PyTorch is a popular open-source machine learning framework for building deep neural networks. With its intuitive syntax and efficient computations, it has become a go-to choice for many data scientists and researchers. As with any complex technology, learning PyTorch can be challenging, especially without proper guidance. Fortunately, there are numerous online courses available that provide comprehensive training, covering everything from basic concepts to advanced techniques. In this article, we will explore some of the best PyTorch courses available online, highlighting their features and benefits to help readers make an informed decision.
Here’s a look at the Best Pytorch Courses and Certifications Online and what they have to offer for you!
10 Best Pytorch Courses and Certifications Online
- 10 Best Pytorch Courses and Certifications Online
- 1. PyTorch for Deep Learning with Python Bootcamp by Jose Portilla (Udemy) (Our Best Pick)
- 2. PyTorch for Deep Learning and Computer Vision by Rayan Slim, Jad Slim, Amer Sharaf, Sarmad Tanveer (Udemy)
- 3. Practical Deep Learning with PyTorch by Deep Learning Wizard (Udemy)
- 4. PyTorch: Deep Learning and Artificial Intelligence by Lazy Programmer Team, Lazy Programmer Inc. (Udemy)
- 5. Deep Learning with PyTorch for Medical Image Analysis by Jose Portilla, Marcel Früh, Sergios Gatidis, Tobias Hepp (Udemy)
- 6. Reinforcement Learning with Pytorch by Atamai AI Team (Udemy)
- 7. PyTorch Tutorial – Neural Networks & Deep Learning in Python by Minerva Singh (Udemy)
- 8. Deep Learning with PyTorch by Packt Publishing (Udemy)
- 9. Federated Learning by Mohamed Gharibi (Udemy)
- 10. Hands-On Natural Language Processing with Pytorch by Packt Publishing (Udemy)
1. PyTorch for Deep Learning with Python Bootcamp by Jose Portilla (Udemy) (Our Best Pick)
The PyTorch for Deep Learning with Python Bootcamp is an online course that focuses on teaching students how to create advanced neural networks for deep learning using Facebook’s PyTorch library. PyTorch is an open source platform that allows for seamless transition from research to deployment, and is known for its deep integration with Python. This course utilizes practical hands-on projects and exercises to balance important theory concepts.
Students will have access to carefully laid out notebooks and slides, which explain concepts in an easily understandable way, including code and explanations side by side. The course covers a wide range of topics including NumPy, Pandas, machine learning theory, test/train/validation data splits, model evaluation, unsupervised learning tasks, tensors with PyTorch, neural network theory, perceptrons, networks, activation functions, cost/loss functions, backpropagation, gradients, artificial neural networks, convolutional neural networks, recurrent neural networks, and more.
Upon completion of the course, students will be able to create deep learning models to solve their own problems using their own data sets. The course is taught by Jose Portilla and is broken down into several sections including course overview and setup, NumPy and Pandas crash courses, PyTorch basics, artificial neural networks, convolutional neural networks, recurrent neural networks, using a GPU with PyTorch and CUDA, NLP with PyTorch, and a bonus section.
Overall, the PyTorch for Deep Learning with Python Bootcamp is an excellent online course that provides a comprehensive introduction to one of the most popular deep learning frameworks for Python.
2. PyTorch for Deep Learning and Computer Vision by Rayan Slim, Jad Slim, Amer Sharaf, Sarmad Tanveer (Udemy)
The PyTorch for Deep Learning and Computer Vision course is designed to teach students how to build highly sophisticated Deep Learning and Computer Vision applications with PyTorch. Taught by experienced instructors Rayan Slim, Jad Slim, Amer Sharaf, and Sarmad Tanveer, this course aims to take students from beginner to expert level.
PyTorch is known for its flexibility and ease of use when building Deep Learning models, and has revolutionized the field of deep learning since its release. With Deep Learning jobs commanding some of the highest salaries in the development world, this course offers a valuable opportunity to learn and master this technology in a fun and exciting way.
Through a “learn by doing” approach, students will work through various projects and build state-of-the-art models in Computer Vision and Deep Learning. By the end of the course, students will have the necessary skills to impress even the most senior developers.
This course covers topics such as working with the tensor data structure, implementing Machine and Deep Learning applications with PyTorch, building neural networks from scratch, and solving complex problems in Computer Vision. Students will also learn how to harness highly sophisticated pre-trained models and use style transfer to build sophisticated AI applications.
No programming or mathematics experience is required, as the course is designed for all skill levels. All course materials and source code are provided, and friendly support is available in the Q&A area. This course is ideal for anyone with an interest in Deep Learning and Computer Vision, as well as entrepreneurs looking to work with cutting-edge technologies.
The Practical Deep Learning with PyTorch course is designed to provide a comprehensive understanding of the fundamentals of deep learning with a python-first framework. The course emphasizes the importance of deep learning in various applications such as facial recognition, self-driving cars, and medical diagnostics. It aims to strike a balance between the practical and mathematical aspects of deep learning, making it accessible to anyone regardless of their background.
The entire course is delivered in Python Notebook to allow learners to follow along with the videos and replicate the results. This approach enables learners to practice and tweak the models until they understand every line of code. The course also takes a gradual learning approach, carefully linking every topic to the previous one to ensure a proper understanding of the models.
To make the content more digestible, over 100 custom-made diagrams are used in the course. These diagrams are designed to help learners visualize the transition from one model to another and understand the models comprehensively. Moreover, the course provides learners with access to a mentor who can guide them through the basics to advanced theories and answer any questions they may have.
While there are some mathematics involved, the course focuses more on getting learners to understand how everything works first, with the aim of making it easier for them to catch up on the mathematics later on. The course is compatible with PyTorch 0.4 and 1.0, with very few changes from PyTorch 0.3.
4. PyTorch: Deep Learning and Artificial Intelligence by Lazy Programmer Team, Lazy Programmer Inc. (Udemy)
PyTorch: Deep Learning and Artificial Intelligence course focuses on neural networks for computer vision, time series forecasting, natural language processing (NLP), generative adversarial networks (GANs), reinforcement learning, and more. PyTorch has gained popularity among professionals and researchers due to its ease of building and testing new ideas, community support, and backing by Facebook AI Research Lab (FAIR). Top AI shops like OpenAI, Apple, and JPMorgan Chase use PyTorch. The course is designed for beginner-level to expert-level students and covers all major deep learning architectures like deep neural networks, convolutional neural networks, and recurrent neural networks. The course includes in-depth sections for theory and focuses more on the PyTorch library rather than deriving mathematical equations.
The course covers recent achievements in deep learning like photo-realistic images of non-existent objects (GANs), beating world champions in strategy games (deep reinforcement learning), self-driving cars (computer vision), speech recognition (Siri), and machine translation (NLP). The course includes new projects like time series forecasting and stock predictions. The instructor has taken an approach that students can still do the course even if they are not comfortable with mathematical concepts. The course is designed for students who want to learn fast, but there are also in-depth sections for those who want to dig deeper.
The course includes 16 sections covering introduction, getting set up, Google Colab, machine learning and neurons, feedforward artificial neural networks, convolutional neural networks, recurrent neural networks, time series, and sequence data, NLP, recommender systems, transfer learning for computer vision, GANs, deep reinforcement learning (theory), stock trading project with deep reinforcement learning, VIP: uncertainty estimation, VIP: facial recognition, in-depth sections for loss functions and gradient descent, and extras like setting up environment, extra help with Python coding for beginners, and effective learning strategies for machine learning.
5. Deep Learning with PyTorch for Medical Image Analysis by Jose Portilla, Marcel Früh, Sergios Gatidis, Tobias Hepp (Udemy)
The Deep Learning with PyTorch for Medical Image Analysis course led by Jose Portilla, Marcel Früh, Sergios Gatidis, and Tobias Hepp covers the application of state of the art Deep Learning architectures to various medical imaging challenges. The course includes lessons on topics such as Machine Learning Theory, Convolutional Neural Networks, and Medical Imaging, and a state of the art high-level pytorch library called pytorch-lightning. Students will learn skills and techniques that the vast majority of AI engineers do not have.
The course provides unique knowledge on the application of deep learning to highly complex and non-standard medical problems in 2D and 3D. All lessons include clearly summarized theory and code-along examples to help students understand and follow every step. Additionally, there is a powerful online community with QA Forums with thousands of students and dedicated Teaching Assistants, as well as student interaction on the Discord Server.
The course is divided into multiple sections, including Introduction, Crash Course: NumPy, Machine Learning Concepts Overview, PyTorch Basics, CNN – Convolutional Neural Networks, Medical Imaging – A short Introduction, Data Formats in Medical Imaging, Pneumonia-Classification, Cardiac-Detection, Atrium-Segmentation, Capstone-Project: Lung Tumor Segmentation, 3D Liver and Liver Tumor Segmentation, and a BONUS SECTION: THANK YOU!. The course covers various tasks such as cancer segmentation, pneumonia classification, cardiac detection, and interpretability.
The Reinforcement Learning with Pytorch course is designed to teach individuals how to apply Reinforcement Learning and Artificial Intelligence algorithms using Python, Pytorch, and OpenAI Gym. The course aims to cover various topics, focusing on essential and practical details. The course begins with basic information, gradually building the understanding of the learner, and finally reaching the point where the learner can make an agent learn in a human-like way, only from video input.
The course will use environments from OpenAI Gym to evaluate algorithms, starting from basic text games, through more complex ones, up to challenging Atari games. The course will cover several topics, ranging from Reinforcement Learning Introduction, Markov Decision Process, Deterministic and Stochastic Environments, to Q Learning, Exploration vs Exploitation, Scaling up, Neural Networks as Function Approximators, Deep Reinforcement Learning, DQN, Improvements to DQN, Learning from video input, Reproducing some of the most popular RL solutions, Tuning Parameters, and General Recommendations.
The course will have six sections, which include the welcome section, introduction, tabular methods, scaling up, DQN, DQN improvements, DQN with video output, and final notes. The goal of the course is to understand why and how the Reinforcement Learning algorithms work, with a significant focus on the practical part. The course is updated to work with Pytorch 1.6, and learners will find all the installation instructions and code on the course website.
The PyTorch Tutorial – Neural Networks & Deep Learning in Python is a comprehensive 5-hour+ course that teaches basic machine learning, neural networks, and deep learning using the PyTorch framework. The course covers all aspects of PyTorch and is an all-in-one guide for practical machine & deep learning using the PyTorch framework in Python. The course is designed to provide a robust grounding in all aspects of data science within the PyTorch framework, unlike other resources that demonstrate how to use PyTorch on in-built datasets. The course consists of 7 sections that address every aspect of PyTorch, including a full introduction to Python Data Science and powerful Python-driven framework for data science, Anaconda.
The course instructor, Minerva Singh, is an Oxford University MPhil graduate and has several years of experience analyzing real-life data from different sources using data science techniques. The course is unique in that it provides a comprehensive understanding of the multidimensional nature of data science within the PyTorch framework. The course is practical and hands-on, with a focus on implementing different techniques on real data and interpreting the results. The problems addressed in the course include identifying credit card fraud and classifying images of different fruits.
The course consists of easy-to-understand, hands-on methods to simplify even the most difficult concepts. The course will help students implement the methods using real data obtained from different sources, empowering them to implement Python-based data science in real-life. The course covers important data science packages such as Pandas and Numpy and introduces learners to deep learning models such as Convolution Neural Network (CNN).
The course is suitable for individuals who want to gain proficiency in PyTorch and give their company a competitive edge. The course is divided into 7 sections, and after each video, learners will learn a new concept or technique that they may apply to their own projects.
Packt Publishing offers a video course, “Deep Learning with PyTorch,” which provides an introduction to PyTorch, a dynamic deep learning framework written in Python, and its ease of use compared to other libraries. The course covers various tasks using Convolutional Neural Networks and Recurrent Neural Networks for processing spatial and sequential data, respectively. Additionally, learners will explore how to use unlabeled data with Auto Encoders and train a neural network for Reinforcement Learning. Throughout the course, learners will implement different mechanisms of the PyTorch framework, ultimately gaining a good understanding of the algorithms and techniques used.
By the end of the course, learners will have a good grasp of how PyTorch works and how it can be used to solve daily machine learning problems. The course uses Python 3.6 and PyTorch 0.3, making it relevant and informative content for legacy users of these technologies.
The course instructor, Anand Saha, is a software professional with 15 years’ experience in developing enterprise products and services, with a particular interest in Deep Learning. His work in Deep Learning includes building pipelines to detect and count endangered species from aerial images, training a robotic arm to pick and place objects, and implementing NIPS papers.
The course is divided into several sections, including Getting Started With PyTorch, Training Your First Neural Network, Computer Vision for Digits Recognition, Sequence Models for Text Generation, Autoencoder for Denoising Images, and Reinforcement Learning for Balancing a Cartpole Using DQN.
The Federated Learning course instructed by Mohamed Gharibi provides a comprehensive understanding of the concepts in Neural Networks and their implementation using PyTorch. The course also covers Federated Learning architecture, including loading the dataset on the devices in IID, non-IID, and unbalanced settings, followed by a tutorial on PySyft to show how to send and receive models and datasets between clients and the server.
The course objectives include teaching Federated Learning techniques by implementing them line by line, such as FedAvg, FedSGD, FedProx, and FedDANE. The course also covers Differential Privacy and how to add it to Federated Learning, followed by implementing FedAvg using DP. The course shows how to implement FL techniques both locally and on the cloud, and for the cloud setting, Google Cloud Platform is used to create and configure all the instances used in the experiments.
The course is organized into multiple sections, including Introduction, Introduction to PyTorch, Setting the Environment, Federated Learning Techniques, Federated Learning with Differential Privacy, and Federated Learning on Cloud. By the end of the course, students will be able to implement different Federated Learning techniques, build their own optimizer and technique, and run experiments both locally and on the cloud.
The Hands-On Natural Language Processing with Pytorch course offered by Packt Publishing aims to provide learners with the necessary skills to perform complex natural language processing (NLP) tasks and develop intelligent language applications using deep learning with PyTorch. Throughout the course, learners will build two complete real-world NLP applications, including a Sentiment Analyzer and an advanced Neural Translation Machine.
The Sentiment Analyzer is designed to analyze data and determine whether a review is positive or negative towards a particular movie, while the Neural Translation Machine is a speech translation engine that utilizes Sequence to Sequence models and PyTorch’s speed and flexibility to translate given text into different languages. By the end of the course, learners will be equipped with the skills required to develop their own real-world NLP models using PyTorch’s deep learning capabilities.
The course uses Python 3.6, PyTorch 1.0, NLTK 3.3.0, and Spacy 2.0. Although these may not be the most recent versions available, the course provides relevant and informative content for legacy users of PyTorch.
The course instructor, Jibin Mathew, is a tech-entrepreneur, artificial intelligence enthusiast, and active researcher with extensive experience as a Software Solutions Architect. He has worked with various solutions in artificial intelligence, including computer vision, natural language processing/understanding, and data sciences, pushing the limits of computational performance and model accuracies. Additionally, Mathew serves as a consultant for clients from retail, environment, finance, and healthcare sectors.
The course is structured into several sections, including Up and Running with PyTorch, Data Cleaning and Preprocessing for Sentiment Analysis, Implement Word Embeddings with gensim, Train RNNs and LSTMs Units for Sentiment Analysis, and Build a Neural Machine Translator. The final section, Improve the Neural Machine Translation with Attention Networks, focuses on improving the Neural Machine Translation application by introducing attention networks.