Example

neuralnetworks.md

00:00:11 we just started one second ago.

00:00:21 today we are going to be talking about new networks.

00:00:27 I'll provide the motivation behind why researchers started looking into new networks.

00:00:44 we'll discuss why new networks have shown impressive performance just now, not earlier.

00:01:01 we'll build from understanding their mathematical background to discussing forward and backward pass.

00:01:17 can anyone tell me what you know about new networks?

00:01:30 neural networks are used for large language models.

00:01:55 neural networks replicate how the human brain functions.

00:02:04 I think about neural networks in terms of layers of linear transformations and nonlinear activation functions.

00:02:42 the biological inspiration behind neural networks comes from the brain's neuron structure.

00:04:01 a biological neuron processes inputs through dendrites, performs processing in the nucleus, and outputs through exons.

00:05:01 the basic building block of a neural network is called a neuron or perceptron.

00:05:43 artificial neurons take input data, process it, and produce an output.

00:06:12 artificial neurons can be combined in series or parallel to form complex structures.

00:06:53 let's take an example of predicting the probability of a t-shirt being a top seller based on features like price and marketing.

00:08:42 traditional logistic regression would use raw features but may miss key factors.

00:09:17 expert knowledge suggests that raw features combine to form factors like affordability and awareness, which impact the probability.

00:10:52 neural networks combine raw features in ways to form valuable factors for predicting outputs.

00:23:02 the real power of neural networks lies in their ability to perform feature engineering internally.

00:25:00 neural networks are often criticized as blackbox algorithms since their internal workings are complex and not easily interpretable.

00:28:13 let's formalize the structure of a neural network with input layers, hidden layers, and an output layer.

00:29:45 forward pass refers to the process of making a decision with a neural network, moving data through the layers to produce an output.

00:32:02 weights are parameters that govern how inputs are combined; biases adjust the activation threshold.

00:32:56 activation functions introduce nonlinearity into the neural network, necessary for modeling complex relationships.

00:33:20 without activation functions, neural networks would reduce to simple linear regressions.

00:42:00 the backpropagation algorithm is used to train neural networks by computing gradients and updating weights.

00:44:20 backpropagation makes use of the chain rule to compute derivatives efficiently.

00:50:43 implementing backpropagation from right to left (backward pass) reduces computational complexity.

00:29:23 deep learning refers to neural networks with multiple hidden layers, enabling them to model complex patterns.

00:49:36 although training neural networks is complex, they excel in tasks that require modeling intricate data relationships.

00:51:11 questions and discussions are encouraged to better understand the complex topic of neural networks.