5 Free Resources to Understand Neural Networks

Image by Author | Ideogram

Neural networks are the building blocks behind every advanced AI system nowadays: from computer vision solutions to generative AI solutions and language models, most real-world solutions that involve some degree of AI have intricate neural network architectures at their core. But, what are neural networks and how do they perform surprisingly well in intelligently solving challenging tasks? To satisfy your curiosity at no cost, this post lists five resources to help you understand the mechanisms behind neural networks.

1. Coursera Deep Learning Specialization

 
This popular Coursera specialization consists of five courses, ranging from beginner to intermediate level, on the foundations of large-scale models based on neural networks, a.k.a. deep learning models. A well-recognized option to learn about neural networks from scratch, the specialization content can be accessed and viewed for free, albeit without getting a certificate of completion. After introducing neural networks and deep learning, the courses examine strategies to optimize and fine-tune models, structure neural network-based projects, and explore advanced architectures for Natural Language Processing (NLP) and computer vision.

Importantly, courses offered by Andrew Ng's DeepLearning.AI platform are of excellent quality, but they are moderately to highly technical, hence some math, statistics, and programming background are essential to make the most of this resource.

2. Michael Nielsen's Online Book: Neural Networks and Deep Learning

 
For those more comfortable with reading resources, the free online book "Neural Networks and Deep Learning", published by Michael Nielsen, constitutes a valuable resource for understanding neural networks foundational concepts. The book covers essentials such as how neural networks learn, the mathematics behind their training process, and how multiple-layer deep neural networks are structured and built "step by step". Advanced architectures like convolutional networks (CNNs) and regularization techniques such as dropout are also examined. The book is highly interactive and provides visual aids and code examples to facilitate a practical understanding of neural networks.

3. 3Blue1Brown Youtube Channel

 
This vast youtube channel contains hundreds of videos about all things math, statistics, and data. One of its playlists is focused on neural networks. Through visually attractive example-based lessons, the author demystifies neural networks and their inner workings, with a particular focus on the mathematical processes underlying them.

3Blue1Brown YouTube channel screenshot (source: 3Blue1Brown)

4. Coding Tutorials: Deep Learning with PyTorch: A 60 Minute Blitz

 
A small yet not-to-miss collection of coding tutorials for beginners interested in a very hands-on introduction to neural networks by using PyTorch, one of Python's major frameworks for building deep learning systems. This resource is ideal for those who want to start learning neural networks by coding, rather than acquiring a deep conceptual or math-driven understanding. A quick-to-complete resource by the end of which you'll have created a neural network-based classifier to categorize low-resolution images.

5. Course: Practical Deep Learning by Fast.AI

 
If you are not a beginner in neural networks or completed the above-suggested coding tutorial "A 60 Minute Blitz" and are hungry for more, this fast.ai free course dives deeper into building deep learning systems by coding. Also based on PyTorch, this course is ideal for experienced programmers who need to dive into deep learning modeling and best practices to build effective neural network architectures to solve more challenging tasks. Spoiler: it is also designed as a "bunny-friendly" course!

Fast.ai website screenshot (source: fast.ai)

Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.