Sprint: Deep Learning Fundamentals Introduction to Deep Learning Preparation Read the instructions below and follow the guidelines. Quiz Sprint 1 - Deep Learning Fundamentals This sprint provides an introduction to Deep Learning Fundamentals. In today’s world, where so much of what we do is driven by data, deep learning can be a game changer. It’s an integral part of artificial intelligence that enables us to understand huge amounts of data leading to significant advancements in fields like natural language processing, image recognition, and autonomous systems.
The aim of this sprint is to ensure you have a strong grounding by beginning with the basics. We’ll cover some important preliminary concepts such as linear algebra and differentiation. These basic concepts are just the first steps towards understanding how deep learning works.
Once you’ve grasped the basics, we’ll move on to more exciting topics. We’ll look at examples of how linear neural networks can be used for regression and classification tasks to help address real-life problems. Then, we’ll examine multi-layer perceptrons to see how they are constructed and trained for identifying complex patterns in data.
We’ll also learn about Pytorch and Pytorch Lightning. These frameworks are incredibly useful as they manage a lot of the more difficult aspects of the deep learning process so you can focus on constructing and refining your models without getting lost in the technical details.
By the end of this module you’lll have the ability to carry out a range of deep learning tasks. From predictive analytics to advancements on the frontier of AI, your new skills and knowledge has the potential to change lives. Let us embark on this exciting journey together!
The main resources for this sprint will be the following:
“Dive into Deep Learning” book
Pytorch Lightning “Deep Learning Fundamentals” course
Part 1: Introduction to Deep Learning In this part, we’ll look at an overview of deep learning to understand its fundamental concepts and its significance in the field of artificial intelligence. Deep learning is a subset of machine learning that leverages neural networks with multiple layers to model complex patterns in data. These layers are capable of automatically learning and extracting features from raw inputs, enabling the creation of sophisticated models for tasks such as image and speech recognition, natural language processing, and autonomous systems.
Understanding deep learning requires a solid grasp of several preliminary topics, including linear algebra, calculus, probability, and statistics. These mathematical foundations are crucial for comprehending the underlying mechanisms of neural networks, such as how they learn from data, optimize their parameters, and generalize to new, unseen examples. We will cover the essential preliminaries, providing a comprehensive background that will equip you with the knowledge needed to dive deeper into the intricacies of deep learning mathematics.
Objectives Understand what is deep learning Understand mathematics (linear algebra, calculus, probability, and statistics) behind deep learning Apply mathematical concepts (linear algebra, calculus, probability, and statistics) behind deep learning
What is Deep Learning? Let’s start our journey towards understanding deep learning by reading this section. It is a comprehensive guide which should give a good foundation in understanding deep learning concepts and their potential applications. Then we can explore the concepts explained in the Deep learning fundamentals course by Pytorch Lightning from 1-2 units.
Preliminaries Next we will cover the mathematics required to understand deep learning. This section gives an overview of key topics. If you are confident that you have a good understanding of that section, you can skip the next section which goes into more detail.
Hands on practice Subsection 2.3 has 13 exercises. Make sure that you are capable of solving at least 75% of exercises without using any external resources. Subsection 2.4 has 10 exercises. Make sure that you are capable of solving at least 50% of exercises without using any external resources. Subsection 22.3 has 4 exercises. Make sure that you are capable of solving at least 50% of exercises without using any external resources.
Additional material (optional) Mathematics for deep learning course (approx 2 hours)
Linear algebra (approx 3 hours)
Gradient calculation (10 minutes)
Chain rule (25 minutes)