Deep Learning Explained

Key Takeaways

hi there and welcome back to 365 data science in today's video we'll explore the fascinating world of deep learning a powerful subset of machine learning we'll cover what deep learning is how deep learning is inspired by the human brain the structure of artificial neural networks Anns how Ann's process information a practical example using the mnist database and real world applications of deep learning let's dive in deep learning is a fascinating subset of machine learning inspired by how the human brain works here's an AI generated image what do you see at first glance it's a sunny day and a crowded Beach right upon closer inspection we see children playing in the sand around a massive sand castle at the center we then examine each person on the beach more closely we find that AI endered this individual's face oddly resulting in a bizarre and slightly unsettling appearance our brain processes information in various phases and at varying depths initially viewing an image provides a raw broad impression that presents insights into the scene's context the more time and attention we devote to details the more information and subtleties we can process and observe in the context of deep learning a neural network processes information similarly here's what a neural network resembles please don't be scared everything will make sense in a second picture the first layer as our input information similar to observing a sunny crowded Beach day then as the data passes through more Network layers intermediate layers start recognizing more complex features such as shapes or specific objects remember how I noticed the enormous sand castle in the middle every intermediate layer of the neural network builds a more detailed understanding of the basic features identified by earlier layers so there's an incremental increase in the level of detail acquired the deeper layers of the network synthesize lower level Fe features into highlevel features representing more complex aspects of the input data this is how we can spot the strange face generated by AI it took some time to process the information and get to this conclusion honestly I think I subconsciously wanted the picture to reflect that because I was curious if the AI had produced a quality example and eventually my brain focused on on this little detail okay perfect so that's the intuition behind deep learning but before we dive deeper I'd like to introduce you to 365 data Sciences learning platform if you're excited about Ai and machine learning we've got the perfect courses to get you started our introduction to AI course provides a comprehensive overview of artificial intelligence while our machine learning in Python course offers hands-on experience with popular ml libraries for those interested in neural networks our deep learning with tensorflow 2 course teaches you how to apply neural networks to solve real world data science challenges providing practical experience with this cuttingedge technology 365 data science has something for everyone explore our full range of courses and start your AI and machine Learning Journey today now let's get back to our discussion about deep learning it's a complex process that allows machines to learn by processing input information in stages let's explore some technical details to deepen our understanding we call this neural network artificial neural network or Ann biological neural networks Inspire it but they work much differently like our senses an Ann's input layer sends raw data to the brain then the intermediate layers or hidden layers layers process input information neural networks can have one or multiple hidden layers increasing layers enhances complexity adding more layers to a network increases its learning capacity but the downside is that it needs to be carefully managed to ensure effective learning we also have an output layer that generates the final result every layer of the artificial neural net is made of neurons or nodes responsible for processing and transforming the information received let's use the mnist database as an example training a model to recognize handwritten digits involves supplying thousands of pre-labeled examples how does this training happen while the process May initially appear complex let's simplify it to a more manageable form the process starts with the input layer of the Ann receiving an image of a handwritten digit each pixel of this image serves as an input node in the case of mnist images are 28x 28 pixels so the input layer typically has 784 input nodes imagine that the 784 input nodes are stored as a vector inside the neural net to form the input layer each of the 784 input nodes can contains a number based on how bright or dark it is here zero represents white while any value greater than zero indicates a color other than white we call this number activation the higher the number the darker the content inside a given node we describe the 784 input nodes in the input layer as the neural Nets width and its number of layers as its depth here the network comprises three hidden layers along with the input and output layers totaling a depth of five we have numerous connections between nodes because each layer's nodes are linked to every node in the subsequent layer this extensive network of connections is essential for learning from input data serving as mathematical transformations these Transformations occur through a mix of weights and nonlinear operations with optimal weight combinations across all nodes enabling learning this method translates the input via various layers refining the information before it generates a result learning occurs by designing a system that identifies optimal weights and biases to solve a specific problem involving thousands of repetitions to discover the best combination so why do we need several layers what kind of Transformations can we expect in each layer to end up with a system capable of recognizing the digits in a photo well when we see an image in the background our brain connects various components of what it is made of for instance the number three comprises two elements a rounded top and a curved bottom the first hidden layer in our neural network might learn to recognize these features from the input data then in the second hidden layer we continue to build on the information processed by the previous one we've identified edges and curves and now we can use them to discern more complex shapes like loops and intersections bringing us closer to recognizing numerals when the information reaches the third layer the neural network has learned to recognize the overall shape and configuration representing the number three finally the output layer takes the processed data from the last hidden layer and determines whether the digit is a three this is how a neural network learns to perform what appears to be a simple task recognizing a number the underlying process C however involves intricate mathematical manipulations which we didn't describe fully in essence the layers of Ann through their depth and bread create a robust system of pattern recognition and data interpretation that mirrors some aspects of human cognitive processes albeit more structured this capability to analyze large high-dimensional data sets and recognize complex patterns with high accur accuracy makes deep learning a revolutionary tool in AI it's what has made today's incredible AI advancement possible let's recap the key points we've covered in this video deep learning is a powerful subset of machine learning inspired by the human brain artificial neural networks Ann's process information through multiple layers from input to Output each layer in Ann builds upon the features identified by previous layers allowing for more complex pattern recognition the mnist database example demonstrates how Ann's can be trained to recognize handwritten digits deep learning has numerous real world applications from image recognition to natural language processing thank you for watching this video if you have found this content helpful please like subscribe and follow for more on data science Ai and machine learning until next time keep learning