and applications. It good starting point for beginners who want to learn how to use PyTorch.
Helps readers get up to speed with PyTorch for building neural networks. It covers setting up environments, creating neural architectures for various data types (images, sound, text), transfer learning, and debugging. It also touches upon deploying models to production, making it relevant for those looking to move beyond theoretical understanding.
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.
This concise reference provides quick access to PyTorch syntax, design patterns, and code examples. It's a useful tool for developers and researchers who need to quickly look up how to perform specific tasks in PyTorch, from basic operations to model deployment. It's more of a reference than a comprehensive learning resource.
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.
This comprehensive book provides a solid theoretical and practical introduction to deep learning, with implementations in multiple frameworks, including PyTorch. It covers a wide range of topics from the basics to more advanced concepts and is suitable for those who want a deep understanding of the underlying principles of deep learning alongside practical PyTorch code.
Specifically written for beginners, this book introduces the fundamentals of PyTorch step-by-step. It covers essential concepts like autograd, model classes, and data handling. This is an excellent resource for those with no prior experience in PyTorch or deep learning, providing a gentle introduction with practical code examples.
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.
While not specifically a PyTorch book, this foundational classic in the field of deep learning. It provides a comprehensive theoretical background on neural networks and deep learning concepts. It's essential for anyone seeking a deep understanding of the principles behind PyTorch and deep learning in general, serving as a valuable reference for advanced students and researchers.
This online book provides a clear and intuitive introduction to the foundational concepts of neural networks and deep learning. While it doesn't use PyTorch, the fundamental knowledge gained from this resource is highly relevant and serves as excellent prerequisite material for understanding how PyTorch works at a deeper level. It's a widely recommended resource for beginners in the field.
Offers a practical perspective on applying deep learning, covering various architectures and workflows. While it may not exclusively focus on PyTorch, it provides valuable insights into real-world deep learning problems and solutions that can be implemented using PyTorch. It's suitable for practitioners looking to bridge the gap between theory and application.
Similar to the NLP book, this resource provides practical recipes and solutions for computer vision problems using PyTorch. It covers tasks like image classification, object detection, and segmentation with clear code examples. It's a go-to guide for anyone applying PyTorch to computer vision.
Explores the field of reinforcement learning and its implementation using PyTorch. It covers various RL algorithms and provides practical examples, making it suitable for those interested in this advanced application area of deep learning with PyTorch.
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.
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.
Authored by the creator of Keras, this book provides a conceptual introduction to deep learning using Python. While it primarily uses TensorFlow/Keras, the explanations of deep learning concepts are framework-agnostic and highly valuable for building a strong theoretical understanding before diving into PyTorch specifics. It's considered a modern classic for its clear explanations.
Takes a unique approach to explaining deep learning concepts from first principles, building neural networks from scratch using Python and NumPy. While it doesn't use PyTorch, it provides an intuitive understanding of how deep learning works, which can significantly aid in grasping PyTorch's functionalities. It's excellent for building foundational knowledge.
Focuses on applying deep learning techniques using PyTorch to solve various problems. It provides practical examples and guidance on building and training models for different applications, making it a useful resource for those looking to gain hands-on experience with PyTorch.

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