Speaker 1: Welcome to the deep dive. Our mission is pretty simple.
Speaker 1: You give us the source material and we jump right
Speaker 1: in to pull out the essential knowledge, basically giving you
Speaker 1: a shortcut to getting up to speed on a topic.
Speaker 2: Exactly. We dig into the pages, the research, whatever you
Speaker 2: send us, and we extract those key insights, maybe some
Speaker 2: surprising details, and the stuff you can actually use.
Speaker 1: And for this deep dive, we're tackling excerpts from Applied
Speaker 1: deep Learning, a case based approach to understanding deep neural
Speaker 1: networks by Umberco Miclucci.
Speaker 2: Right, and this source material it really gets into the
Speaker 2: nuts and bolts how neural networks function, how they actually learn,
Speaker 2: and then the practical side like building and training them
Speaker 2: using tools like TensorFlow.
Speaker 1: So our goal here is simple walk you through these
Speaker 1: core deep learning ideas straight from this applied angle, making
Speaker 1: it hopefully clear and digestible. Let's get started.
Speaker 2: Okay, let's start where the book does really at the
Speaker 2: foundation computational graphs.
Speaker 1: Right, but before you even talk about neurons, the book
Speaker 1: sets it up with this idea. A computational graph is well,
Speaker 1: it's just a way to map out and organized calculations. Right.
Speaker 1: You define the steps how the data.
Speaker 2: Flows, And that's precisely how libraries like TensorFlow work. You
Speaker 2: build this graph defining all the operations, additions, multiplications, activation, functions, whatever.
Speaker 2: Then TensorFlow takes that graph and runs it, executing everything
Speaker 2: very efficiently.
Speaker 1: Okay, so what are the basic building blocks for these
Speaker 1: graphs and TensorFlow? According to the source, Well.
Speaker 2: The most fundamental thing is the tensor itself. You can
Speaker 2: think of it basically as a multidimensional array, very much
Speaker 2: like NUMPI arrays actually, and it's rank just tells you
Speaker 2: how many dimensions it has. Ranked zero is a centile number,
Speaker 2: rank one is a vector ranked to a matrix, and
Speaker 2: so on.
Speaker 1: Got it. Multidimensional arrays holding the data. But what about
Speaker 1: the pieces that do the work or change during training?
Speaker 2: A right, So you have different kinds of nodes. There's
Speaker 2: tf dot variable. These are for parameters that the network
Speaker 2: needs to update as it learns. Think weights and biases,
Speaker 2: the classic examples.
Speaker 1: They vary, makes sense, they vary. What about tf dot placeholder? Then?
Speaker 2: Placeholders are different. They're like entry points into the graph.
Speaker 2: You use them to feed in data from outside when
Speaker 2: you actually run the calculation. They hold values that are
Speaker 2: fixed during one run, but you might change them between runs.
Speaker 1: Like feeding in a batch of training data, or maybe
Speaker 1: the learning rate.
Speaker 2: Exactly input data batches are the prime example, or maybe
Speaker 2: a learning rate you're setting manually.
Speaker 1: Okay, so variables change during a run, placeholders get new
Speaker 1: values between runs, and tf dot constant.
Speaker 2: That sounds easy. It's just for a value that stays
Speaker 2: the same, always, never changes.
Speaker 1: And TensorFlow runs this whole graph thing using something called
Speaker 1: a session.
Speaker 2: That's right. You define the graph structure first, then you
Speaker 2: need a TensorFlow session to actually execute the operations in
Speaker 2: that graph. The book makes a distinction between session dot
Speaker 2: run and tensor dot evil. Dot run lets you execute
Speaker 2: specific nodes you list, whereas evil is more like a
Speaker 2: shortcut you called directly on a tensor or variable. It
Speaker 2: just runs that specific thing within the current session and
Speaker 2: gives you its value back.
Speaker 1: So we've got the structure for doing calculations. Now, what's
Speaker 1: the book says the smallest unit in deep learning.
Speaker 2: That would be the single neuron. It's the fundamental building block,
Speaker 2: kind of inspired by biological neurons, but much simpler as
Speaker 2: the book describes it. It takes several numerical inputs your data
Speaker 2: features usually does some processing and spits out a single number.
Speaker 1: And that processing involves multiplying inputs by weights, adding a bias,
Speaker 1: and then hitting it with an activation function.
Speaker 2: Precisely, the weights give importance to different inputs, the bias
Speaker 2: shifts the result, and that activation function that's super important.
Speaker 2: It introduces nonlinearity because if you just stacked layers of
Speaker 2: linear operations, the whole network would still just be doing
Speaker 2: a linear transformation. Nonlinearity lets it learn complex stuff.
Speaker 1: What activation functions does the book focus on?
Speaker 2: It covers some key ones. There's the Sigmoorid function, squashes
Speaker 2: everything between zero and one, historically used a lot for
Speaker 2: like binary classification. Then the identity function, which is just
Speaker 2: linear output equals input, and the really common one now
Speaker 2: re lu the rectified linear unit, which is output is
Speaker 2: just the input if it's positive and zero if it's negative.
Speaker 2: Simple but works very well.
Speaker 1: Now the book points that are really practical. Kind of
Speaker 1: tricky thing with sigmoid. Doesn't it something that can cause problems?
Speaker 2: Ah? Yeah, this is a classic theory versus practice issue. Mathematically,
Speaker 2: sigmoid gets really close to zero or one, but never
Speaker 2: quite touches them. But computers use floating point numbers. So
Speaker 2: for really big positive or negative inputs, the result can
Speaker 2: actually get rounded to exactly zero or one.
Speaker 1: And why is that a problem? Where does it bite you?
Speaker 2: It bites you when you calculate the cost function, Especially
Speaker 2: in classification, you often need the logarithm of the output
Speaker 2: or log one output. If the output is exactly zero
Speaker 2: or one, you're trying to calculate log zero, which is undefined.
Speaker 1: Leading to those nan values not a number exactly.
Speaker 2: You see nan popping up in your training loss, that's
Speaker 2: often a clue, could be the sigmoid issue, maybe related
Speaker 2: to data scaling or initial weights being too large. It's
Speaker 2: a debugging flag.
Speaker 1: That's a super useful tip, watch eff nance. Another practical
Speaker 1: point the book makes is about speed right computational efficiency.
Speaker 2: Absolutely, it has this great comparison it shows implementing something
Speaker 2: like ReLU using numb pies built in matrix operations versus
Speaker 2: just writing a standard Python for loop.
Speaker 1: I think I remember seeing that graphic. The difference was huge,
Speaker 1: wasn't it massive?
Speaker 2: Something like one hundred times faster in their example for
Speaker 2: a big array. Wow, And it really drives home why
Speaker 2: we use libraries like NUMPI or TensorFlow. They push these
Speaker 2: operations down to low level code like see and use vectorization.
Speaker 2: They process chunks of data all at once, which is
Speaker 2: way faster than Python looping through element by element. Understanding
Speaker 2: that efficiency is key to why deep learning scales.
Speaker 1: Okay, so we have the neuron the basic unit, but
Speaker 1: how does it or whole network of them actually learn anything? Right?
Speaker 2: So, learning here means finding the best possible values for
Speaker 2: the network's parameters, the weights and biases. Best means the
Speaker 2: values that make the network predictions match the true answers
Speaker 2: as closely as possible.
Speaker 1: You measure that closeness using the cost function.
Speaker 2: Precisely, the cost function gives you a number that says
Speaker 2: how wrong the network is. Lower cost means better performance
Speaker 2: on the training data.
Speaker 1: And the main algorithm for lowering that cost is gradient
Speaker 1: descent YEP.
Speaker 2: Gradient descent is the workhorse YEP. It works by calculating
Speaker 2: the gradient basically the slope of the cost function with
Speaker 2: respect to each weight and bias. Then it adjusts the
Speaker 2: parameters slightly in the opposite direction of the gradient. It's
Speaker 2: like taking a small step downhill on the cost landscape,
Speaker 2: always trying to find the lowest point and.
Speaker 1: The size of that step.
Speaker 2: That's the learning rate exactly. The learning rate, often written
Speaker 2: as gamma or alpha, is a really critical hyperparameter. It
Speaker 2: dictates how biggest step you take down hell each time.
Speaker 2: A small learning rate means tiny, maybe cautious steps. Large
Speaker 2: learning rate means big, bold steps, which sounds good.
Speaker 1: But that's where the quirks come in. As the book
Speaker 1: puts it, what happens if it's too big.
Speaker 2: If it's too big, you can overshoot the minimum point
Speaker 2: in the cost landscape. You jump right over it. You
Speaker 2: might end up bouncing back and forth, oscillating around the minimum,
Speaker 2: or even flying off entirely and diverging. The cost gets
Speaker 2: worse instead of better.
Speaker 1: Yeah, I can picture that like rolling a ball down
Speaker 1: a hill too fast and it rolls right across the
Speaker 1: valley and up the other side.
Speaker 2: That's a good analogy. Finding that just right. Learning rate
Speaker 2: is often one of the first big challenges when you're
Speaker 2: training a network.
Speaker 1: Okay, so individual neurons learn by minimizing cost with gradient descent,
Speaker 1: but the real power comes when you connect lots of
Speaker 1: them together in feed forward neural networks.
Speaker 2: That's right. You arrange neurons in layers. You have an
Speaker 2: input layer, one or more hidden layers in the middle,
Speaker 2: and then an output layer. And in a standard fully
Speaker 2: connected network, every neuron in one layer passes it output
Speaker 2: to every neuron in the very next layer, and.
Speaker 1: The calculations just flow forward layer by layer, Which is
Speaker 1: why those matrix operations we talked about are so useful. Right,
Speaker 1: processing a whole layer at.
Speaker 2: Once exactly that equations zwx plus B that's not just
Speaker 2: one neuron. W is a matrix of all weights for
Speaker 2: the layer, x is a matrix of all inputs or
Speaker 2: previous layer outputs for a whole batch, and B is
Speaker 2: the bias factor. It calculates everything for the layer in
Speaker 2: one go, super efficient, but.
Speaker 1: Building these bigger, deeper networks introduces a huge challenge. The
Speaker 1: book really digs into overfitting.
Speaker 2: Oh yeah. Overfitting is a constant concern. It's when your
Speaker 2: model gets too good at the training data. It doesn't
Speaker 2: just learn the underlying patterns, it starts memorizing the specific
Speaker 2: training examples, including all the random noise and quirks, so.
Speaker 1: It eases the practice test but fails the real.
Speaker 2: Exam perfect analogy. It performs great on data it's seen,
Speaker 2: but poorly on new unseen data because it didn't learn
Speaker 2: the general rules.
Speaker 1: And the opposite is underfitting or high bias, where the
Speaker 1: model is too simple it can't even capture the training
Speaker 1: data patterns well.
Speaker 2: Right, and the book stresses that the very first step
Speaker 2: in fighting overfitting is being able to spot it, which
Speaker 2: means you have to split your.
Speaker 1: Data into a training set and a development set or
Speaker 1: a validation set exactly.
Speaker 2: You train the model only on the training set, but
Speaker 2: periodically you check its performance on the development dove set,
Speaker 2: which it hasn't been trained on.
Speaker 1: And if the training air keeps going down but the
Speaker 1: dev air stops improving or starts going.
Speaker 2: Up, bingo, that's your alarm bell. The model is starting
Speaker 2: to overfit the training data. The dev set acts like
Speaker 2: your early warning system.
Speaker 1: Now going back to grading descent for training these networks,
Speaker 1: there are different flavors of.
Speaker 2: It, yes, because using the entire data set for every
Speaker 2: single weight update that's called batch gradient descent can be
Speaker 2: incredibly slow and memory intensive. For large data sets. Batch
Speaker 2: GD gives you a very accurate gradient estimate, but the
Speaker 2: updates are infrequent.
Speaker 1: So the alternative is stochastic gradient descent or SGD.
Speaker 2: Right, SGD goes to the other extreme, it updates the
Speaker 2: weights after looking at just one training example. This makes
Speaker 2: the updates very fast and frequent, but also very noisy
Speaker 2: or stochastic. The path towards the minimum jumps around a lot.
Speaker 2: That noise can sometimes help it escape shallow local minimum, though.
Speaker 1: In the most common approach sits in the middle. Mini
Speaker 1: batch gradient descent exactly.
Speaker 2: This is what people usually mean by SGD nowadays, even
Speaker 2: though it's technically minibatch. You calculate the gradient and update
Speaker 2: the weights based on a small batch maybe thirty two,
Speaker 2: sixty four, hundred and twenty eight examples. It's a compromise.
Speaker 2: You get smoother convergence than pure SGD, but much faster
Speaker 2: updates than batch GD. It leverages matrix operations efficiently.
Speaker 1: And that mini batch size is another one of those
Speaker 1: hyper parameters you have to.
Speaker 2: Choose yep and the book clarifies terminology. An iteration is
Speaker 2: usually one pass through a mini batch and one weight update,
Speaker 2: and epoch is one full pass through the entire training
Speaker 2: data set, so many iterations per at BOCH.
Speaker 1: The book also mentions something about starting weights weight initialization
Speaker 1: being important, very important.
Speaker 2: It's not just setting them to zero, which causes problems.
Speaker 2: How you initialize them can seriously affect how quickly or
Speaker 2: even if the network trains successfully. Bad initialization can lead
Speaker 2: to exploding gradients getting huge, or vanishing gradients getting tiny,
Speaker 2: or those nan values again.
Speaker 1: So what does the book suggest?
Speaker 2: It often uses something like TFT truncated normal with a
Speaker 2: small standard deviation, maybe zero point one. This draws initial
Speaker 2: weights from a normal distribution but cuts off extreme values.
Speaker 2: The idea is to start with small random weights to
Speaker 2: break symmetry but avoid large values.
Speaker 1: Initially, let's talk architecture. Why are deeper networks like with
Speaker 1: multiple hidden layers often better than just one really wide
Speaker 1: hidden layer.
Speaker 2: Well, empirically, deeper networks often seem to need fewer neurons
Speaker 2: in total to get the same level of performance as
Speaker 2: a very wide but shallow network, but perhaps more importantly,
Speaker 2: they often generalize better. The thinking is that layers learn
Speaker 2: features higher archically. How So, like the first layer might
Speaker 2: learn simple things like edges or corners from pixels, the
Speaker 2: next layer combines those into shapes, The layer after that
Speaker 2: combines shapes into objects, and so on. It builds up complexity.
Speaker 1: So potentially a more sophisticated understanding of the data. But
Speaker 1: the book is clear right there's no magic formula for
Speaker 1: the number of layers or neurons.
Speaker 2: Absolutely not. It's very much problem dependent. Finding the right
Speaker 2: architecture usually involves a lot of trial and error experimentation,
Speaker 2: maybe drawing on architectures known to work well for similar problems.
Speaker 1: Okay, we've got network structure learning algorithms. How to spot overfitting?
Speaker 1: What about making the training itself better, faster, more reliable.
Speaker 2: One key area is tweaking the learning rate during training
Speaker 2: instead of just fixing it using learning rate decay is common.
Speaker 1: So starting higher and then reducing it over time.
Speaker 2: Exactly, you might start with a relatively large learning rate
Speaker 2: to make quick progress when you're far from the solution.
Speaker 2: Then as the training goes on and you get closer
Speaker 2: to the minimum, you gradually decrease the learning rate to
Speaker 2: take smaller, finer steps. This helps avoid that oscillation we
Speaker 2: talked about and allows for more precise convergence.
Speaker 1: What are common ways to decay it?
Speaker 2: The book mentions things like in verse time decay or
Speaker 2: exponential decay, where the rate decreases smoothly over training iterations.
Speaker 2: It's usually tied to the iteration count, not just the
Speaker 2: epoch count.
Speaker 1: And then there are fancier optimization algorithms beyond just basic
Speaker 1: gradient descent with decay.
Speaker 2: Oh yes, these aim to speed up training and make
Speaker 2: it more robust. Many of them rely on the idea
Speaker 2: of exponentially weighted averages.
Speaker 1: Okay, what's the intuition there.
Speaker 2: Instead of just using the gradient from the current mini batch,
Speaker 2: which can be noisy, these methods keep a running average
Speaker 2: of recent gradients. This average smooths out the noise and
Speaker 2: gives a better estimate of the true downhill direction. It
Speaker 2: helps the optimizer build up momentum to get through flat
Speaker 2: regions or damp down oscillations in narrow valleys of the
Speaker 2: cost function.
Speaker 1: So it's like smoothing out the bumps in the road,
Speaker 1: and that leads to optimizers life momentum RMS PROP.
Speaker 2: ADAM exactly those momentum adds a fraction of the previous
Speaker 2: update step to the current one. RMSProp adapts the learning
Speaker 2: rate for each parameter individually based on the average size
Speaker 2: of recent ingredients for that parameter, and ADAM, as the
Speaker 2: source suggests, kind of combines the ideas of momentum and
Speaker 2: RMS PROP. It's often the default go to optimizer because
Speaker 2: it tends to work well across a wide range of
Speaker 2: problems with relatively little tuning, usually faster and better.
Speaker 1: The book says, now, let's circle back to fighting overfitting.
Speaker 1: We mentioned the train dev split. What about techniques built
Speaker 1: into the training process itself? Regularization right.
Speaker 2: Regularization methods are specifically designed to prevent overfitting and help
Speaker 2: the model generalize better to data it hasn't seen before.
Speaker 1: The book talks about E two and E to one regularization.
Speaker 1: What's the difference?
Speaker 2: Both work by adding a penalty term to the cost function.
Speaker 2: This penalty is based on the size of the network's weights.
Speaker 2: Under two, regularization, sometimes called weight decay, adds a penalty
Speaker 2: proportional to the sum of the squares of all the weights.
Speaker 2: It pushes weights towards zero, but not usually exactly zero.
Speaker 2: It encourages smaller whites overall, making the model simpler. And
Speaker 2: one home one regularization adds a penalty proportional to the
Speaker 2: sum of the absolute values of the weights. It also
Speaker 2: pushes weights towards zero, but because of the math involved
Speaker 2: the shape of the penalty function, it tends to make
Speaker 2: many weights exactly zero, so.
Speaker 1: It leads to sparser models where some connections are effectively
Speaker 1: turned off exactly.
Speaker 2: L one can be useful for feature selection in a
Speaker 2: way because it zero's out weights for less important inputs.
Speaker 1: Then there's dropout, which sounds completely different.
Speaker 2: It is quite different. Yeah, dropout is a very clever
Speaker 2: and widely used technique. During each training iteration, you randomly
Speaker 2: drop out, temporarily remove a fraction of the neurons in
Speaker 2: certain layers.
Speaker 1: Just randomly ignore them for that update.
Speaker 2: Yep, for that one mini batch calculation, those neurons and
Speaker 2: their connections are just gone. In the next iteration, a
Speaker 2: different random sat might be dropped.
Speaker 1: How does that help?
Speaker 2: It prevents the network from becoming too reliant on any
Speaker 2: single neuron or specific pathway. Since any neuron might disappear,
Speaker 2: the network is forced to learn more robust, redundant representations.
Speaker 2: It's kind of like training a large ensemble of slightly
Speaker 2: different networks all at once.
Speaker 1: Yeah, that makes sense. Forces redundancy. The source notes that
Speaker 1: can make the training costs jump around.
Speaker 2: A bit more, though, yes, because the network structure is
Speaker 2: literally changing slightly on every iteration due to the randomness.
Speaker 2: So the training metric might look a bit noisier, but
Speaker 2: it often leads to much better generalization on the dev
Speaker 2: and test sets.
Speaker 1: Okay, so we've trained or model applied regularization, how do
Speaker 1: we really know if it's any good? Evaluation seems critical.
Speaker 2: Absolutely crucial, and just looking at training error isn't enough.
Speaker 2: The book brings up human level performance HLP and Bayes
Speaker 2: error in for tasks humans are good at, like recognizing
Speaker 2: images or transcribing speech. HLP can be a practical estimate
Speaker 2: for the theoretical best possible error. The bees aer Beyes
Speaker 2: error is the irreducible error rate. No model, however, perfect
Speaker 2: could do better due to inherent ambiguity or noise in
Speaker 2: the data itself.
Speaker 1: So knowing the HLP gives you a target, like what's
Speaker 1: potentially achievable?
Speaker 2: Exactly? If human error on a task is say one percent,
Speaker 2: and your model has ten percent error, you know there's
Speaker 2: likely a lot of room for improvement. If your model
Speaker 2: is at one point five percent, maybe you're getting close
Speaker 2: to the limit. The book uses MS digit recognition, where
Speaker 2: HLP is cited around zero point two percent error.
Speaker 1: Okay, hlt bese er is the theoretical floor. How do
Speaker 1: we diagnose our model's specific shortcomings?
Speaker 2: The book introduces a simple framework called the metric analysis
Speaker 2: diagram or MENE. It helps you pinpoint where the error
Speaker 2: is coming from by looking at different gaps.
Speaker 1: Let's walk through those gaps.
Speaker 2: Okay, First, gap bias or sometimes avoidable bias. This is
Speaker 2: the difference between the Bayes error or HLP and your
Speaker 2: training error. If this gap is large, it means you're
Speaker 2: model isn't even fitting the training data. Well, it's likely
Speaker 2: to simple underfitting, or the training algorithm itself isn't finding
Speaker 2: a good solution.
Speaker 1: Okay, so bias is about performance on data it's already
Speaker 1: seen relative to the best possible. What's the next gap?
Speaker 2: Variance? This is the difference between your training error and
Speaker 2: your development set error. If your training error is low
Speaker 2: but your DEV error is much higher, that's a classic
Speaker 2: sign of overfitting. The model learned the training data specifics,
Speaker 2: but isn't generalizing. High variance, and.
Speaker 1: There's potentially a third gap mentioned.
Speaker 2: Yes, overfitting on the dev set. This is the gap
Speaker 2: between your doveset error and your error on a completely
Speaker 2: separate test set. If you tune your hyperparameters extensively based
Speaker 2: on the debset results, you might inadvertently make your model
Speaker 2: perform well, specifically on that deb set, but it might
Speaker 2: not generalize as well to totally new data.
Speaker 1: AHH, so you've sort of used up the deb set
Speaker 1: for unbiased evaluation by tuning on it too much. That's
Speaker 1: why you need that final untouched.
Speaker 2: Test set precisely keep the test set sacred until the
Speaker 2: very end for a final honest assessment.
Speaker 1: This all really highlights how crucial that initial data split
Speaker 1: is train dev.
Speaker 2: Test, and the book emphasizes a critical point. Your dev
Speaker 2: and test sets must reflect the real world data distribution.
Speaker 2: Your model will actually see.
Speaker 1: What kinds of problems happen if they don't well.
Speaker 2: A big one is unbalanced classes. The book mentions examples
Speaker 2: like detecting rare fraud or maybe identifying only certain digits
Speaker 2: in MNIST. If say, fraud is only a point one
Speaker 2: percent of your real data, but your dev set is
Speaker 2: balanced fifty to fifty, your devset accuracy won't tell you
Speaker 2: how the model does on the real skewed distribution, right.
Speaker 1: Because getting ninety nine point nine percent accuracy by just
Speaker 1: always predicting not fraud would look great on the real data,
Speaker 1: but terrible on the balanced DEV set or vice versa.
Speaker 2: Exactly so, especially with unbalanced data. The book stresses looking
Speaker 2: beyond plane accuracy. You need metrics like the confusion matrix.
Speaker 1: Which shows true positives, false positives, true negatives, false negatives right.
Speaker 2: And from that you calculate precision how many of the
Speaker 2: positive predictions were actually positive, and recall how many of
Speaker 2: the actual positives did you find? And often the F
Speaker 2: one score, which combines precision and recall into one.
Speaker 1: Number, gives you much more nuanced picture of performance. What
Speaker 1: about when the training data itself is just different from
Speaker 1: the evaluation data.
Speaker 2: That's another major challenge. Maybe you trained on high quality images,
Speaker 2: but you need to evaluate on blurry phone pictures, or
Speaker 2: trained on data from one country deploying in another, performance
Speaker 2: will almost certainly drop if the distributions don't match. You
Speaker 2: need to be aware of that potential mismatch.
Speaker 1: For situations with smaller data sets, the book brings up
Speaker 1: kfold cross validation.
Speaker 2: Yes, it's a really useful technique. When you can't afford large,
Speaker 2: separate dev and test sets, you split your data into
Speaker 2: say five or ten folds. Then you train the model
Speaker 2: five or ten times. Each time, you hold out one
Speaker 2: fold for validation and train on the remaining folds. Then
Speaker 2: you average the validation performance across all.
Speaker 1: The folds, so you get a more robust estimate of performance,
Speaker 1: less dependent on one specific split exactly.
Speaker 2: It gives a better sense of generalization and helps check
Speaker 2: for overfitting, especially with limited data.
Speaker 1: Okay, evaluation tells us what's wrong. To fix things, we
Speaker 1: often need to adjust those settings. We don't learn directly
Speaker 1: the hyper parameters.
Speaker 2: Hyper parameter tuning. It's about finding the best values for
Speaker 2: things like the learning rate, the number of layers neurons
Speaker 2: per layer, which optimizer to use, the strength of regularization
Speaker 2: like that L two penalty, the mini batch size, how
Speaker 2: many epochs to train for the weight initialization method. The
Speaker 2: list goes on, and.
Speaker 1: The book frames. This is trying to optimize a black
Speaker 1: box function. What does that mean?
Speaker 2: It means you can't just calculate a derivative to find
Speaker 2: the best setting. The function takes hyper parameters as input
Speaker 2: and its output is the model's performance, like defset accuracy
Speaker 2: after training. But evaluating that function actually training the network
Speaker 2: with those settings is computationally expensive, often taking hours or days,
Speaker 2: and you have potentially many hyper parameters. So the search
Speaker 2: space is huge.
Speaker 1: So what are the basic strategies for searching this space.
Speaker 2: The simplest are grid search and random search. Grid search
Speaker 2: is systematic. You define a grid of possible values for
Speaker 2: each hyper parameter and try every single combination.
Speaker 1: Which sounds thorough, but the book warns about the cursive
Speaker 1: dimensionality right.
Speaker 2: Absolutely, If you have even just a few hyper parameters
Speaker 2: with several values each, the total number of combinations explodes.
Speaker 2: It becomes computationally infeasible very.
Speaker 1: Quickly, so random search.
Speaker 2: Random search often works better in practice, especially in high
Speaker 2: dimensional spaces. You define ranges for your hyper parameters, and
Speaker 2: then you just sample random combinations within those ranges. The
Speaker 2: insight is that usually only a few hyper parameters really
Speaker 2: dominate performance. Random search has a better chance of landing
Speaker 2: on good values for those important ones compared to grid search,
Speaker 2: which waste a lot of time testing combinations where unimportant
Speaker 2: parameters vary.
Speaker 1: The book makes a really key point about how to
Speaker 1: sample certain parameters like the learning rate, not linearly.
Speaker 2: Yes, this is crucial for parameters like learning rates or
Speaker 2: regularization strengths that often work best across different orders of
Speaker 2: magnitude like zero point one zero one point zero one
Speaker 2: point zero zero one, Sampling them on a logarithmic scale
Speaker 2: is much more effective. Why is that if you sample
Speaker 2: learning rate linearly between say zero point zero zero zero
Speaker 2: one point one, most of your samples will be clustered
Speaker 2: up nearer zero point one. You'll barely test the smaller values.
Speaker 1: Ah, because the range point Zerolier point at one point
Speaker 1: one is much wider than point zero zero zero zero
Speaker 1: one point zero zero one on a linear scale.
Speaker 2: Right, But if you sample uniformly on a log scale,
Speaker 2: maybe by sampling an exponent are uniformly between mio four
Speaker 2: nine oh one and using ten r as your learning rate,
Speaker 2: you distribute your search effort much more evenly across those
Speaker 2: critical orders of magnitude. You're just as likely to test
Speaker 2: values around point zero zero one as values around point
Speaker 2: zero one.
Speaker 1: That makes a lot of sense for finding those sweet spots. Yeah,
Speaker 1: does the book mention more sophisticated tuning methods.
Speaker 2: It briefly touches on things like Beaesan optimization. These are small,
Speaker 2: harder search strategies. They build a probabilistic model, like a
Speaker 2: Goshian process, of how hyper parameters relate to performance based
Speaker 2: on the trials run so far. Then they use that
Speaker 2: model to intelligently decide which combination of hyper parameters to
Speaker 2: try next balancing exploring areas they're uncertain about, versus exploiting
Speaker 2: areas that already look promising.
Speaker 1: Trying to learn the black box function to optimize it faster.
Speaker 2: That's the basic idea. Yeah, more complex, but potentially much
Speaker 2: more efficient than random search if evaluations are very expensive.
Speaker 1: Now we mostly talked about standard fully connected networks, but
Speaker 1: the book also covers specialized architectures for specific data types.
Speaker 2: Right because fully connected networks treat every input feature of
Speaker 2: the same and don't account for spatial or sequential structure
Speaker 2: in the data, that's not always ideal, which leads.
Speaker 1: Us to convolutional neural networks or CNNs.
Speaker 2: CNN's are king for grid like data, especially images. Their
Speaker 2: core operation is the convolution. You have these small filters
Speaker 2: called kernels that slide across the input image. Each kernel
Speaker 2: is designed, or rather learned, to detect a specific local
Speaker 2: pattern or feature, like an edge, a corner, or a texture.
Speaker 1: The book had those examples of simple kernels detecting horizontal
Speaker 1: or vertical lines right exactly.
Speaker 2: The network learns hierarchies of these features. CNNs also typically
Speaker 2: use pooling layers like max pooling.
Speaker 1: What do pooling layers do they.
Speaker 2: Reduce the size the spatial dimensions of the feature maps
Speaker 2: coming out of the convolutional layers. This makes the network
Speaker 2: computationally cheaper and importantly makes the learned features more robust
Speaker 2: to small shifts or distortions in the input image.
Speaker 1: Okay, so CNNs for grids like images, What about sequences
Speaker 1: like text or time series data?
Speaker 2: For that, you have recurrent mirural networks or RNNs. They're
Speaker 2: designed specifically for sequential data where the order matters. The
Speaker 2: key idea in an RNN is that it has a memory,
Speaker 2: a hidden state that gets updated at each step in
Speaker 2: the sequence and carries information from previous steps forward, so
Speaker 2: it can.
Speaker 1: Remember what happened earlier in the sentence or time series
Speaker 1: to help process the.
Speaker 2: Current element precisely. This allows rn ns to capture dependencies
Speaker 2: and context over time. The book mentions applications like speech recognition,
Speaker 2: machine translation, or even generating captions for images by processing
Speaker 2: image features sequentially.
Speaker 1: Very cool. It really shows how the architecture needs to
Speaker 1: match the data structure definitely. Now to kind of tie
Speaker 1: this all together, the book uses a real world research
Speaker 1: example and also emphasizes understanding the fundamentals yeah.
Speaker 2: It includes this interesting project where they use neural networks
Speaker 2: for calibrating an oxygen sensor. The traditional approach involved complex
Speaker 2: nonlinear physics equations. Instead, they just collected data sensor readings
Speaker 2: and corresponding known oxygen concentrations and trained a neural network
Speaker 2: to learn that mapping directly, letting.
Speaker 1: The network figure out the complex relationship from the data.
Speaker 2: Itself a great example of using deep learning for a
Speaker 2: practical regression problem, and then to really drive home the
Speaker 2: fundamentals the book as you build logistic or agression from.
Speaker 1: Scratch, using just numb pitt, no high level.
Speaker 2: Libraries, none of the deep learning framework magic. You have
Speaker 2: to manually code the sigmoid function, calculate the cost function,
Speaker 2: figure out the gredient, the derivatives, and implement the gradient
Speaker 2: des send update group yourself.
Speaker 1: Why put someone through that pain when TensorFlow exists.
Speaker 2: The book's point, and it's a good one, is that
Speaker 2: doing it manually gives you a much much deeper understanding
Speaker 2: and appreciation for what the frameworks are handling automatically. It
Speaker 2: forces you to grapple with the math and the algorithms directly.
Speaker 2: The book really believes that understanding how it works under
Speaker 2: the hood is essential if you want to effectively debug, optimize,
Speaker 2: or even just intelligently use these powerful tools.
Speaker 1: It pushes back on the idea that you could just
Speaker 1: call functions without knowing.
Speaker 2: What they do exactly. It argues that true effectiveness requires
Speaker 2: that deeper grasp, especially as models get more complex.
Speaker 1: Wow. Okay, we have really covered a ton of ground here.
Speaker 1: Following the source material, we started with computational graphs and
Speaker 1: the basic neuron.
Speaker 2: Moved into how they learned with cost functions, ingredient descent,
Speaker 2: including the different variations like mini batch.
Speaker 1: Scaled up to feed forward networks, tackle overfitting with train
Speaker 1: dev splits, and regularization techniques like L two, L one,
Speaker 1: and dropout.
Speaker 2: Looked at optimization strategies from learning rate decay to advanced
Speaker 2: optimizers like ATOM.
Speaker 1: Dived into evaluating models properly using things like HLP, THEMAD framework,
Speaker 1: precision recall F one, and thinking about data splits and distributions.
Speaker 2: Talked about the challenge of hyperparameter tuning using random search
Speaker 2: sampling on log scales.
Speaker 1: And even touched on specialized architectures like CNNs for images
Speaker 1: and RNNs for sequences.
Speaker 2: Plus that practical oxygen sensor example and the value of
Speaker 2: building from the ground up with NUMPI.
Speaker 1: Yeah, we've really tried to pull out the core concepts,
Speaker 1: the practical advice, and those interesting details from the Makealucchi
Speaker 1: book excerpts you provided. The aim was to give you
Speaker 1: that solid overview.
Speaker 2: A shortcut to being well informed on these deep learning
Speaker 2: fundamental based directly on the source.
Speaker 1: So here's a final thought to leave you with. Connecting
Speaker 1: some of these threads. We've seen how these models can
Speaker 1: get incredibly good at complex tasks, sometimes hitting or even
Speaker 1: beating human level performance like that point two percent error
Speaker 1: on mnist mentioned. But as the book strongly emphasizes, really
Speaker 1: mastering these tools, truly understanding them, debugging them, pushing their
Speaker 1: limits requires a serious grasp of the underlying math and algorithms.
Speaker 1: It kind of challenges that data science for everyone's narrative, right.
Speaker 2: So the question is, as these models become even more
Speaker 2: powerful and more accessible through libraries, does the barrier to
Speaker 2: true mastery actually get higher? Does it require more fundamental understanding,
Speaker 2: not less to use them effectively and responsibly.
Speaker 1: Something to think about as you continue your own journey
Speaker 1: with deep learning