Speaker 1: Welcome to the deep dive today. We're taking a shortcut
Speaker 1: really to understanding deep learning. It's everywhere, right, really is.
Speaker 1: So we've got some excerpts here from deep Learning with Python,
Speaker 1: and basically our mission is to pull out the core ideas,
Speaker 1: what's it doing, how does it work fundamentally, and maybe
Speaker 1: where it's headed?
Speaker 2: Yeah, getting the essence without you know, needing a PhD
Speaker 2: in maths exactly, avoid the overwhelm, and the book itself
Speaker 2: really tries to make it accessible. It pushes back against
Speaker 2: this idea that deep learning is some kind of like
Speaker 2: dark art. It highlights how Python and TensorFlow two specifically,
Speaker 2: plus the Caras community, how all that has made it
Speaker 2: practical for way more people.
Speaker 1: Right, So we want to give you listening a clear
Speaker 1: sense of what it's good for. It's limits to.
Speaker 2: Right absolutely, and the sort of standard steps people take
Speaker 2: to solve problems with it, you know, from computer vision
Speaker 2: to language stuff.
Speaker 1: And the author talks about how fast it's all.
Speaker 2: Moving, oh, incredibly fast. Yeah.
Speaker 1: Yeah, So for you listening, if you want to get
Speaker 1: a handle on complex top pretty quickly, maybe for work,
Speaker 1: maybe just because you're curious. This is aimed at giving
Speaker 1: you that solid foundation.
Speaker 2: Core ideas, the impact.
Speaker 1: We're looking for those aha moments, keeping it focused. So
Speaker 1: you walk away feeling like, okay, I get the big picture.
Speaker 2: Now sounds good. Where should we start?
Speaker 1: Okay, let's unpack it. What is deep learning and how
Speaker 1: does it fit in with you know, AI and machine learning?
Speaker 1: Those terms get thrown around a lot, they do.
Speaker 2: It's a good starting point. So AI, artificial intelligence in
Speaker 2: the really broad sense, is just automating tasks that usually
Speaker 2: need human smarts.
Speaker 1: Okay.
Speaker 2: It's a huge field actually, and older than many people think.
Speaker 2: Early AI, sometimes called symbolic AI, was very different, so
Speaker 2: it was more about programmers writing down tons and tons
Speaker 2: of rules by hand, building these big knowledge databases. The
Speaker 2: computer wasn't really learning from experience in the way we
Speaker 2: think of now.
Speaker 1: Ah, So no actual learning, just following pre written instructions,
Speaker 1: basically like those old chess program.
Speaker 2: Exactly like that, just rules. Machine learning then emerged as
Speaker 2: its own thing, where the focus shifted. The idea became
Speaker 2: can we build programs models that learn from data?
Speaker 1: Okay, that sounds more familiar.
Speaker 2: Yeah. The model finds patterns in the data itself, makes predictions,
Speaker 2: makes decisions, all without programmers explicitly telling it every single rule.
Speaker 1: Right, and deep learning? Where does that fit?
Speaker 2: Deep learning is a subfield within machine learning. It's defining
Speaker 2: characteristic is using these multi stage ways of learning representations
Speaker 2: of the data.
Speaker 1: Multi stage.
Speaker 2: Yeah, think of processing the data through many layers. Each
Speaker 2: layer learns to represent the data in a slightly more complex,
Speaker 2: more useful way based on the layer before it.
Speaker 1: Okay, so breaking it down, not trying to learn everything
Speaker 1: in one giant leap. The book uses three figures right
Speaker 1: to explain how it works.
Speaker 2: It does. Yeah, it's a good way to picture. First,
Speaker 2: the basic idea deep learning maps inputs to targets. It
Speaker 2: learns this mapping by just looking at lots and lots
Speaker 2: of examples.
Speaker 1: Well, show it cat pictures and dog pictures.
Speaker 2: Exactly and tell it which is which. That's the input
Speaker 2: in the image and the target the label cat or dog. Second,
Speaker 2: this mapping isn't direct. The data flows through a deep
Speaker 2: sequence of simple transformations the layers you mentioned precisely. These
Speaker 2: layers are like steps in an assembly line. Each one
Speaker 2: does something relatively simple to the data it receives. And
Speaker 2: the third point, crucially, these transformations, these operations, the layers
Speaker 2: perform they aren't hand coded by a programmer. The model
Speaker 2: learns what transformations are useful by seeing all those examples
Speaker 2: during training.
Speaker 1: Okay, learned transformations. That feels like the core of it,
Speaker 1: doesn't it. It figures out what features matter on its own.
Speaker 2: That's the magic.
Speaker 1: Yeah, And this figuring out happens in what the book
Speaker 1: calls the training loop. Can you walk us through that?
Speaker 1: What's happening there?
Speaker 2: Okay? The training loop. So when you first create a network,
Speaker 2: it's internal settings that these numbers called weights are just
Speaker 2: at randomly small random numbers.
Speaker 1: Usually, so it knows nothing. Basically, its first guesses are
Speaker 1: a while.
Speaker 2: Pretty much guaranteed to be wrong, yeah, which means it
Speaker 2: will have a high loss score. The loss is just
Speaker 2: a number that measures how far off the network's predictions
Speaker 2: are from the actual targets. High loss means very wrong.
Speaker 1: Like static on a radio you haven't tuned.
Speaker 2: Yet, good analogy, lots of static initially, but then for
Speaker 2: each example you show it during training.
Speaker 1: Like one cap picture, right.
Speaker 2: It makes a prediction. Yeah, it calculates the loss how
Speaker 2: wrong it was for that picture, And then comes the
Speaker 2: clever part, using calculus, specifically the gradient.
Speaker 1: Gradient sounds technical it is a.
Speaker 2: Bit, but think of it like this. The gradient tells
Speaker 2: you the direction of steepest increase in the loss, like
Speaker 2: which way is more wrong?
Speaker 1: Okay?
Speaker 2: So the optimization algorithm, usually some form of gradient descent,
Speaker 2: takes that information and adjusts the weights slightly in the
Speaker 2: opposite direction, the direction that would have made the loss
Speaker 2: a tiny bit smaller for that one example.
Speaker 1: Ah, So it nudges the weights downhill towards less error.
Speaker 2: Exactly, it takes a small step downhill on the air landscape.
Speaker 1: And it does this over and over for every example, yep.
Speaker 2: For every example in your training data, usually in small badges,
Speaker 2: and you repeat this process over the entire data set
Speaker 2: multiple times. Each full pass through the data set is
Speaker 2: called an epoch epoch, got it, And as you go
Speaker 2: through more and more epochs, tweaking the waves after each badge,
Speaker 2: the overall loss score gradually goes.
Speaker 1: Down the static clears up.
Speaker 2: Right. A well trained network is one where the loss
Speaker 2: is very low, meaning its predictions are consistently close to
Speaker 2: the actual target values.
Speaker 1: Okay, that makes a lot of sense learning from mistakes
Speaker 1: step by tiny step. Now. The book also mentions other
Speaker 1: machine learning algorithms kind of for context logistic regression, SVMs,
Speaker 1: random forests. Why bring those up?
Speaker 2: It helps to see where deep learning fits in the
Speaker 2: broader picture. These are really important tools in what you
Speaker 2: might call classical or shallow machine learning. Shallow yeah, generally
Speaker 2: meaning they don't have that deep, multi layered structure for
Speaker 2: learning representation logistic regression, for instance, it's pretty simple, but
Speaker 2: still super useful. For classification, it's often the first thing
Speaker 2: you try. Like the Hello world.
Speaker 1: Of mL okay and SVMs support vector machines.
Speaker 2: SVMs try to find the best possible boundary, like a
Speaker 2: line or a plane, to separate different classes in your data.
Speaker 2: They have this neat mathematical trick called the kernel.
Speaker 1: Trick ooh, kernel tricks hounds.
Speaker 2: Fancy it kind of is? It lets SVMs handle complex
Speaker 2: nonlinear separations without explicitly calculating coordinates in a super high
Speaker 2: dimensional space. It's computationally clever.
Speaker 1: Hmm. Interesting Maybe for another deep dive. What about random
Speaker 1: forests and gradient boosting.
Speaker 2: Both are ensemble methods. They combine predictions from many simpler models.
Speaker 2: Random forests build lots of decision trees on different subsets
Speaker 2: of the data and features, then average their outputs or
Speaker 2: take a majority.
Speaker 1: Vote like Wisdom of the Crowd, but for trees.
Speaker 2: Sort of Yeah, they're often really strong performers, very robust.
Speaker 2: Gradient boosting machines are also ensemble methods, but they build
Speaker 2: trees sequentially. Each new tree tries to correct the errors
Speaker 2: made by the trees that came before.
Speaker 1: It. Oh interesting, like building on previous mistakes.
Speaker 2: Exactly, and they often slightly outperform random forests, though they
Speaker 2: can be a bit more sensitive to tuning. But again,
Speaker 2: these are generally considered shallow compared.
Speaker 1: To deep learning, right, they don't have that automatic, multi
Speaker 1: layered feature learning. So that brings us back, what is
Speaker 1: it about deepe learning that's so transformative? What's the key difference?
Speaker 2: I think the biggest thing is its ability to learn
Speaker 2: all the layers of representation jointly, simultaneously, jointly, as opposed
Speaker 2: as opposed to traditional approaches where you might have separate steps.
Speaker 2: Like first you'd manually engineer some features from the.
Speaker 1: Raw data, like counting specific words and text or finding
Speaker 1: edges and an image exactly.
Speaker 2: You'd do that feature engineering, and then you'd feed those
Speaker 2: engineers features into a classifier like an SVM or a
Speaker 2: logistic regression. Deep learning does it all in one go.
Speaker 2: The network learns the best features and how to classify
Speaker 2: based on them altogether.
Speaker 1: Ah okay, and why is learning them jointly so powerful?
Speaker 2: Because the features can adapt to each other during learning.
Speaker 2: If one layer starts extracting a slightly different, maybe better
Speaker 2: type of future, the layers above it can adjust automatically
Speaker 2: to make use of that improved representation. It's much more
Speaker 2: dynamic and integrated.
Speaker 1: So the features themselves evolve during training to be optimal
Speaker 1: for the task.
Speaker 2: That's a great way to put it. This allows deep
Speaker 2: learning to learn really complex abstract concepts by breaking them down.
Speaker 2: You start with simple features at the bottom layers, like
Speaker 2: edges or textures in an image, and as you go
Speaker 2: up through the layers, the network combines these to learn
Speaker 2: more complex things like object parts and eventually whole objects.
Speaker 1: Like building complex ideas from simpler blocks. That makes sense.
Speaker 1: The book also touches on the pace of progress and
Speaker 1: mentions a sort of explosive phase. Where are we now?
Speaker 1: According to the author?
Speaker 2: Yeah, the author reflects on that period maybe around twenty
Speaker 2: seventeen twenty eighteen, especially with transformer models revolutionizing language tasks.
Speaker 2: It felt like huge breakthroughs were happening constantly, like the
Speaker 2: steep early part of an S.
Speaker 1: Curve, exponential growth almost almost.
Speaker 2: But the feeling, at least when the book was written
Speaker 2: around twenty twenty one was that we're probably in the
Speaker 2: second half of that S curve now, meaning meaning progress
Speaker 2: is still definitely happening and it's significant, but maybe the
Speaker 2: era of those absolutely fundamental paradigm shifting discoveries every few
Speaker 2: months is slowing down a bit.
Speaker 1: So more refinement, building on the existing foundations.
Speaker 2: That's a sensia, more incremental, but still powerful progress, finding
Speaker 2: new ways to apply these incredibly strong foundations that have
Speaker 2: been late.
Speaker 1: Okay, interesting perspectives, still moving fast, but maybe maturing. All right,
Speaker 1: let's get into the real nuts and bolts, the components.
Speaker 1: The book starts with tensors. What are they? Why are
Speaker 1: they the starting point?
Speaker 2: Tensors are basically the containers for data in neural networks.
Speaker 2: You can think of them as generalizations of vectors and
Speaker 2: matrices to potentially higher dimensions.
Speaker 1: So like a number is a tensor, a list of
Speaker 1: numbers a table.
Speaker 2: Exactly, a single number is a scaler or ranked zero
Speaker 2: tensor a list of numbers, like A vector is a
Speaker 2: rank one tensor a table of numbers. A matrix is
Speaker 2: a ranked two tensor, and you.
Speaker 1: Can have ranked three, ranked four, and so on.
Speaker 2: Yep, the rank just tells you how many axes or
Speaker 2: dimensions the tensor has.
Speaker 1: What defines a tensor then, besides the data.
Speaker 2: Itself two key things its shape and its data type
Speaker 2: or d type. The shape tells you how many elements
Speaker 2: are along each axis, like a matrix might be shape
Speaker 2: three five. The d type tells you what kind of
Speaker 2: numbers are inside, like thirty two bit floating point numbers
Speaker 2: or integers.
Speaker 1: Okay, can you give examples of real world data as tensors? Sure?
Speaker 2: Simple tabular data like customer infoage, income, whatever, It could
Speaker 2: be a ranked two tensor rows or customers columns or features. Right,
Speaker 2: time series data like daily stock prices for several stocks
Speaker 2: might be ranked three stocks time steps features like open
Speaker 2: Hilo clothes. Images are typically ranked four number of images
Speaker 2: height with color channels usually three for RGB.
Speaker 1: More dimensions for images.
Speaker 2: Video adds another dimension for time or frames, making at
Speaker 2: rank five number videos, frames, height with channels.
Speaker 1: Okay, I see how tensors provide this flexible structure for
Speaker 1: all sorts of data. So if tensors hold the data,
Speaker 1: what are the tensor operations? The book mentions the gears.
Speaker 2: These are the mathematical operations that the layers perform on
Speaker 2: the tensors. There are the calculations that transform the data
Speaker 2: as it flows through the network.
Speaker 1: Like, what kind of operations?
Speaker 2: Well, there are simple element wise operations where you do
Speaker 2: the same thing like add, multiply, or apply a function
Speaker 2: to each individual number in the tensor. There's broadcasting, which
Speaker 2: is a set of rules allowing operations between tensors of
Speaker 2: different but compatible shapes. It's very useful. The tensor product
Speaker 2: or dot product is absolutely fundamental. It's a core operation
Speaker 2: in linear algebra and use constantly in dense layers and reshaping,
Speaker 2: which changes the tensor shape without changing its contents.
Speaker 1: The book also has this geometric interpretation deep learning as
Speaker 1: untangling data manifolds. That sounds abstract.
Speaker 2: It is a bit abstract, but it's a powerful way
Speaker 2: to think about it. Imagine your raw data points, maybe
Speaker 2: images of handwritten digits are all jumbled together in a
Speaker 2: high dimensional space like a crumpled piece of paper.
Speaker 1: Okay, a messy blob.
Speaker 2: Right, A data manifold each layer in a deep network
Speaker 2: applies a transformation, a tensor operation that essentially tries to
Speaker 2: uncrumple that paper a little bit. It stretches, rotates, and
Speaker 2: folds the space that data lives in, trying to make
Speaker 2: the different categories the different digits in this example more
Speaker 2: easily separable.
Speaker 1: So layer by layer, it's smoothing out the crumpled paper
Speaker 1: until the digits written on different parts are clearly distinct.
Speaker 2: Exactly untangling the manifold. After enough layers, ideally that different
Speaker 2: classes of data will be nicely separated, maybe even by
Speaker 2: simple planes.
Speaker 1: That's a great visual. Okay, So tensors are data operations
Speaker 1: manipulate THEMMI metrically. The next piece is layers. What are they?
Speaker 2: Fundamentally, layers are the building blocks you stack together to
Speaker 2: create a deep learning model. You can think of them
Speaker 2: as modules that process data. They take one or more
Speaker 2: tensors as input and spit out one or more tensors
Speaker 2: as output.
Speaker 1: And they perform those tensor operations we.
Speaker 2: Just talked about precisely. Some layers are stateless, their output
Speaker 2: just depends on the current input. Others have internal state.
Speaker 2: This state consists of the layer's weights.
Speaker 1: The things that get learned during training.
Speaker 2: Exactly. The weights are themselves tensors, and they contain the
Speaker 2: knowledge the layer has learned. They get updated during training
Speaker 2: via gradient descent.
Speaker 1: And we use different types of layers for different.
Speaker 2: Data, right, yes, absolutely. Dense layers, also called fully connected
Speaker 2: layers are common for vector data. Convolutional layers like conv
Speaker 2: two D are the stars for image data. Recurrent layers
Speaker 2: like LSTMs or grus are designed for sequential data like
Speaker 2: text or time series. You choose layers suited to your
Speaker 2: data structure.
Speaker 1: Let's zoom it on dense layers for a second. What's
Speaker 1: the core operation they do and what's the deal with
Speaker 1: activation functions like re lu.
Speaker 2: Okay, A dense layer performs what's mathematically called an affine transform,
Speaker 2: takes the input vector, multiplies it by a weight matrix
Speaker 2: that's a tensor product, and then as a bias vector,
Speaker 2: it's basically output dot input plus.
Speaker 1: B a linear transformation plus an offset.
Speaker 2: Correct. Now, here's a really important point. If you just
Speaker 2: stack a bunch of these dense layers together doing only
Speaker 2: these Effin transforms, the whole stack is mathematically equivalent to
Speaker 2: just one single Effen transform. You haven't actually gained any
Speaker 2: expressive power beyond a simple linear model, no matter how
Speaker 2: many layers you add.
Speaker 1: WHOA. Okay, so stacking linear operations just gives you another
Speaker 1: linear operation. That seems limiting.
Speaker 2: It is. That's why we need activation functions. They introduce
Speaker 2: non linearity into the network after the fin transform in
Speaker 2: each layer.
Speaker 1: Non linearity. Why is that crucial?
Speaker 2: Because most real world relationships are non linear. If your
Speaker 2: network can only model linear functions, it's going to fail
Speaker 2: on most interesting problems. Activation functions break that linearity.
Speaker 1: And re LU is a common one, rectified linear unit.
Speaker 2: Very common and incredibly simple. It just computes max x zero.
Speaker 2: So if the input x is positive, it passes it
Speaker 2: through unchanged. If it's negative, it outputs zero.
Speaker 1: That's it. That little kink at zero is enough.
Speaker 2: It seems simple. But stacking layers with these ReLU activations
Speaker 2: allows the network to approximate arbitrarily complex nonlinear functions. It's
Speaker 2: what gives deep networks their power.
Speaker 1: Okay, ReLU simple function, massive impact because it adds nonlinearity.
Speaker 2: Got it.
Speaker 1: Now, these layers have weight matrices. You said they're initialized
Speaker 1: randomly yep.
Speaker 2: Usually with small random values. If you started them all
Speaker 2: at zero, they wouldn't learn properly. Randomness breaks the symmetry.
Speaker 1: And the whole point of training is to adjust these
Speaker 1: random weights.
Speaker 2: Exactly, to adjust them based on the feedback signal a loss,
Speaker 2: so that the network's overall transformation from to output performs
Speaker 2: the task correctly. The learned weights encode the solution, and.
Speaker 1: That adjustment mechanism is gradient based optimization. Let's break that
Speaker 1: down right.
Speaker 2: This is the engine driving the learning. The core idea
Speaker 2: is to use the gradient of the loss function.
Speaker 1: The direction of steepest descent.
Speaker 2: To figure out how to change the weights to decrease
Speaker 2: the loss. We want to go downhill on that lost
Speaker 2: landscape we.
Speaker 1: Talked about, okay, and how does it actually take the steps?
Speaker 2: A common algorithm is doochastic gradient descent or SGD. Stochastic
Speaker 2: just means it uses small random batches of the training
Speaker 2: data to estimate the gradient at each step, rather than
Speaker 2: the whole data set, which would be very slow.
Speaker 1: So it gets a noisy estimate of the downhill direction
Speaker 1: from a small sample.
Speaker 2: Exactly, and it takes a small step in that estimated
Speaker 2: downhill direction updating the weights. The size of that step
Speaker 2: is controlled by the learning rate.
Speaker 1: Ah, the learning rate.
Speaker 2: That sounds important, it's critical. Too big and you might
Speaker 2: overshoot the minimum or bounce around wildly. Too small and
Speaker 2: training will take forever, or you might get stuck easily.
Speaker 2: Finding a good learning rate is key.
Speaker 1: And the loss function itself, that's what defines the landscape
Speaker 1: we're descending. It tells us how wrong we are.
Speaker 2: Precisely, it quantifies the mismatch between the network's predictions and
Speaker 2: the true target values. Different tasks need different loss functions,
Speaker 2: but the goal is always to minimize it.
Speaker 1: Now, this dissent, can it get stuck? The book mentions
Speaker 1: local versus global minima.
Speaker 2: Yes, that's a potential issue. The lost landscape for deep
Speaker 2: networks can be very complex, with many valors. SGD might
Speaker 2: find the bottom of a small nearby valley the local minimum,
Speaker 2: but miss a much deeper valley elsewhere the global minimum.
Speaker 1: So it finds a solution, but maybe not the best
Speaker 1: possible one.
Speaker 2: Potentially, yes, although in practice for very high dimensional problems
Speaker 2: in deep learning, many local minimum are often quite good anyway.
Speaker 2: But techniques like momentum can help momentum.
Speaker 1: How does that help?
Speaker 2: Momentum adds a sort of inertia to the update step.
Speaker 2: It considers the direction of previous steps, not just the
Speaker 2: current gredient. This can help the optimizer roll through small
Speaker 2: local minima or navigate flat regions more effectively.
Speaker 1: Like giving it a push to get over little bumps.
Speaker 1: Yeah cool, okay, And you mentioned back propagation earlier as
Speaker 1: the way to calculate these gradients efficiently.
Speaker 2: Yes. Backpropagation is the algorithm that makes training deep networks feasible.
Speaker 2: It's a clever application of the chain roll from calculus.
Speaker 1: Chang right for nested functions.
Speaker 2: Exactly. A deep network is just a long chain of
Speaker 2: nested functions the layers. Backpropagation starts with the final loss
Speaker 2: and works backward to the network layer by layer. Why
Speaker 2: backward because it efficiently calculates how much each weight in
Speaker 2: the network contributed to the final error by reusing calculations
Speaker 2: from later layers. It figures out the gradient of the
Speaker 2: loss with respect to every single weight in the network.
Speaker 1: Wow, without having to recalculate everything from scratch for each
Speaker 1: weight precisely.
Speaker 2: It's computationally very efficient, and modern frameworks like tensor flo
Speaker 2: have automatic differentiation tools built.
Speaker 1: In, like gradient tape and TensorFlow Exactly.
Speaker 2: You define your networks, forward pass how the data flows through,
Speaker 2: and TensorFlow, using tools like gradient tape, automatically figures out
Speaker 2: how to compute the gradients needed for backpropagation. It handles
Speaker 2: all that calculus for you.
Speaker 1: That's amazing, takes away a huge mathematical burden. Okay, so
Speaker 1: we have tensors, operations, layers, activation functions, and this gradient
Speaker 1: descent engine powered by backpropagation. Let's talk about Paris. The
Speaker 1: book focuses on it heavily. What is Keras.
Speaker 2: Keiras is essentially a user friendly interface, an API for
Speaker 2: doing deep learning and Python. Its main goal is to
Speaker 2: make building and experimenting with models fast and easy.
Speaker 1: An interface on top of something else.
Speaker 2: Yes, it runs on top of lower level tensor computation libraries.
Speaker 2: TensorFlow is the primary one, especially since Keras was integrated
Speaker 2: directly into TensorFlow too. But it was designed to be
Speaker 2: back end diagnostic.
Speaker 1: So TensorFlow does the heavy lifting the tensor math running
Speaker 1: on GPUs or TPUs, and Keras provides a simpler way
Speaker 1: to tell TensorFlow what to do.
Speaker 2: That's a great way to put it. Kearras abstracts away
Speaker 2: a lot of the boilerplate code you'd need if you
Speaker 2: were using raw TensorFlow. Let you focus more on the
Speaker 2: model architecture and the experiment design makes sense.
Speaker 1: The book mentions TensorFlow concepts like TF dot tensor and
Speaker 1: TF dot variable. How do they fit in?
Speaker 2: Well? TF tensor is just tensorflow's implementation of the tensors
Speaker 2: we've been discussing the multi dimensional arrays holding data. TF
Speaker 2: variable is a special kind of tensor used to hold
Speaker 2: the model state, specifically the learnable parameters the weights and biases.
Speaker 1: Ah So variables are the tensors that the optimizers allowed
Speaker 1: to change during training.
Speaker 2: Exactly their values persist across training steps. TensorFlow also provides
Speaker 2: all the tensor operations like matrix multiplication, addition, activation functions, etc.
Speaker 2: That operate on these tensors and variables, often mimicking the
Speaker 2: interface of NUMPI, which is familiar to many Python users.
Speaker 1: Okay, so how do you actually build a model using keras?
Speaker 1: The book mentions a few ways. Munch API right.
Speaker 2: The sequential API is the simplest way. It's literally for
Speaker 2: building a model layer by layer, and the linear stack
Speaker 2: output of one layer feeds directly into the next. Super
Speaker 2: straightforward from many common network types, like building a single
Speaker 2: tower of legos.
Speaker 1: Simple but maybe limited. If you want something more.
Speaker 2: Complex, exactly for more complex architectures, you'd use the functional API.
Speaker 2: This lets you build models that are like graphs of
Speaker 2: layers rather than just a straight line graphs meaning meaning
Speaker 2: you can have multiple inputs, multiple outputs, layers that share connections, branches, merges,
Speaker 2: much more flexible if your model isn't just a simple stack,
Speaker 2: the functional API is usually the way to.
Speaker 1: Go, Okay, more powerful, and the third way model subclassing.
Speaker 2: Model subclassing is the most flexible approach. You define your
Speaker 2: model as a Python class inheriting from karst model. You
Speaker 2: define the layers in the init method, and then crucially
Speaker 2: you define the forward pass how data flows through the
Speaker 2: layers yourself, in a method called call.
Speaker 1: So you have complete control over the computation.
Speaker 2: Total control. Great for research or really non standard architectures.
Speaker 2: The trade off is that you lose some of the
Speaker 2: automatic features of the other APIs, like easy model plotting
Speaker 2: or serialization.
Speaker 1: You have bit more responsibility, right, more power, more work
Speaker 1: makes sense. Okay, so you've built your model using one
Speaker 1: of these APIs, what's the standard workflow for training and
Speaker 1: using it. The compile, fit, evaluate, predict that's the core loop.
Speaker 2: Yeah, first you compile the model. This step configures the
Speaker 2: learning process. You tell Keras which optimizer to use, like
Speaker 2: ATOM or MUG.
Speaker 1: The algorithm for gradient descent.
Speaker 2: Right. You specify the longs function you want to minimize,
Speaker 2: but categorical cross entropy for multi class classification or MZ
Speaker 2: for regression. And you list any metrics you want attrack
Speaker 2: during training like accuracy.
Speaker 1: Okay, setting the rules for learning.
Speaker 2: Then fit yep fit is where the actual training happens.
Speaker 2: You give it your training data inputs and target outputs.
Speaker 2: You tell it how many epbos to train for and
Speaker 2: usually the batch size, how many SAMs to process before
Speaker 2: updating the weights.
Speaker 1: And Kars handles the look, the backpropagation, updating weights.
Speaker 2: All of it. You can also pass validation data to fit,
Speaker 2: so Karas will evaluate the model on data it hasn't
Speaker 2: trained on after each epoch. That's crucial for monitoring progress monitoring.
Speaker 1: Yeah, so after fit finishes.
Speaker 2: What's next You use Evaluate. You give it a separate
Speaker 2: test data set data the model is never seen during
Speaker 2: training or validation tuning. Evaluate returns the final loss and
Speaker 2: metric values like accuracy on this test set. This gives
Speaker 2: you the best estimate of how well your model will
Speaker 2: generalize to new real world data the final report card exactly.
Speaker 2: And then finally predict. You give predict new input data
Speaker 2: without labels, and the trained model gives you its predictions.
Speaker 2: This is how you actually use the model.
Speaker 1: Compile, fit, evaluate, predict, got the flow you mentioned monitoring
Speaker 1: progress during fit using validation data. The book also talks
Speaker 1: about callbacks and tensor board.
Speaker 2: Yes, these are super useful validation data. As we said,
Speaker 2: let you see if your model's starting to overfit doing
Speaker 2: better on training data but worse on unseen data. Callbacks
Speaker 2: are objects you can pass to fit that perform actions
Speaker 2: at certain points during training, like what kind of actions
Speaker 2: things like early stopping. This callback monitors a metric on
Speaker 2: the validation set, maybe validation loss, and if it stops
Speaker 2: improving for a certain number of etbox, it automatically stops
Speaker 2: the training.
Speaker 1: Now that's smart. Prevents wasting time and overfitting exactly.
Speaker 2: Or model checkpoint which saves your model's weights whenever the
Speaker 2: validation performance improves, so you always keep the best version.
Speaker 2: And tensive board is a visualization toolkit.
Speaker 1: What does tensor boards show.
Speaker 2: You you can log metrics during training, loss, accuracy, etc.
Speaker 2: And view plots of them in your web browser in
Speaker 2: real time. You can visualize the model graph, examine histograms
Speaker 2: of weights and activations. It gives you much deeper insight
Speaker 2: into what's happening during training.
Speaker 1: Sounds invaluable for debugging and understanding. Okay. The book also
Speaker 1: distinguishes between regression and classification tasks. How do they differ
Speaker 1: in terms of loss and metrics?
Speaker 2: Right? The goal is different. Classification is about predicting a
Speaker 2: category label catdog, spam not spam. Regression is about predicting
Speaker 2: a continuous number, price, temperature.
Speaker 1: Age, So the way you measure success has to be
Speaker 1: different exactly.
Speaker 2: For classification, you often use loss functions like categorical cross
Speaker 2: entropy or binary cross entropy, and you measure performance with
Speaker 2: metrics like accuracy, what fraction did it get right?
Speaker 1: Right?
Speaker 2: Precision recall?
Speaker 1: Okay.
Speaker 2: For regression, common loss functions are mean squared air MC
Speaker 2: or mean absolute air. These measure how far off the
Speaker 2: numerical promptions are on average, So your metrics are also
Speaker 2: things like MAE or rmc root means squared air. Accuracy
Speaker 2: doesn't really make sense for regression.
Speaker 1: Got it, Different targets, different ways to measure how close
Speaker 1: you are. Okay, let's shift to some really key concepts
Speaker 1: in developing models. Generalization, overfitting, underfitting.
Speaker 2: These seem critical, absolutely fundam Generalization is the whole point. Really,
Speaker 2: it's the model's ability to perform well on new unseen data,
Speaker 2: not just the data was trained on.
Speaker 1: You want it to work in the real world exactly.
Speaker 2: Overfitting is when the model learns the training data too well.
Speaker 2: It memorizes the noise and specific quirks of the training set,
Speaker 2: but it fails to generalize to new data. It performs
Speaker 2: great on training data, poorly on test data.
Speaker 1: Okay, memorizing instead of learning the underlying pattern and underfitting.
Speaker 2: Underfitting is the opposite problem. The model is too simple.
Speaker 2: It can't even capture the underlying patterns and the training data,
Speaker 2: let alone generalize. It performs poorly on both training and
Speaker 2: test data.
Speaker 1: So we need to find that sweet spot. Complex enough
Speaker 1: to learn, but not so complex it just memorizes. The
Speaker 1: book calls this the tension between optimization and generalization.
Speaker 2: Right, because as you train your model longer optimizing it
Speaker 2: on the training data, its performance on the training data
Speaker 2: keeps getting better, but its performance on unseen validation data
Speaker 2: will improve for a while, then peak, and then start
Speaker 2: to get worse as overfitting kicks in.
Speaker 1: Ah, So optimizing too much hurts generalization.
Speaker 2: Beyond a certain point. Yes, all the techniques for building
Speaker 2: good models are about managing this tension, finding that peak
Speaker 2: generalization performance.
Speaker 1: And how do we reliably measure generalization. The book mentions
Speaker 1: different evaluation protocols.
Speaker 2: Hold out K fold, yeah, because just looking at performance
Speaker 2: on the training set is misleading. The simplest is hold
Speaker 2: out validation. You split your data training set, validation set testing.
Speaker 1: Train on training, tune on validation, final check on tests.
Speaker 2: Precisely, you use the validation set during development to make
Speaker 2: decisions like when to stop training or how many layers
Speaker 2: to use. The test set is kept completely separate until
Speaker 2: the very end for one final unbiased evaluation.
Speaker 1: What if you don't have much data, then kfold.
Speaker 2: Cross validation is better. You split the data minus the
Speaker 2: test set into k chunks or folds. Then you train
Speaker 2: k models. Each model uses k one folds for training
Speaker 2: and one fold for validation.
Speaker 1: So every data point gets used for validation exactly once.
Speaker 2: Right. Then you average the validation scores from the k runs.
Speaker 2: It gives a more robust estimate, especially with small data sets.
Speaker 2: Iterated kfold just repeats this whole process multiple times with
Speaker 2: different shuffles for even more stability. But always always keep
Speaker 2: that final test set pristine until the very end.
Speaker 1: Got it validation guides development test gives the final score.
Speaker 1: What about data preprocessing things like scaling features? Why do that?
Speaker 2: Neural networks can be quite sensitive to the scale of
Speaker 2: input features. If one feature ranges from zero to one
Speaker 2: and another from zero to one million, the network might struggle.
Speaker 2: The larger valued feature could dominate the learning process or
Speaker 2: cause numerical.
Speaker 1: Instability, so you need to put them on a similar scale.
Speaker 2: Yeah, it generally helps training significantly. Common techniques are normalization
Speaker 2: scaling to be between zero one or standardization scaling to
Speaker 2: have zero mean and unit variants. You typically scale features independently.
Speaker 1: Makes sense. And model capacity that's about how complex the
Speaker 1: model is, number of layers units exactly.
Speaker 2: It's roughly how much information the model can store, how
Speaker 2: complex a function it can learn. Too little capacity leads
Speaker 2: to underfitting. Too much capacity makes overfitting easier.
Speaker 1: So finding the right capacity for your specific problem in
Speaker 1: data is crucial.
Speaker 2: It's a key part of model development. Yeah. Often involves experimentation, and.
Speaker 1: If your model has too much capacity and starts overfitting,
Speaker 1: you use regularization techniques.
Speaker 2: Yes. Regularization methods are designed specifically to combat overfitting and
Speaker 2: encourage better generalization. They work by constraining the complexity of
Speaker 2: the model during training.
Speaker 1: How what are some examples.
Speaker 2: Well, one simple form is just reducing the model size
Speaker 2: fewer layers or fewer units neurons per layer. Another very
Speaker 2: common one is dropout during training. Dropout randomly sets the
Speaker 2: output of a fraction of neurons in a layer to
Speaker 2: zero for each training example. This forces the network to
Speaker 2: learn more robust representations that don't rely too heavily on
Speaker 2: any single neuron.
Speaker 1: Like forcing it to have redundant pathways kind of Yeah.
Speaker 2: Another technique is weight regularization, like L one or L two.
Speaker 2: This adds a penalty to the loss function based on
Speaker 2: the size of the model's weights. It encourages the model
Speaker 2: to learn smaller, simpler weight configurations, which often generalize better.
Speaker 1: Okay, so a whole toolkit to fight overfitting. Now, the
Speaker 1: book also touches on ethical considerations. What's the main point there?
Speaker 2: It's a really important reminder that technology isn't neutral. The
Speaker 2: choices we make when designing and deploying AI systems, what
Speaker 2: data we use, what objective we optimize for, how we
Speaker 2: test it can have real world ethical.
Speaker 1: Consequences, like biases in the training data leading to unfair outcomes.
Speaker 2: That's a major one. If your data reflects historical biases,
Speaker 2: your model will likely learn and perpetuate them. We need
Speaker 2: to be aware of potential harms and actively work to
Speaker 2: mitigate them. Technical choices have moral dimensions a crucial point.
Speaker 1: Okay, let's walk through the overall machine learning workflow. The
Speaker 1: book outlines what are the big stages.
Speaker 2: It starts crucially with defining the task, really understanding the
Speaker 2: problem you're trying to solve before you jump into code.
Speaker 1: Understanding the context, the user, the.
Speaker 2: Goal, exactly what's the value, how will the model actually
Speaker 2: be used? What data do you have or can you get?
Speaker 2: And then framing that business problem is a specific mL task?
Speaker 2: Is it classification, regression, something else?
Speaker 1: So problem definition first, then then.
Speaker 2: Collect a data set. This is often the hardest, most
Speaker 2: expensive part. You need inputs, You need corresponding targets, labels,
Speaker 2: data quality and availability, or often the bottlenecks sometimes involves
Speaker 2: lots of manual labeling.
Speaker 1: Right garbage in garbage out applies strongly here absolutely.
Speaker 2: Step three is develop a model. This involves choosing a
Speaker 2: suitable architecture.
Speaker 1: Like convents for images, transformers for texts right, and then.
Speaker 2: The initial goal is often counterintuitively, to build a model
Speaker 2: that's powerful enough to overfit the training data first. Why
Speaker 2: overfit first, because if you can't even overfit the training data,
Speaker 2: it means your model doesn't have enough capacity or something
Speaker 2: else is fundamentally wrong. Overfitting proves your model can learn
Speaker 2: the training patterns. You need to reach that point before
Speaker 2: you can start regularizing, and you monitor training and validation
Speaker 2: metrics constantly.
Speaker 1: Okay, achieve overfitting, then pull back. So step four is
Speaker 1: regularize and tune exactly.
Speaker 2: Now you focus on generalization. You adjust hyper parameters like
Speaker 2: learning rate, network size, regularization strength, apply techniques like dropout,
Speaker 2: all guided by the performance on your validation set. The
Speaker 2: goal is to find the settings that give the best validation.
Speaker 1: Performance, maximize generalization, and the final step.
Speaker 2: Deploy the model. Get it out into the real world.
Speaker 2: This involves exporting it, maybe to a non Python format,
Speaker 2: integrating it into your production system, monitoring its performance live
Speaker 2: and crucially collecting data on how it's doing to feed
Speaker 2: into training the next version of the model.
Speaker 1: So it's a cycle really define, collect, develop, regularize, deploy,
Speaker 1: monitor and repeat.
Speaker 2: It's very much an iterative process.
Speaker 1: Yes, okay, that workflow makes a lot of sense. So
Speaker 1: wrapping up our deep dive today, we covered a lot
Speaker 1: of ground we did. The core idea. Deep learning uses
Speaker 1: multi layered neural networks to learn representations from data. It's
Speaker 1: built on tensors, tensor operations, layers, and powered by a
Speaker 1: gradient descent and backpropagation.
Speaker 2: The tools like keras and TensorFlow make it much more
Speaker 2: accessible to build and train these complex models.
Speaker 1: Plus understanding those key concepts generalization, overfitting, the evaluation protocols,
Speaker 1: the whole workflow is crucial for actually using it effectively
Speaker 1: and responsibly.
Speaker 2: Absolutely, it's not just about the algorithms, but the whole
Speaker 2: process around them.
Speaker 1: So a final thought for you, our listener to chew
Speaker 1: on the book mentions this interesting trade off maybe losing
Speaker 1: some cultural diversity for more intellectual or technical diversity as
Speaker 1: societies become more globally connected.
Speaker 2: Yeah, that was an intriguing point.
Speaker 1: As AI and deep learning become even more pervasive, how
Speaker 1: might they influence that balance? Could they create new kinds
Speaker 1: of digital diversity, or maybe new forms of homogeneity. It's
Speaker 1: something to think about how this technology shapes not just
Speaker 1: what we can do, but maybe even how we think
Speaker 1: and interact.
Speaker 2: Definitely food for thought. The impact goes way beyond just
Speaker 2: the tech itself.
Speaker 1: Indeed, well thanks for joining us on this deep dive.
Speaker 2: My pleasure.