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Mat Leonard,
Miguel Morales,
Chhavi Yadav,
Dana Sheahan,
Cezanne Camacho,
Alexis Cook,
Arpan Chakraborty,
Luis Serrano,
and
Juan Delgado
Register for this course and see more details by visiting:
OpenCourser.com/course/i7np59/neural
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What's inside
Syllabus
Review the basics of neural networks.
Review the basics of convolutional neural networks.
Learn how to use PyTorch for building deep learning models.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores essential deep learning concepts
Taught by highly recognized instructors in the field
Develops foundational skills for advanced deep learning
Save this course
Save Neural Networks in PyTorch to your list so you can find it easily later:
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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 Neural Networks in PyTorch with these
activities:
Review your notes and assignments from previous courses related to neural networks
Show steps
Refresh your foundational knowledge of neural networks.
Browse courses on
Neural Networks
Show steps
-
Gather your notes and assignments from previous courses that covered neural networks.
-
Review the materials, focusing on key concepts and techniques.
-
Take notes or create summaries to reinforce your understanding.
Gather a collection of resources on convolutional neural networks
Show steps
Build a valuable resource for yourself and others, while organizing and expanding your knowledge of convolutional neural networks.
Browse courses on
Convolutional Neural Networks
Show steps
-
Search for and identify reputable sources of information on convolutional neural networks, including research papers, articles, and tutorials.
-
Gather and organize the resources in a systematic way, creating a comprehensive collection.
-
Consider sharing your compilation with others through a public repository or platform.
Follow along with online tutorials on advanced topics in deep learning
Show steps
Expand your knowledge and explore advanced techniques in deep learning and convolutional neural networks.
Browse courses on
Deep Learning
Show steps
-
Identify reputable online platforms or resources that offer tutorials on advanced deep learning topics.
-
Select tutorials that align with your interests and learning goals.
-
Follow along with the tutorials, taking notes and completing any exercises or challenges provided.
-
Experiment with the techniques covered in the tutorials to reinforce your understanding.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve coding challenges on convolutional neural networks
Show steps
Reinforce your understanding of convolutional neural networks and improve your coding skills.
Browse courses on
Convolutional Neural Networks
Show steps
-
Find coding challenges websites or platforms specifically focused on convolutional neural networks.
-
Select challenges that align with your current level of understanding and gradually increase the difficulty.
-
Attempt to solve the challenges on your own, referring to documentation or tutorials as needed.
-
Review your solutions, identify areas for improvement, and make necessary adjustments.
Build a custom neural network model using PyTorch
Show steps
Develop a strong understanding of the practical implementation of neural networks and PyTorch.
Browse courses on
PyTorch
Show steps
-
Design and plan the architecture of your neural network model.
-
Implement the model using PyTorch, including layers, activation functions, and loss functions.
-
Train the model on a relevant dataset, monitoring its performance and adjusting hyperparameters as needed.
-
Optimize the model for efficiency and accuracy by exploring different architectures and training techniques.
-
Present and document your model, including its performance metrics and insights gained.
Create a detailed blog post or video tutorial on a specific aspect of convolutional neural networks
Show steps
Enhance your understanding of a specific topic related to convolutional neural networks while developing your communication skills.
Browse courses on
Convolutional Neural Networks
Show steps
-
Research and select a specific aspect of convolutional neural networks that you want to cover.
-
Gather information, examples, and case studies to support your content.
-
Write or record your blog post or video tutorial, ensuring clarity, accuracy, and engagement.
-
Publish and share your content with others, including relevant online communities or platforms.
Build a small-scale machine learning project using a convolutional neural network
Show steps
Practical application of convolutional neural networks in a real-world scenario.
Browse courses on
Machine Learning
Show steps
-
Define a small-scale machine learning problem that can be solved using a convolutional neural network.
-
Gather and prepare a dataset appropriate for your chosen problem.
-
Design and implement a convolutional neural network model to solve the problem.
-
Train and evaluate your model, adjusting hyperparameters and techniques as needed.
-
Deploy and test your model on a real-world dataset.
Review your notes and assignments from previous courses related to neural networks
Show steps
Refresh your foundational knowledge of neural networks.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Gather your notes and assignments from previous courses that covered neural networks.
- Review the materials, focusing on key concepts and techniques.
- Take notes or create summaries to reinforce your understanding.
Gather a collection of resources on convolutional neural networks
Show steps
Build a valuable resource for yourself and others, while organizing and expanding your knowledge of convolutional neural networks.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Search for and identify reputable sources of information on convolutional neural networks, including research papers, articles, and tutorials.
- Gather and organize the resources in a systematic way, creating a comprehensive collection.
- Consider sharing your compilation with others through a public repository or platform.
Follow along with online tutorials on advanced topics in deep learning
Show steps
Expand your knowledge and explore advanced techniques in deep learning and convolutional neural networks.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify reputable online platforms or resources that offer tutorials on advanced deep learning topics.
- Select tutorials that align with your interests and learning goals.
- Follow along with the tutorials, taking notes and completing any exercises or challenges provided.
- Experiment with the techniques covered in the tutorials to reinforce your understanding.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve coding challenges on convolutional neural networks
Show steps
Reinforce your understanding of convolutional neural networks and improve your coding skills.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Find coding challenges websites or platforms specifically focused on convolutional neural networks.
- Select challenges that align with your current level of understanding and gradually increase the difficulty.
- Attempt to solve the challenges on your own, referring to documentation or tutorials as needed.
- Review your solutions, identify areas for improvement, and make necessary adjustments.
Build a custom neural network model using PyTorch
Show steps
Develop a strong understanding of the practical implementation of neural networks and PyTorch.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Design and plan the architecture of your neural network model.
- Implement the model using PyTorch, including layers, activation functions, and loss functions.
- Train the model on a relevant dataset, monitoring its performance and adjusting hyperparameters as needed.
- Optimize the model for efficiency and accuracy by exploring different architectures and training techniques.
- Present and document your model, including its performance metrics and insights gained.
Create a detailed blog post or video tutorial on a specific aspect of convolutional neural networks
Show steps
Enhance your understanding of a specific topic related to convolutional neural networks while developing your communication skills.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Research and select a specific aspect of convolutional neural networks that you want to cover.
- Gather information, examples, and case studies to support your content.
- Write or record your blog post or video tutorial, ensuring clarity, accuracy, and engagement.
- Publish and share your content with others, including relevant online communities or platforms.
Build a small-scale machine learning project using a convolutional neural network
Show steps
Practical application of convolutional neural networks in a real-world scenario.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Define a small-scale machine learning problem that can be solved using a convolutional neural network.
- Gather and prepare a dataset appropriate for your chosen problem.
- Design and implement a convolutional neural network model to solve the problem.
- Train and evaluate your model, adjusting hyperparameters and techniques as needed.
- Deploy and test your model on a real-world dataset.
Career center
Learners who complete Neural Networks in PyTorch will develop knowledge and skills
that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers specialize in building, deploying, and maintaining deep learning models. They use neural networks to solve complex problems, such as image recognition, natural language processing, and speech recognition. This course provides a deep dive into neural networks using PyTorch, which is essential for Deep Learning Engineers.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning models. They use neural networks to solve complex problems, such as image recognition and natural language processing. This course provides hands-on experience with building deep learning models using PyTorch, which is a valuable skill for Machine Learning Engineers.
Data Scientist
Data Scientist
Data Scientists design and execute data analysis processes to extract meaningful insights and actionable information from data. They build and train models, using their expertise in machine learning and neural networks. This course provides a strong foundation in neural networks and PyTorch, which are key technologies for building complex models and driving today's Data Science applications.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop systems that can interpret and understand images and videos. They use neural networks to solve problems such as object detection, image classification, and facial recognition. This course provides a strong foundation in neural networks and convolutional neural networks, which are key technologies for building Computer Vision applications.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers design and develop systems that can understand and generate human language. They use neural networks to solve problems such as text classification, sentiment analysis, and machine translation. This course provides a foundation in neural networks and deep learning, which are essential for building Natural Language Processing applications.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use neural networks to solve problems such as fraud detection, recommendation systems, and anomaly detection. This course provides a solid foundation in neural networks and PyTorch, which are valuable skills for Software Engineers.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They use neural networks to solve problems such as risk management, portfolio optimization, and algorithmic trading. This course provides a solid foundation in neural networks and PyTorch, which can be beneficial for Quantitative Analysts.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use neural networks to solve problems such as predictive analytics, customer segmentation, and risk assessment. This course provides an introduction to neural networks and PyTorch, which can be helpful for Data Analysts wanting to expand their skillset.
Business Analyst
Business Analyst
Business Analysts analyze business processes and identify opportunities for improvement. They use neural networks to solve problems such as customer segmentation, churn prediction, and fraud detection. This course provides an introduction to neural networks and PyTorch, which can be helpful for Business Analysts wanting to incorporate AI techniques into their work.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They use neural networks to solve problems such as demand forecasting, customer segmentation, and product optimization. This course provides an overview of neural networks and PyTorch, which can be beneficial for Product Managers wanting to understand the potential of AI for their products.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data and make recommendations to investors. They use neural networks to solve problems such as stock prediction, portfolio optimization, and risk assessment. This course provides an overview of neural networks and PyTorch, which can be beneficial for Financial Analysts wanting to enhance their analytical capabilities.
Marketer
Marketer
Marketers develop and execute marketing campaigns to reach and engage customers. They use neural networks to solve problems such as customer segmentation, targeted advertising, and personalized recommendations. This course provides an introduction to neural networks and PyTorch, which can be helpful for Marketers wanting to leverage AI in their marketing efforts.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to draw meaningful conclusions. They use neural networks to solve problems such as predictive analytics, customer segmentation, and risk assessment. This course provides an overview of neural networks and PyTorch, which can be beneficial for Statisticians wanting to expand their skillset.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex problems in various industries. They use neural networks to solve problems such as supply chain optimization, logistics planning, and inventory management. This course provides an introduction to neural networks and PyTorch, which can be helpful for Operations Research Analysts wanting to explore AI techniques.
Academic Researcher
Academic Researcher
Academic Researchers conduct research in various fields, including science, engineering, and social sciences. They use neural networks to solve problems such as image recognition, natural language processing, and speech recognition. This course provides a solid foundation in neural networks and PyTorch, which can be beneficial for Academic Researchers wanting to incorporate AI into their research.
For more career information including salaries, visit:
OpenCourser.com/course/i7np59/neural
Reading list
We've selected nine 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
Neural Networks in PyTorch.
This comprehensive guide complements the course by providing a solid foundation in machine learning concepts, techniques, and tools. It covers essential building blocks for constructing and evaluating deep learning models in PyTorch.
As a more advanced reference, this book complements the course by providing in-depth coverage of PyTorch and deep learning techniques. It offers detailed explanations and code examples for building and deploying complex neural network models.
This practical guide complements the course by providing a comprehensive overview of deep learning using Keras, a high-level API for TensorFlow. It covers essential concepts, architectures, and best practices.
This visual guide complements the course by providing a clear and intuitive explanation of deep learning concepts. Its use of diagrams and illustrations makes it easier for learners to understand complex algorithms and architectures.
For learners seeking a theoretical foundation, this book provides a rigorous treatment of machine learning algorithms. It complements the course by offering a mathematical and statistical perspective on neural networks and deep learning.
For learners with a strong mathematical background, this book provides a comprehensive treatment of the mathematical foundations of machine learning. It complements the course by offering a deeper understanding of the underlying algorithms and techniques.
This introductory book provides a strong foundation in Python programming, which is essential for understanding and implementing neural networks in PyTorch. It covers core concepts, data structures, and algorithms.
This practical guide provides a comprehensive overview of machine learning in Python. It complements the course by covering a wide range of topics, including data preprocessing, feature engineering, and model evaluation.
While the course focuses on PyTorch, this book offers insights into machine learning using R, a popular programming language in data science. It provides a comparative perspective and expands learners' knowledge of different programming environments.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/i7np59/neural
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Time
330 hours
Level
Advanced
Via
Udacity
Instructors
Mat Leonard
Miguel Morales
Chhavi Yadav
Dana Sheahan
Cezanne Camacho
Alexis Cook
Arpan Chakraborty
Luis Serrano
Juan Delgado
Language
English
Good to know
Explores essential deep learning concepts
Taught by highly recognized instructors in the field
Develops foundational skills for advanced deep learning
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