Full-Stack Deep Learning (FSDL) is an immersive, four-month training program for existing software engineers who want to become full-time deep learning engineers. FSDL is an unprecedented opportunity to create and commercialize your machine learning product, an essential skill needed to stay competitive in the age of artificial intelligence.
The term “full-stack” means something different in every field. For example, web development refers to a developer who can handle both front-end and back-end projects. In the case of deep learning, the stack includes everything from defining and training a neural network to deploying it into production.
Deep learning full-stack can recognize patterns and establish relationships between data inputs and outputs, allowing it to perform classification, regression, and clustering on large datasets.
It has been successfully applied in many fields, including object recognition, image classification, speech recognition, natural language processing (NLP), and time series forecasting.
Before deep learning full-stack became popular, shallow machine learning models such as support vector machines (SVM) or random forests were the dominant approach to machine learning. These models had the advantage of being more interpretable than deep learning because they could be easily understood without much effort.
Full-stack deep learning requires more resources due to the complex nature of its neural networks. They need a lot more data than shallow machine learning models and are computationally expensive, so they are more suited for problems with large datasets.
Importance of full-stack deep learning?
Full–stack deep learning is both necessary and essential to modern-day machine learning. They say that the devil is in the details, and never has there been a more appropriate adage in machine learning. While starting from scratch, you’ll start with a basic grounding of deep learning mathematical primitives. This will be useful for a lifetime. After that, you’ll cover the most critical tools and techniques for designing, training, and evaluating your models.
This is important because it allows you to see better what works and what doesn’t work on your problems. Deep learning has brought about many positive changes in modern technologies, making them much more intelligent. At the same time, what hasn’t changed is the need for an interpretable model. If an AI model is used in a product or service that a consumer interacts with, they must understand how that product or service will react to their input.
That’s where full-stack deep learning comes into play. It requires neural networks across multiple contexts to combine different types of information and usage patterns from internal data sources to build robust models. Full tack deep learning can help companies improve products and services by capturing more customer data and analyzing it in more contexts than usual machine learning techniques.
Things you should know as a full-stack developer
As you may have guessed, the term “full-stack” comes from web development. It refers to developers who are comfortable working with both back-end and front-end technologies (i.e., everything from databases to CSS). Similarly, we want to call ourselves full-stack deep learning practitioners if we are comfortable going from A to Z in the deep learning process. Specifically, this means that you should be able to do all of the following:
Understand the business problem and formulate it as a data science problem.
Obtain the relevant data, either by querying databases or web APIs, scraping websites, or organizing experiments on Mechanical Turk.
Clean and munge the data into a form suitable for modeling.
Create a test harness to evaluate candidate models.
Develop candidate models using machine learning algorithms like regression or random forests.
Select the best model based on empirical performance.
Optimize and refine the chosen model.
Present results clearly and effectively.
Employment scope of full-stack deep learning
With the help of deep learning, we can take AI from new technology to levels only dreamed of before. The role of a data scientist can be further split into three main areas, Hadoop – python developers, research and development engineers, and full-stack developers. The Full-Stack Deep Learning Developer employs programming tools to build complex systems that mine data in large volumes. It is also employed in diverse disciplines such as financial analysis, astrophysics, and computer vision.
The employment scope of full-stack deep learning is as follows:
- Deep Learning Engineer: You tinker with neural networks and implement algorithms and code. You train neural networks to solve specific problems. You often work with your data scientists and build models in TensorFlow, PyTorch, or Keras.
- Data Scientist: You discover information from the data and help your team make better decisions. You need to have good domain knowledge, a strong grasp of statistics and mathematics, and data visualization skills. And most importantly, you should be able to communicate your findings to others.
- Data Engineer: Your job is to ensure that all the data required for analysis is available in a single place and can be easily accessed for further analysis without compromising on its quality. Knowledge of programming languages such as Python and Java are must-haves in this role.
- Machine Learning Engineer: You engineer machine learning systems that solve real-world problems. You collaborate with data engineers, data scientists, and other team members while working on machine learning projects.
What are some full-stack deep learning tools?
- Keras is capable of running on top of MXNet, Deeplearning4j, TensorFlow, CNTK, or Theano.
- Array in Theano-compiled functions; transparent use of the GPU – Perform data-intensive calculations up to 140x faster than with CPU; efficient symbolic differentiation – Theano does your derivatives for function with one or many inputs.
- PyTorch is one of the fastest-growing Deep Learning frameworks around. Adopted by companies like Salesforce and Facebook, along with institutions like Fast.ai and Stanford.