Best Courses on TensorFlow and PyTorch

Intro

TensorFlow and PyTorch are both popular deep learning frameworks, and both have their own strengths and weaknesses. TensorFlow is widely adopted and has a large community, while PyTorch has been growing in popularity and is known for its ease of use and flexibility.

If you compare the popularity of the two frameworks, it is now 2:1 (it was 3:1 once) in terms of search frequency, job postings, number of questions, etc. In short, while TF remains a very safe choice, PT is rapidly gaining popularity.

Ultimately, the choice between the two will depend on the specific needs and preferences of the user or developer. It’s also important to note that both frameworks are continuously being developed and improved, so it’s worth keeping an eye on new updates and features to see if they align with your project’s requirements.

Trends

Source: https://insights.stackoverflow.com/trends?tags=pytorch%2Ctensorflow

Consider

I recommend to consider the following when choosing between PyTorch and TensorFlow:

1. Ease of use: PyTorch is known for its easy-to-use API and dynamic computational graph, which allows for more flexibility and experimentation. TensorFlow, on the other hand, has a more complex API and requires more boilerplate code.

2. Community support: TensorFlow has a larger and more established community, which means more resources and a wider range of pre-trained models available. PyTorch is still relatively new, but its community is growing rapidly.

3. Performance: Both frameworks are highly optimized and perform well, but TensorFlow is generally considered to be more efficient for large-scale production deployments. PyTorch is better suited for research and prototyping.

4. Features: TensorFlow has a wider range of features, including distributed training and mobile deployment. PyTorch has recently added support for mobile deployment and is focusing more on research and experimentation.

5. Development: TensorFlow has a more mature development cycle and is typically released in a stable version. PyTorch development is more experimental, with updates and features being released more frequently.

Ultimately, the best choice will depend on the specific needs of your project, but it’s worth trying out both frameworks and seeing which one works best for you.

If you need more help choosing between them, check out this comprehensive article: PyTorch vs TensorFlow for Your Python Deep Learning Project.

Courses

To improve proficiency in using PyTorch or TensorFlow, taking an online course can be a great way to gain hands-on experience and learn from experts in the field. Some popular online courses that cover deep learning with PyTorch or TensorFlow include:

Top 5 Best Courses on PyTorch:

Top 5 Best Courses on TensorFlow:

It’s worth noting that these are not the only resources available, and there are many other great courses and tutorials available online. It’s important to find the one that best fits your needs and learning style.

For instance, the fast.ai Practical Deep Learning for Coders has great videos and a supportive community. It’s especially useful if you want to learn PyTorch. And, of course, both TensorFlow and PyTorch official sites have great tutorials and guides too.

Furthermore, there are other resources like GitHub, StackOverflow, and forums where you can find valuable information and help in troubleshooting problems you may encounter, while learning how to use PyTorch and/or TensorFlow, or doing real projects.

More Courses

If you’re interested in learning or refreshing your Data Science and/or Python skills in general before moving on to specific topics of Machine Learning, Deep Learning, Computer Vision, Neural Networks, and Artificial Intelligence, these courses are worth checking out 👇

Practice

It’s also important to practice and work on real-world projects to gain hands-on experience and solidify your understanding of the frameworks.

Best of luck!

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