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    • Pytorch

    PyTorch Courses Online

    Master PyTorch for deep learning. Learn to build and train neural networks using PyTorch for various AI applications.

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    Explore the PyTorch Course Catalog

    • Status: Free Trial
      Free Trial
      D

      DeepLearning.AI

      Probability & Statistics for Machine Learning & Data Science

      Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Probability Distribution, Probability, Statistical Inference, A/B Testing, Statistical Analysis, Statistical Machine Learning, Data Science, Exploratory Data Analysis, Statistical Visualization

      4.6
      Rating, 4.6 out of 5 stars
      ·
      565 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      I

      IBM

      Generative AI Engineering and Fine-Tuning Transformers

      Skills you'll gain: Large Language Modeling, Generative AI, Prompt Engineering, PyTorch (Machine Learning Library), Natural Language Processing, Applied Machine Learning, Performance Tuning, Application Frameworks

      4.6
      Rating, 4.6 out of 5 stars
      ·
      57 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      I

      IBM

      Introduction to Deep Learning & Neural Networks with Keras

      Skills you'll gain: Keras (Neural Network Library), Deep Learning, Unsupervised Learning, Artificial Neural Networks, PyTorch (Machine Learning Library), Tensorflow, Image Analysis, Computer Vision, Natural Language Processing, Machine Learning Algorithms

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.8K reviews

      Intermediate · Course · 1 - 3 Months

    • I

      IBM

      Introduction to Computer Vision and Image Processing

      Skills you'll gain: Computer Vision, Image Analysis, Artificial Neural Networks, IBM Cloud, Keras (Neural Network Library), Cloud Applications, Deep Learning, Tensorflow, Visualization (Computer Graphics), PyTorch (Machine Learning Library), Artificial Intelligence and Machine Learning (AI/ML), Computer Programming, Application Deployment, Augmented Reality, Machine Learning, Data Processing

      4.3
      Rating, 4.3 out of 5 stars
      ·
      1.4K reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      D

      DeepLearning.AI

      Convolutional Neural Networks in TensorFlow

      Skills you'll gain: Tensorflow, Keras (Neural Network Library), Computer Vision, Image Analysis, Applied Machine Learning, Deep Learning, Artificial Neural Networks, Supervised Learning, Data Processing

      4.7
      Rating, 4.7 out of 5 stars
      ·
      8.2K reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free
      Free
      C

      Coursera Project Network

      Fine Tune BERT for Text Classification with TensorFlow

      Skills you'll gain: Tensorflow, Keras (Neural Network Library), Natural Language Processing, Deep Learning, Data Processing, Data Pipelines

      4.6
      Rating, 4.6 out of 5 stars
      ·
      207 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • Status: Free Trial
      Free Trial
      D

      DeepLearning.AI

      Sequence Models

      Skills you'll gain: Natural Language Processing, Artificial Neural Networks, Tensorflow, Large Language Modeling, Artificial Intelligence and Machine Learning (AI/ML), PyTorch (Machine Learning Library), Deep Learning, Applied Machine Learning

      4.8
      Rating, 4.8 out of 5 stars
      ·
      31K reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      D

      DeepLearning.AI

      Natural Language Processing with Attention Models

      Skills you'll gain: Natural Language Processing, PyTorch (Machine Learning Library), Keras (Neural Network Library), Deep Learning, Tensorflow, Machine Learning Methods, Artificial Intelligence, Artificial Neural Networks, Text Mining, Data Processing

      4.4
      Rating, 4.4 out of 5 stars
      ·
      1.1K reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      A

      Arizona State University

      Introduction to Machine Learning with Python

      Skills you'll gain: Supervised Learning, Unsupervised Learning, Generative AI, Deep Learning, Image Analysis, Machine Learning Algorithms, Applied Machine Learning, Python Programming, Machine Learning, Artificial Neural Networks, Computer Vision, Computer Programming, Regression Analysis

      3.7
      Rating, 3.7 out of 5 stars
      ·
      20 reviews

      Beginner · Course · 1 - 4 Weeks

    • C

      Coursera Project Network

      Sentiment Analysis with Deep Learning using BERT

      Skills you'll gain: Text Mining, PyTorch (Machine Learning Library), Data Processing, Performance Tuning, Deep Learning, Natural Language Processing

      4.4
      Rating, 4.4 out of 5 stars
      ·
      396 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • Status: Free Trial
      Free Trial
      I

      Illinois Tech

      Statistical Learning

      Skills you'll gain: Statistical Analysis, Data Analysis, Statistical Methods, Statistical Machine Learning, Statistical Modeling, Regression Analysis, Bayesian Statistics, Supervised Learning, Statistical Inference, Machine Learning, Unsupervised Learning, Predictive Modeling, Classification And Regression Tree (CART), Feature Engineering, Artificial Neural Networks

      Build toward a degree

      Intermediate · Course · 1 - 3 Months

    • Status: Free
      Free
      C

      Coursera Project Network

      Tweet Emotion Recognition with TensorFlow

      Skills you'll gain: Tensorflow, Python Programming, Natural Language Processing, Artificial Neural Networks, Text Mining, Machine Learning Algorithms, Deep Learning, Data Processing

      4.5
      Rating, 4.5 out of 5 stars
      ·
      163 reviews

      Intermediate · Guided Project · Less Than 2 Hours

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    In summary, here are 10 of our most popular pytorch courses

    • Probability & Statistics for Machine Learning & Data Science: DeepLearning.AI
    • Generative AI Engineering and Fine-Tuning Transformers: IBM
    • Introduction to Deep Learning & Neural Networks with Keras: IBM
    • Introduction to Computer Vision and Image Processing: IBM
    • Convolutional Neural Networks in TensorFlow: DeepLearning.AI
    • Fine Tune BERT for Text Classification with TensorFlow: Coursera Project Network
    • Sequence Models: DeepLearning.AI
    • Natural Language Processing with Attention Models: DeepLearning.AI
    • Introduction to Machine Learning with Python: Arizona State University
    • Sentiment Analysis with Deep Learning using BERT: Coursera Project Network

    Frequently Asked Questions about Pytorch

    PyTorch is a deep learning framework that doubles as a fast, flexible deep learning research platform. This Python package is based on Torch, an open-source Lua-based machine learning package. It delivers tensor computation similar to NumPy, but with more powerful GPU acceleration. It also speeds up the process from prototyping to production. It was first introduced in 2017 by the Facebook Artificial Intelligence Research team, and it's become popular among amateurs and professionals alike, largely because of its intuitive approach and easy-to-understand modular process that makes it easier to build and experiment with deep learning architectures.‎

    If you're passionate about deep learning and want simple-to-understand code, PyTorch is a good framework to learn. It works with Python instead of creating friction. Additionally, it's simple to get started using with a long list of optimizers and easy techniques for loading data. If you're interested in creating applications like natural language processing, computer vision, or anything in the AI field, PyTorch is a strong contender. Because it offers a simple interface and powerful API, plus the ability to implement it on Windows and Linux, PyTorch provides higher productivity for developers.‎

    If you're an aspiring machine learning engineer, data scientist, computer engineer, data analyst, or computational chemist, gaining a solid foundation in PyTorch puts you on the path to your chosen career. Researchers rely heavily on PyTorch, using it to create dynamic graphs, and it has steadily become the chosen framework featured at machine learning conferences. Learning about PyTorch and becoming proficient in using it is useful for a variety of career options. It's also essential for students and anyone dabbling in machine learning on their own.‎

    Online courses provide a mix of classroom learning and hands-on experience to give you a firm grasp of the fundamentals like linear regression, artificial neural networks, and data science, while also honing your skills. Taking online courses on Coursera exposes you to a mix of PyTorch subjects ranging from beginner-level courses and Guided Projects to Professional Certificate programs provided by industry leaders like IBM. By opting for online PyTorch classes, you have greater flexibility to go at your own pace, making it easier to fit your courses around your busy life.‎

    Online PyTorch courses offer a convenient and flexible way to enhance your knowledge or learn new PyTorch skills. Choose from a wide range of PyTorch courses offered by top universities and industry leaders tailored to various skill levels.‎

    When looking to enhance your workforce's skills in PyTorch, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

    This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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