Speaker 1: Welcome to the deep dive. Today we're plunging into neural
Speaker 1: network programming with Java by Fabiosaurs and Alan Susa.
Speaker 2: Yeah, and this book it's not just code, is it.
Speaker 2: It really goes deep into the fundamentals exactly.
Speaker 1: It's about how these well intelligence systems are built, how
Speaker 1: they learn. Our mission today extract the key insights.
Speaker 2: Uh huh, those surprising bits, the aha moments. We want
Speaker 2: to give you a real shortcut to understanding these networks.
Speaker 1: We'll make the complex stuff digestible, engaging, show you what
Speaker 1: they are, but also why they.
Speaker 2: Work and the incredible things they can actually do out
Speaker 2: there in the real world.
Speaker 1: Okay, so neural networks, these artificial brands, where do we
Speaker 1: even start? What's the core idea? How can we even
Speaker 1: build something based on a brain?
Speaker 2: It's fascinating. Actually, you have to go way back, like
Speaker 2: the nineteen forties, really that early. Yeah. A neurophysiologist Warren
Speaker 2: McCulloch and a mathematician Walter Pitts. They created the first
Speaker 2: mathematical model of an artificial.
Speaker 1: Neuron, inspired by the real thing.
Speaker 2: Absolutely, they saw the natural neuron as a kind of
Speaker 2: simple processor. It sums up signals, decides whether to fire
Speaker 2: propagates it onward. That basic idea was the spark.
Speaker 1: Biological simplicity leading to well, technological complexity.
Speaker 2: You got it.
Speaker 1: So, building on that, these artificial networks have some key parts, right,
Speaker 1: the building blocks definitely.
Speaker 2: First up, the artificial neuron itself.
Speaker 1: The basic processing unit exactly.
Speaker 2: Takes multiple inputs kind of like dendrites.
Speaker 1: Aggregates them right.
Speaker 2: Sums them up, and then produces a single output like
Speaker 2: an axon firing based on some internal logic.
Speaker 1: Okay, makes sense. But the connections matter too.
Speaker 2: Oh hugely. That's where the weights come in. They're the
Speaker 2: connections between.
Speaker 1: Neurons, not just wires though.
Speaker 2: No, No, they amplify or reduce these signals passing through.
Speaker 2: They multiply the input signal.
Speaker 1: And that's where the learning happens, adjusting these weights decisely.
Speaker 2: The weights essentially store the network's knowledge.
Speaker 1: But is that enough just weights and neurons. Feels like
Speaker 1: something's missing for real complexity.
Speaker 2: You're right, you need bias and those crucial activation functions.
Speaker 1: Okay, tell me about bias first.
Speaker 2: Bias is like an extra input always set to one
Speaker 2: with its own weight. It adds a constant value to
Speaker 2: the sum before the activation function kicks in.
Speaker 1: Why what does that do?
Speaker 2: It gives the neuron more flexibility. It basically shifts the
Speaker 2: activation threshold, allowing the network to model more complex relationships
Speaker 2: stuff that doesn't necessarily pass through the origin helps handle
Speaker 2: nonlinear stuff better.
Speaker 1: Got it? And activation functions you said they're crucial. The
Speaker 1: book mentions sigmoid ton.
Speaker 2: Right, hyperbolic tangent also purely linear functions. But the key
Speaker 2: insight here is the nonlinear.
Speaker 1: Ones like sigmoid and ton.
Speaker 2: Exactly. Without nonlinearity, even a deep network, a multi layer
Speaker 2: one would just be doing a sequence of linear operations.
Speaker 2: It means it could only solve linear problems. Nonlinear activation
Speaker 2: functions let the network learn really complex curved boundaries in
Speaker 2: the data. I think image recognition that's inherently nonlinear.
Speaker 1: Okay, So that nonlinearity is the secret sauce for handling.
Speaker 2: Complexity A big part of it.
Speaker 1: Yeah, And these neurons they aren't just you know, floating around.
Speaker 1: They're organized into layers.
Speaker 2: Correct. You have input layer where data comes in, an
Speaker 2: output layer where the result comes out, and in between
Speaker 2: potentially one or more hidden layers.
Speaker 1: Hidden layers sound important.
Speaker 2: They really are. They allow the network to build up
Speaker 2: layers of abstraction. It learns intermediate features representations of the
Speaker 2: data that aren't obvious in the raw input but are
Speaker 2: useful for the final task. That's where complex knowledge gets represented.
Speaker 1: Like building its own internal understanding tind of. Yeah, and
Speaker 1: how these layers and neurons are arranged. That gives different architectures.
Speaker 2: Yep. Simple ones are mono layer just input and output.
Speaker 2: More complex or multi layer with those hidden layers. Okay,
Speaker 2: then there's how the signal flows. Feed Forward is the
Speaker 2: basic type signal goes one way input to output straightforward,
Speaker 2: But then you have feedback networks or recurrent networks.
Speaker 1: We're current, meaning the signal can loop back exactly.
Speaker 2: Outputs from neurons can be fed back as inputs to
Speaker 2: neurons in the same or earlier layers. This introduces memory,
Speaker 2: a sense of.
Speaker 1: Time, useful for sequences time series data.
Speaker 2: Perfect for that pattern recognition over time. But the catch
Speaker 2: is they're significantly harder to train. Why is that because
Speaker 2: the network state depends on its previous states. That feedback
Speaker 2: loop complicates the learning process tracking how errors should propagate back?
Speaker 1: Right, that makes sense. So we have these components, these
Speaker 1: architectures these artificial brains. But how do they actually learn?
Speaker 1: What's the mechanism?
Speaker 2: Well, fundamentally, learning is about adjusting those weights, systematically, changing
Speaker 2: the connection strengths based on experience, based on data. Yeah,
Speaker 2: but what's really fascinating is the distributed nature of this intelligence,
Speaker 2: meaning it's spread out exactly. It's not one central brain
Speaker 2: part holding all the knowledge. It's across potentially millions or
Speaker 2: billions of tiny connections, each weight holding a small piece.
Speaker 1: So it's robust. Losing a few connections isn't catastrophic.
Speaker 2: Generally, yes, very robust compared to traditional programs where one
Speaker 2: error can crash everything. And this distributed learning helps them
Speaker 2: generalize well to new data.
Speaker 1: Okay, and the book talks about two main ways they learn,
Speaker 1: two paradigms. First is supervised.
Speaker 2: Learning right learning with a teacher. Essentially, you give the
Speaker 2: network an input X and the correct output why you wanted.
Speaker 1: To produce labeled data exactly.
Speaker 2: The network makes a prediction, compares it to the target why,
Speaker 2: calculates the error, and then uses that error to adjust.
Speaker 1: Its weights, so it learns to map X to Y precisely.
Speaker 2: This is great for things like image classification. Here's a
Speaker 2: picture tell if it's a cat or a dog, or
Speaker 2: speech recognition forecasting tasks where you know the right answer
Speaker 2: during training.
Speaker 1: Okay, supervised is learning from examples. What's the other.
Speaker 2: Type unsupervised learning? Here there's no teacher labels. You just
Speaker 2: give the network the input data XP and it has
Speaker 2: to figure things out on its own, find hidden structures, patterns, correlations,
Speaker 2: group similar data points.
Speaker 1: Together, so discovering patterns rather than predicting known answers exactly.
Speaker 2: Think clustering, grouping customers based on purchasing habits without knowing
Speaker 2: the groups beforehand, or data compression finding efficient ways to
Speaker 2: represent the information.
Speaker 1: That sounds powerful for exploration, it really is.
Speaker 2: Discovering insights you didn't even know to look for.
Speaker 1: So in both cases there's a learning algorithm driving this
Speaker 1: weight adjustment.
Speaker 2: Yes, a systematic procedure. The goal is usually to minimize
Speaker 2: a cost function, which is just a mathematical way of
Speaker 2: measuring the total error the network is making.
Speaker 1: And a key part of this is splitting the data.
Speaker 1: Training and testing absolutely crucial.
Speaker 2: You train the network on one set of data, but
Speaker 2: you evaluate its real performance on a separate set. It's
Speaker 2: never seen before the test set.
Speaker 1: Why separate them.
Speaker 2: To prevent overtraining or overfitting. Yeah, that's when the network
Speaker 2: basically just memorizes the training examples, noise and.
Speaker 1: All, like cramming for a test.
Speaker 2: Exactly. It does great on the stuff it memorized, but
Speaker 2: fails miserably on new questions because it didn't learn the
Speaker 2: underlying concepts. Testing on unseen data checks for that generalization ability.
Speaker 1: Okay, And there are knobs to tune in this learning
Speaker 1: process parameters.
Speaker 2: Oh yeah, A big one is the learning rate usually
Speaker 2: called eta. What does that control? It controls how much
Speaker 2: the weights are adjusted in response to the error in
Speaker 2: each step.
Speaker 1: So like the size of the learning steps.
Speaker 2: Kind of too high and you might overshoot the best
Speaker 2: solution bouncing around radically too low and learning can be
Speaker 2: incredibly slow, might get stuck. It's a balancing act, makes sense?
Speaker 1: And how does the network know when to stop learning?
Speaker 2: Those are the starting conditions? Could be a maximum number
Speaker 2: of training cycles called epochs.
Speaker 1: Epox meaning passes through the whole training data set.
Speaker 2: Right, Or you might stop when the error on the
Speaker 2: training set or maybe a separate validation set drops below
Speaker 2: a certain target threshold, or when the error stops improving significantly.
Speaker 1: Setting the goalposts for its education pretty much. Yeah, so
Speaker 1: let's get concrete. The book talks about some early algorithms.
Speaker 2: The perceptron the simplest one, really. It updates weights based
Speaker 2: directly on the output error and the learning rate.
Speaker 1: Super basic, but it has limits, right you mentioned that earlier.
Speaker 2: Big limits. This raises the really important question of what
Speaker 2: can't hit do?
Speaker 1: And the classic example is the XOR problem Y's.
Speaker 2: Exactly exclusive or R. If you plot the inputs and
Speaker 2: outputs for xor on a two D graph, you have
Speaker 2: points at zero zero, zero, one meters one mate of one,
Speaker 2: matters one and one middle.
Speaker 1: Zero right, two classes zero and one.
Speaker 2: Try drawing a single straight line to perfectly separate the
Speaker 2: zeros from the ones.
Speaker 1: You can't.
Speaker 2: You absolutely cannot, And that's the perceptron's limitation. It can
Speaker 2: only learn problems that are linearly separable, problems where you
Speaker 2: can draw that single line or a plane in higher dimensions.
Speaker 1: Like an A and D gate that's linearly separable. The
Speaker 1: book uses a warning system example for that.
Speaker 2: Right, if sensor A and D sensor B or on
Speaker 2: trigger the alarm. A perceptron can learn that easily, but XRP.
Speaker 1: So a step up from the basic perceptron was the
Speaker 1: delta rule.
Speaker 2: Yeah, an improvement. It takes the activation functions non linearity
Speaker 2: into account. Specifically, it's derivative when calculating the weight updates.
Speaker 2: It's a bit more sophisticated, uses gradiate descent conceptually.
Speaker 1: But still fundamentally limited to single layers mostly.
Speaker 2: Yeah, still struggles with things like XR.
Speaker 1: So here's where it gets really interesting, right, how did
Speaker 1: they crack problems like xor?
Speaker 2: The breakthrough was multilayer perceptrons or MLPs, adding those hidden layers.
Speaker 1: That was the key.
Speaker 2: That was the revolutionary idea. By adding one or more
Speaker 2: hidden layers between the input and output, the network gains
Speaker 2: the ability to learn nonlinear decision boundaries.
Speaker 1: How what did the hidden layers do?
Speaker 2: They essentially learned to transform the input data into a
Speaker 2: new representation. In this new hidden space, the problem can
Speaker 2: become linearly separable. The hidden layer learns useful intermediate features.
Speaker 1: So it finds its own way to make the problem solvable.
Speaker 2: Exactly, it learns abstractions for xor, a hidden layer can
Speaker 2: create internal representations that allow a final output layer to
Speaker 2: draw that separating line.
Speaker 1: Metaphorically speaking, Wow, but how do you train these. If
Speaker 1: the hidden layers aren't directly connected to the final error,
Speaker 1: how do their weights get updated.
Speaker 2: Ah, that's where the truly game changing algorithm comes in. Backpropagation,
Speaker 2: the famous backprop that's the one. It calculates the error
Speaker 2: at the output layer, just like before, but then it
Speaker 2: propagates that error backwards, layer by layer.
Speaker 1: Back through the hidden layers.
Speaker 2: Yes, it uses the chain rule from calculus essentially to
Speaker 2: figure out how much each weight in every layer, including
Speaker 2: the hidden ones, contributed to the.
Speaker 1: Final error, and then adjusts them accordingly.
Speaker 2: Precisely, it allows the entire network, all the connections, to
Speaker 2: learn in a coordinated way based on the final output error.
Speaker 2: It's what made training deep complex networks feasible.
Speaker 1: Powerful stuff. The book also mentions Levenberg mark Wort.
Speaker 2: Yeah. Briefly, it's another more complex optimization algorithm, often converges
Speaker 2: faster than basic backprop for smaller networks or certain types
Speaker 2: of problems, but computationally more intensive. It's like a more
Speaker 2: sophisticated engine for finding those optimal.
Speaker 1: Weights and thinking about implementation. The book uses Java. How
Speaker 1: does it structure things?
Speaker 2: It takes a nice object oriented approach. You have classes
Speaker 2: like neuron layer.
Speaker 1: Neuralnet modeling the concepts directly.
Speaker 2: In code exactly. Neural objects have their weights, bias, activation function,
Speaker 2: layer objects, group neurons. Neural net puts the layers together,
Speaker 2: makes the theory very concrete and practical if you're coding
Speaker 2: it up.
Speaker 1: Cool. So we have these powerful MLPs trained with backprop.
Speaker 1: What kinds of real world problems do they tackle? The
Speaker 1: book mentions two main classes.
Speaker 2: Right, broadly speaking, classification and regression.
Speaker 1: Classification is putting things into.
Speaker 2: Category exactly, assigning input record to one of several pre
Speaker 2: defined classes, like is this email spam or not spam?
Speaker 2: Is this tumor malignant or benign? Predicting a student's major
Speaker 2: based on grades.
Speaker 1: How does the network output work for that?
Speaker 2: Multiple outputs often, yeah, you might have one output neuron
Speaker 2: per class. The neuron with the highest activation wins and
Speaker 2: determines the predicted class.
Speaker 1: And evaluating classification, you need specific metrics. The book mentions
Speaker 1: confusion matrices.
Speaker 2: Absolutely, a confusion matrix shows you not just the overall accuracy,
Speaker 2: but what kind of errors the network is making. How
Speaker 2: many actual positives were predicted as negative false negatives? How
Speaker 2: many actual negatives were predicted as positive false.
Speaker 1: Positives, which leads to metrics like sensitivity and specificity.
Speaker 2: Right, Sensitivity is the true positive rate, how well it
Speaker 2: identifies actual positives. Spensificity is the true negative rate, how
Speaker 2: well it identifies actual negatives. Super important in medical diagnosis,
Speaker 2: for example, you need to know.
Speaker 1: Both makes sense and the other class was regression.
Speaker 2: Regression is about predicting a continuous numerical value. You not
Speaker 2: a category.
Speaker 1: It's like predicting house prices or stock values exactly.
Speaker 2: Finding a function that maps inputs to a number, predicting
Speaker 2: best ticket prices based on root, time of day, et cetera.
Speaker 2: That's a regression task.
Speaker 1: The book gives some concrete examples, right, Yeah.
Speaker 2: Some good ones. A university enrollment status predictor that's classification
Speaker 2: takes gender grades, predicts a fill enroll, and the medical
Speaker 2: ones disease diagnosis specifically, they look at breast cancer and
Speaker 2: diabetes data sets using various medical inputs to predict the diagnosis.
Speaker 2: Again classic classification.
Speaker 1: And they show how their classification class helps analyze this
Speaker 1: with those confusion matrices.
Speaker 2: Yeah, it calculates the matrix. Sensitivity, specificity, accuracy really helps
Speaker 2: you understand the performance beyond just a single accuracy number.
Speaker 2: It's fascinating seeing how networks find patterns in that complex
Speaker 2: medical data.
Speaker 1: Definitely now shifting gears slightly. What about that other learning paradigm,
Speaker 1: unsupervised learning? Where does that shine?
Speaker 2: Right? Unsupervises about discovery and a prime example the book
Speaker 2: covers is self organizing maps or SOMs, also called Cohona networks.
Speaker 1: What's unique about SOMs?
Speaker 2: They map high dimensional input data onto a lower dimensional grid,
Speaker 2: usually one D year two D. They create a kind
Speaker 2: of map where similar inputs activate neurons that are close
Speaker 2: to each other on the map.
Speaker 1: So it organizes the data visually pretty much.
Speaker 2: It preserves the topology of the data. You get these
Speaker 2: clusters forming naturally on the map, showing relationships in the data.
Speaker 2: It's great for visualization and exploration.
Speaker 1: How do they learn without labels? What's the mechanism?
Speaker 2: It's based on competitive learning, sometimes called winner takes all, though.
Speaker 1: It's a bit more nuanced winner takes all.
Speaker 2: When an input is presented, all neurons compute their output,
Speaker 2: but only one winner neuron, the one whose weight vector
Speaker 2: is closest to the input vector, gets strongly activated. Okay,
Speaker 2: then that winterer neuron and its neighbors on the map
Speaker 2: grid update their weights to become even closer to that
Speaker 2: input vector.
Speaker 1: Ah, so neighboring neurons learn similar things exactly.
Speaker 2: That's how the map organizes itself over time. Different regions
Speaker 2: of the map specialize in responding to different types of inputs,
Speaker 2: forming those clusters or centroids.
Speaker 1: Cool. What are some examples the book uses for this?
Speaker 2: One is clustering animals, giving the network characteristics as it
Speaker 2: have fur is a terrestrial? Does it have mammary glands?
Speaker 2: And letting the SAM group the animals based on similarity
Speaker 2: without any predefined labels like mammal or reptile.
Speaker 1: It discovers the categories right.
Speaker 2: Another big one is customer profiling, analyzing transaction data maybe demographics,
Speaker 2: to find hidden segments or clusters of customers.
Speaker 1: That sounds commercially very valuable.
Speaker 2: Hugely businesses use it to understand their customer based better
Speaker 2: target marketing, etc. But it often requires careful data preprocessing.
Speaker 1: Because the network needs numbers.
Speaker 2: Yeah, you need to convert different data types of numerical
Speaker 2: categorical like gender or city into a format the network
Speaker 2: can handle. That's often a big part of the job.
Speaker 1: Okay, so we have supervised for prediction, unsupervised for discovery.
Speaker 1: What about tasks that combined aspects like pattern recognition.
Speaker 2: Pattern recognition, especially something like optical character recognition OCR is
Speaker 2: a great example. It often involves elements of.
Speaker 1: Both recognizing handwriting or typed text.
Speaker 2: Exactly. The book has a nice OCR case study recognizing
Speaker 2: handwritten digits zero through nine.
Speaker 1: How did they represent the digits for the network?
Speaker 2: They use simple five y five pixel grayscale images. Each
Speaker 2: image is flattened into a vector of twenty five pixel inputs.
Speaker 1: So the image becomes numerical data.
Speaker 2: Precisely, that transformation from visual information to numbers the network
Speaker 2: and process is fundamental. Then typically you train it using
Speaker 2: supervised learning. Show it lots of examples of three images
Speaker 2: labeled as three four images labels four, and so on.
Speaker 1: Okay, Now throughout these examples, something you mentioned earlier seems important.
Speaker 1: The trial and error aspect of designing these networks.
Speaker 2: Oh, absolutely, it's rarely straightforward. The weather forecasting example they
Speaker 2: discuss in chapter five really highlights this. Oh, so they
Speaker 2: had to experiment empirically, try different network structures, different numbers
Speaker 2: of hidden neurons, different learning parameters, and crucially carefully select
Speaker 2: the training and test data sets, and.
Speaker 1: The goal isn't always just the lowest possible error on
Speaker 1: the training set.
Speaker 2: Not necessarily, this is a key point. Sometimes a network
Speaker 2: that achieves a slightly higher error say means squared error
Speaker 2: MESSE during training might actually perform better on the unseen
Speaker 2: test data.
Speaker 1: Better generalization exactly.
Speaker 2: They learned the underlying pattern better wasn't just overfitting to
Speaker 2: the training noise. They saw this in both the weather
Speaker 2: forecasting and the OCR digit recognition results. The network that
Speaker 2: generalized best wasn't always the one with the absolute rock
Speaker 2: bottom training MSc.
Speaker 1: So it's an iterative design process requires judgment.
Speaker 2: Very much so part science, part art maybe.
Speaker 1: And things can go wrong right. Common issues for.
Speaker 2: Sure, bad input selection, feeding the network irrelevant data, noisy
Speaker 2: data that obscures the patterns, choosing an unsuitable network structure,
Speaker 2: too simple or maybe overly.
Speaker 1: Complex, so optimization is key.
Speaker 2: Definitely, techniques exist to help, Like for input selection, you
Speaker 2: can analyze data correlation using something like the piercing coefficient
Speaker 2: to see which potential inputs are actually strongly related to
Speaker 2: the output you're trying to predict. Helps weed out the noise.
Speaker 1: Makes sense, and if you have tons of inputs, like
Speaker 1: from high res images.
Speaker 2: Then dimensionality reduction techniques become vital ways to compress the
Speaker 2: input data, capture the most important information in fewer dimensions,
Speaker 2: making the learning task more manageable without losing too much signal.
Speaker 1: So it sounds like mastering neural networks takes patience, experimentation
Speaker 1: in const of refinement.
Speaker 2: Yeah, it's not usually a one shot deal. You build,
Speaker 2: you test, you tweak, you learn for the results and iterate.
Speaker 1: Well, this has been an incredible deep dive. We've really
Speaker 1: unpacked the core pieces artificial neurons, weights, bias, activation, functions, layers.
Speaker 2: Uh huh, the building blocks.
Speaker 1: Explore how they learn supervised with teacher, unsupervised, discovering patterns
Speaker 1: on their own.
Speaker 2: With algorithms like backpropagation making the complex learning possible.
Speaker 1: And competitive learning driving that self organization in essoms. Yeah,
Speaker 1: and we saw their versatility forecasting, diagnosis, clustering, even reading handwriting.
Speaker 2: It really shows they're more than just algorithms. They're inspired
Speaker 2: by life, finding knowledge in ways we might not expect,
Speaker 2: almost like extensions of our own ways of finding patterns.
Speaker 1: Absolutely so, given everything we've discussed, their ability to self
Speaker 1: organize adapt, create internal representations. Here's a final thought for you.
Speaker 1: What new frontiers of human knowledgement these networks unlocked that
Speaker 1: maybe we can't even conceive of yet.
Speaker 2: That is the big question, isn't it.
Speaker 1: Thank you for joining us on this deep dive.