We’ll set the scene: you’re just getting started with deep learning and are unsure of where to start: from frameworks to libraries, it seems like there’s an unending selection of things to learn. And while that is somewhat true (there will always be something new to explore in the field of deep learning), don’t worry too much. To begin your career in the deep learning field, you’ll just need to find a place to jump in and TensorFlow and PyTorch are two great places to start.
In this article, we’ll dive deep into both of these frameworks, discussing their advantages and disadvantages and, most importantly, how to make the right choice for you and your career goals.
First, however, we’ll review exactly what a deep learning framework is.
What are Deep Learning Frameworks?
To keep it simple, let’s explain it this way: deep learning frameworks simplify the process of building and training AI models through providing the basic foundation and instructions needed. So, instead of working on every little step yourself, which is both time consuming and complicated, you can get right to the good stuff.
Let’s make it even simpler: think of frameworks as recipes for meals you want to make. Frameworks will provide the recipe, instructions, and possibly the ingredients, so that you can just focus on getting to the end goal: your dish. In the case of AI models, however, this final product would be the AI model that can then carry out its intended function.
There are tons of deep learning frameworks out there, but two of the most common choices for beginners are TensorFlow and PyTorch because of the following:
They’re user-friendly: their built-in tools and functions let you get started almost immediately, making it a great choice for beginners and experts alike.
They have a large community: as two of the most popular framework options for both beginners and experts, there is a large online community full of information and tips, in case you get stuck.
They’re versatile: from the simplest model to the most complex, both TensorFlow and PyTorch possess the requirements to build whatever model you need.
They’re popular with experts: because many companies use TensorFlow and PyTorch for their own research and AI model development, these frameworks are frequently used in the latest and most innovative projects, allowing you to understand exactly what’s progressing in the industry.
They’re both in constant evolution: backed by Google (TensorFlow) and Meta (PyTorch), both are constantly being updated, improved, and documented, ensuring you’re using frameworks that are up-to-date with the latest trends and technologies.
They both sound great, right? We agree. There are, however, some differences between the two, and now we’re going to dive into exactly what sets them apart.
What is TensorFlow?
In addition to everything we listed above, TensorFlow brings some peculiarities to the table that may make it the right fit for you–or not. Let’s dive a bit deeper into exactly what makes up TensorFlow so that you can decide if it’s the best choice for you and your deep learning journey.
Although TensorFlow is easy to use and user-friendly today, it wasn’t always like this. Its older versions were quite complex and therefore had a more significant learning curve. TensorFlow 2.0, however, eliminated some of these hurdles and is now just as user-friendly as other frameworks.
One example of this is that originally, TensorFlow used a static computation graph, which works by first building the graph and then running, which isn’t as easy to adjust later on. TensorFlow 2.0, however, introduced eager execution, which is what PyTorch employs, to simplify the process.
An advantage of TensorFlow is that its production and development tools are very advanced, facilitating the product deployment process significantly. In addition, it has the ability to deploy products for mobile and embedded devices, instead of just focusing on typical environments.
TensorFlow also boasts the following:
An established community: because TensorFlow is so popular, its online community is vast and includes many libraries that are built on top of it. And because it’s backed by Google, its production capacities and large-scale systems are advanced.
High performance: with distributed computing and efficient scaling, TensorFlow is one of the best optimized for production frameworks.
What is PyTorch?
PyTorch, as its name suggests, is similar to Python, so those who are familiar with Python will benefit from this connection. And its code follows the same path as traditional coding, making the code writing and debugging process very straightforward.
Through the use of a dynamic computation graph, which is built as the code runs, PyTorch allows you to make changes as you run. This is incredibly useful for adjusting specific aspects to work better or more efficiently without wasting time going back to square one. Dynamic computation graphs are also the best choice for checking out various model designs before choosing the one that works best for you.
PyTorch is known as one of the best frameworks for research, as it’s catching up to other frameworks for deploying models. However, it is advancing in the production and deployment environments–this focus on research means that PyTorch’s online community is vast and full of innovative uses, thanks to its flexibility and ease of experimentation.
PyTorch also boasts the following:
A strong research focus: PyTorch is favored by the research community due to its dynamic computation graph, making it easier to experiment and rapidly prototype new ideas. Its intuitive design also attracts users who are familiar with Python, making it a natural fit for fast-paced development environments.
Flexibility and ease of debugging: with PyTorch’s dynamic graphing and real-time execution, debugging is straightforward, allowing developers to test and tweak models with standard Python tools like print() statements and debuggers.
Choosing the right deep learning framework depends on your goals. Whether you're looking to innovate in research with PyTorch or scale your AI model to production with TensorFlow, each tool brings its own strengths to the table. If you're ready to take your understanding of deep learning to the next level, Ironhack's AI and Data Analytics Bootcamps offer the hands-on experience and industry knowledge you need to break into the world of AI.
Enroll today to start building your future in tech!
About the Author:
Juliette Carreiro is a tech writer, with two years of experience writing in-depth articles for Ironhack. Covering everything from career advice and navigating the job ladder, to the future impact of AI in the global tech space, Juliette is the go-to for Ironhack’s community of aspiring tech professionals.
