Welcome to the world of PyTorch - a deep learning framework that has changed and re-imagined the way we build deep learning models.
PyTorch was recently voted as the favorite deep learning framework among researchers. It has left TensorFlow behind and continues to be the deep learning framework of choice for many experts and practitioners.
PyTorch is super flexible and is quite easy to grasp, even for deep learning beginners. If you work on deep learning and computer vision projects, you’ll love working with PyTorch.
- What is PyTorch?
- Why should you learn PyTorch?
- How does PyTorch work?
- How can you install PyTorch?
- PyTorch vs. TensorFlow vs. Keras - which deep learning framework is the best?
- What are the advantages of using PyTorch?
- What are some challenges you might face when using PyTorch?
- What kind of deep learning projects can you solve using PyTorch?
- Is PyTorch relevant in the industry?
- Do you need to know deep learning to learn PyTorch?
- What kind of neural networks can you build using PyTorch?
- Which programming language works best with PyTorch?
- Getting Started with PyTorch
- Why should we use PyTorch?
- A word from the creators of PyTorch
- Tensors in PyTorch
- Mathematical Operations in PyTorch(vs. NumPy)
- Matrix Operations in PyTorch(vs. NumPy)
- Tensor Operations
- AI&ML Blackbelt Plus Program (Sponsored)
- Getting started with Neural Networks
- Exercise : Getting started with Neural Networks
- Independent and Dependent Variables
- Understanding Forward Propagation
- Exercise : Forward Propagation
- Error and Reason for Error
- Exercise : Error and Reason for Error
- Gradient Descent Intuition
- Understanding Math Behind Gradient Descent
- Exercise : Gradient Descent
- Back Propagation
- Exercise : Back Propagation
- Summary of the Module
- Modules in PyTorch - Autograd
- Modules in PyTorch: Optim
- Modules in PyTorch: nn
- Implementing a Neural Network from Scratch
- Case Study – Solving an Image Recognition problem in PyTorch
- Other Use cases for Deep Learning in PyTorch
- What Next?
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What is PyTorch?
PyTorch is a Python-based library that provides maximum flexibility and speed. We’ve found PyTorch to be as simple as working with NumPy! You’ll figure this out inside the course for yourself.
We highly recommend learning PyTorch right now - it is quickly becoming the framework of choice for deep learning practitioners. PyTorch will be adopted by the industry soon as well so get on board today!
Why should you learn PyTorch?
PyTorch is one of the most popular and upcoming deep learning frameworks that allows you to build complex neural networks. It is rapidly growing among the research community and companies like Facebook and Uber are using it as well.
Here is what Andrej Karpathy, head of AI at Tesla, has to say about PyTorch - “I've been using PyTorch a few months now and I've never felt better. I have more energy. My skin is clearer. My eyesight has improved.”
PyTorch vs. TensorFlow vs. Keras - which deep learning framework is the best?
This is a common question and a relevant one. There is no shortage of deep learning frameworks out there so which one should you choose?
There’s no one-size-fits-all approach here. Each deep learning framework has its own unique set of features which is why data scientists go for one over the other.
We recommend checking out this article on Analytics Vidhya to understand how PyTorch compares against the other deep learning frameworks like TensorFlow and Keras. This will help you settle the PyTorch vs. TensorFlow debate for sure!
What are the advantages of using PyTorch?
The main advantage of PyTorch is the imperative programming feature - which performs computations on your code as you are typing it, so debugging it is super easy! It is also much faster than NumPy and easy to learn too.
What are some challenges you might face when using PyTorch?
PyTorch is not meant to be an end-to-end deep learning framework and using it for production in the industry remains a challenge. It is also relatively newer than other deep learning frameworks and has only recently released its stable versions.
What kind of deep learning projects can you solve using PyTorch?
You can work on all sorts of deep learning projects using PyTorch! Here are a few examples:
- Handwritten Digit Classification
- Object and Image Classification
- Sentiment Text Classification
- Image Style Transfer, among others
You can check out the different datasets and projects to apply PyTorch on Analytics Vidhya’s DataHack platform.
Is PyTorch relevant in the industry?
Absolutely! While it’s still in its nascent stage, PyTorch is quickly becoming the go-to tool of choice for a lot of leading organizations. It’s flexible approach and easy-to-understand style have won over a lot of newcomers and industry veterans.
Do you need to know deep learning to learn PyTorch?
No! PyTorch is a framework - you do not need to know deep learning to learn how it works. It will of course help you to learn both PyTorch and deep learning - they both go hand-in-hand to be truly effective.
Make sure you check out our popular Computer Vision using Deep Learning course to dive in depth into this subject.
What kind of neural networks can you build using PyTorch?
PyTorch is an excellent framework for getting into actual machine learning and neural network building. In fact, in the course, we will be building a neural network from scratch using PyTorch. It is ideal for more complex neural networks like RNNs, CNNs, LSTMs, etc and neural networks you want to design for a specific purpose.
Which programming language works best with PyTorch?
As the name suggests, PyTorch is built on Python. The flexibility and ease- of understanding comes from Python as you’ll see in this Introduction to PyTorch for Deep learning course.
Who should take the Introduction to PyTorch for Deep Learning course?
This course is designed for anyone who wants to learn PyTorch. So if you’re a newcomer to deep learning and aren’t sure which framework to pick up, this course is for you! We have designed the course in a way that all newcomers will be able to start learning PyTorch. A basic knowledge of Python would be really helpful.
I have decent programming experience but no background in machine learning or deep learning. I have never designed a neural network. Is this course right for me?
Absolutely! We have designed the course in a way that will cater to newcomers and beginners. We start from scratch to get you onboard the PyTorch bandwagon and take things forward from there.
What is the fee for the course?
This course is free of cost!
How long would I have access to the “Introduction to PyTorch for Deep Learning” course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost.
How much effort do I need to put in for this PyTorch course?
You can complete the “Introduction to PyTorch for Deep Learning” course in a few hours. You are also expected to apply your knowledge of PyTorch and learning of this course to solve deep learning problems. The time taken in projects varies from person to person.
I’ve completed this course and have decent knowledge about PyTorch. What should I learn next?
That’s great! The next step in your journey should be all about learning deep learning concepts. Take the Computer Vision using Deep Learning course and start applying PyTorch on the concepts taught there.
Can I download the videos in this course?
We regularly update the “Introduction to PyTorch for Deep Learning” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos.
Which programming language is used to teach the Introduction to PyTorch for Deep Learning course?
This course uses Python programming language throughout. If you need a refresher or are new to Python, you’ll find the free Python for Data Science course really useful.