Machine Learning EP 03 What are Neural Networks in 9 minutes

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Tech enthusiasts! • Dive into the neural networks that power AI. • We'll explore their architecture, layers, and activation functions, and see how they're the building blocks of intelligent systems. And if you're keen to master these concepts, stick around for some exciting learning opportunities at the end of the video! • Segment 1: So what are Nerial Networks? • Neural networks are inspired by the human brain, composed of nodes or neurons connected by pathways. These networks process information in layers, starting from the input layer, through hidden layers, and finally to the output layer. • The architecture of a neural network, including how many layers it has and how these are connected, defines its ability to learn and solve problems. From simple networks for basic tasks to complex ones like deep neural networks for advanced image recognition, the structure is key. • Segment 2: Layers in Neural Networks • Each layer in a neural network has a specific function. The input layer receives the data, hidden layers process it, and the output layer delivers the final verdict. Hidden layers are where the magic happens, transforming inputs into something the network can use to make decisions or predictions. The depth and design of these layers play a crucial role in the network’s learning capability. • Segment 3: Activation Functions • Moving on to activation functions, these are the heartbeats of neural networks. They decide whether a neuron should be activated, influencing the network’s output. • Each activation function has its purpose. For example, ReLU is great for non-linear problems, while softmax is used in classification tasks to determine probabilities of different classes. • ReLu (Rectified Linear Unit) and Softmax are two commonly used activation functions in neural networks. • 3A. ReLu: • ReLu is a type of activation function that is used to introduce non-linearity into the neural network. In other words, ReLu returns 0 if the input is negative and returns the input value if it is positive. This means that ReLu can effectively turn off certain neurons in the network by setting their outputs to 0, which can help with the problem of vanishing gradients. ReLu is also computationally efficient and is widely used in convolutional neural networks (CNNs) for image recognition tasks. • 3B. Softmax: • Softmax is another activation function that is typically used in the output layer of a neural network for classification tasks. It takes in a vector of logits (unnormalized predictions) and outputs a vector of probabilities that sum to 1. This is useful for multi-class classification problems, where the model needs to predict the probability of each class being the correct one. Softmax can handle a variable number of classes and is more robust for classification problems compared to other activation functions like sigmoid or ReLu. • In summary, ReLu introduces non-linearity and helps with the problem of vanishing gradients, while Softmax is used for multi-class classification tasks. Both are important components in neural networks and are used in different layers for different purposes. • • Conclusion: • There you have it, some simple insights into the intricate world of neural networks, from their architecture to the layers and activation functions that make them tick. Ready to go from curious to expert? OBI Academy is your next stop! • With courses like Certified AI Machine Learning Professional (CAIMLP), Generative Python Prompting Professional (GPTPv2), and Certified Blockchain Cryptocurrency Professional (CBCP), we have all you need to thrive in the AI era. And guess what? You get to enjoy exclusive offers on these courses today with our 25% discount on any of our courses! • Join us at OBI Academy to unlock your potential in AI and beyond. • Thank you all for listening today. • Please Like, share, and subscribe for more insights into the future of technology. Your journey to becoming an AI pro starts today! • Enjoy :-) ! • Best regards • -Dimitrios Zacharopoulos • OBIPIXEL LTD | OBI.ACADEMY • 🙏 Support my channel by becoming a member: • / @obipixel • • 🎶 Music: • Music from #Uppbeat (free for Creators!): • https://uppbeat.io/t/torus/progression • License code: BYO98BEARNRJOMVR • #End of video animation audio: • License from Paul Werner United (Creator License ID) • License code: 23904 • • 🔔 SUBSCRIBE • / @obipixel • 🙏 SUPPORT MY CHANNEL IN OTHER WAYS • PATREON: patreon.com/obipixel_ • BUY ME A COFFEE ☕️: https://www.buymeacoffee.com/obipixel • MORE • 📸 Instagram: / obipixel_ • 🌎 Web: https://obi.academy • 😃 Facebook: / dimitrios.zacharopoulos • 🐦 Twitter: / obipixel_ • Thanks for watching, see you on our next video!

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