Fundamentals of Neural Networks & DL

1. Fundamentals of Neural Networks & DL

2. Almost all of us use the benefits of the meticulous Deep Learning models that are hidden beneath the covers of our smartphones on a daily basis. It starts with a simple auto-correction and progresses to asking Alexa for the daily news or score, a chatbot assisting us with support queries, and whether Facebook suggests we tag our friends based on identifying the images. • This is fantastic! But what exactly is a Neural Network? And how do we create it? Continue reading to learn more!

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4. Neural Networks

5. A Neural Network, also known as a Neural Net, is a system of interconnected processing units known as neurons. • Artificial Neural Networks (ANN) or Neural Networks are a component of AI and the foundation of Deep Learning. ANN is a computational architecture made up of neurons that mathematically represent how a biological neural network operates in order to identify and recognize relationships in data. • Essentially Non-linear machine learning models such as neural networks can be used for both supervised and unsupervised learning. Neural networks can also be thought of as a collection of algorithms that are loosely based on the human brain and are designed to recognize patterns.

6. Deep Learning

7. A deep learning algorithm represents a complex concept through a hierarchy of simpler concepts. Neural networks in Deep Learning perform unsupervised learning from unstructured data. Unsupervised, supervised, or semi-supervised learning can occur.

8. What is the significance of Deep Learning, and how does it help AI models? • Deep learning models work on their own and automatically uncover patterns because they are based on artificial neural networks, which are designed to mimic and learn from human brains. Unlike machine learning models, which require manual feature extraction, deep learning models train themselves to perform the task, learn through their own data processing, and implicitly drive features from training data.

9. Deep learning models require large amounts of data to learn better and extract features from data. The image below shows that deep learning models outperform, or are more accurate than machine learning models, which plateau with more data. • Because they are trained on large datasets, these multi-layered neural nets require a lot of computing power and must be run on a GPU.

10. Conclusion • For a long time, most AI analysts have focused on a narrow definition of Artificial Intelligence. Today, we know that Machine Learning algorithms can be programmed to do a lot more on their own with very little human intervention.