Speaker 1: Welcome to the deep Dive. Today, we're marking on a
Speaker 1: journey into the powerful world of deep learning, seen specifically
Speaker 1: through the lens of R.
Speaker 2: That's right.
Speaker 1: Our mission is to extract the most important insights and
Speaker 1: practical applications from hands on deep learning with R by
Speaker 1: Michael Paulis and Roger Devine.
Speaker 2: And this isn't just about theory, right, think of this
Speaker 2: as your guide to designing, building, and truly improving neural
Speaker 2: network models. We're distilling it down into core concepts and
Speaker 2: some surprising applications. It's kind of a shortcut really to
Speaker 2: being genuinely well informed in this complex space.
Speaker 1: So, whether you're prepping for a big meeting, maybe just
Speaker 1: catching up on the latest in data science, or you're
Speaker 1: simply like insatiably curious, prepare for some serious aha moments.
Speaker 1: Let's start at the beginning. Then, deep learning it's a
Speaker 1: powerful subset of machine learning, but they fundamentally share a
Speaker 1: lot of common ground. What are some of those essential
Speaker 1: building blocks?
Speaker 2: Well, at its core, it's all about preparing your data
Speaker 2: for modeling. The book uses a great example actually with
Speaker 2: the London air Quality network data set. The goal there
Speaker 2: is predicting nitrogen dioxide levels, and this involves some really
Speaker 2: crucial steps like identifying and extracting relevant data information you know,
Speaker 2: the day, the month, and also intelligently handling missing values,
Speaker 2: for instance, removing maybe a small percentage like three or
Speaker 2: four percent of missing target values that you just can't
Speaker 2: reliably guess.
Speaker 1: Right because bad guesses could throw the whole model.
Speaker 2: Off exactly, and filtering out variables that don't add any
Speaker 2: real information. Think about columns where every single value is identical,
Speaker 2: like maybe a site idea. If you're only looking at
Speaker 2: one site or units, they just don't help.
Speaker 1: And data quality isn't just about missing values, is it.
Speaker 1: We also saw the importance of checks like confirming provisional
Speaker 1: or ratified values, making sure you know the status of
Speaker 1: the data, and transforming data types too, ensuring numeric data
Speaker 1: isn't accidentally stored as text, which happens more than you'd think.
Speaker 2: Oh definitely. Then comes the actual model training, so that
Speaker 2: means splitting your data into training and testing sets, typically
Speaker 2: a good chunk maybe seventy or eighty percent for training,
Speaker 2: and then choosing the right algorithm. The book highlights exch
Speaker 2: boost as a pretty robust gradient tree boosting method boosting.
Speaker 1: That's different from something like a random.
Speaker 2: Forest, right, Yeah, very different. What's important about boosting methods
Speaker 2: like xt boost is that they learn iteratively. Each new
Speaker 2: model essentially tries to correct the mistakes of the previous one.
Speaker 2: It's a refinement process. Random forests, on the other hand,
Speaker 2: use bagging. They build many independent models and sort of
Speaker 2: average their results. Both powerful but different approaches.
Speaker 1: Okay, that makes sense. This brings us to a critical point. Then,
Speaker 1: how do you truly evaluate your model's results? Because simple
Speaker 1: accuracy can be really misleading? Can it?
Speaker 2: Absolutely?
Speaker 1: The credit card fraud example in the book perfectly illustrates this.
Speaker 1: If only say, zero point one percent of transactions are fraudulent,
Speaker 1: a model predicting no fraud every time gets ninety nine
Speaker 1: point nine percent accuracy, But it's completely useless. It misses
Speaker 1: every single instance of actual fraud precisely.
Speaker 2: That's why just looking at accuracy it can be well
Speaker 2: dangerously deceptive. Sometimes it really forces you to think about
Speaker 2: the cost of being wrong. You need metrics like mean
Speaker 2: absolute error MA or root means squared error rmse E
Speaker 2: and RMS.
Speaker 1: That's the one that squares the errors.
Speaker 2: Yeah, and the real insight with RMSC isn't just the math.
Speaker 2: It's that it forces you to heavily penalize those big,
Speaker 2: potentially catastrophic mispredictions. So if missing badly is really really
Speaker 2: bad for your project, RMSE is often the better choice.
Speaker 2: It makes those big errors hurt more in the calculation.
Speaker 1: Got it, So, once we have a model, how do
Speaker 1: we actually make it better? The book talks about strategies
Speaker 1: like cross validation.
Speaker 2: Right cross validation. That's where you basically repeat the train
Speaker 2: to split multiple times with different slices of your data.
Speaker 2: It gives you a much more reliable estimate of how
Speaker 2: the model will perform on unseen data.
Speaker 1: And early stopping. That sounds important too.
Speaker 2: Yeah, early stopping is key. It means you monitor the
Speaker 2: model's performance on a valuelidation set during training, and you
Speaker 2: just stop the training if things haven't improved for say,
Speaker 2: twenty five rounds. Or EPOX prevents overfitting.
Speaker 1: And grid searches for hyper parameter tuning, that's about finding
Speaker 1: the best settings.
Speaker 2: Exactly systematically, trying out different combinations of settings like learning
Speaker 2: rates or tree depths to find that optimal configuration for
Speaker 2: your specific problem. And you know, we also briefly touched
Speaker 2: on a wider range of machine learning algorithms. Beyond XP
Speaker 2: you boost, there's a whole family things like decision trees
Speaker 2: and their ensemble cousin random forests, logistic regression for classification problems,
Speaker 2: support vector machines which are great at finding separation lines
Speaker 2: and data k nearest neighbors k means for clustering, and
Speaker 2: other boosting methods too, like gradient boosting machines GBM and
Speaker 2: light GBM. Understanding how these iterative methods work, how they
Speaker 2: build on information, it really does lay some groundwork for
Speaker 2: grasping deep learning concepts later on.
Speaker 1: All right, So, if you're listening and ready to get
Speaker 1: hands on with deep learning in R, what are the
Speaker 1: essential libraries we need to get started? Okay?
Speaker 2: The primary work courses mentioned are H two O, mx
Speaker 2: NET and KERAS, and we also saw some more specialized
Speaker 2: packages like RBM for restricted Boltzmann machines and reinforcement.
Speaker 1: Learning and installation. It's not always just installed packages, is it.
Speaker 2: Some seem straightforward from KARAN the main R archives right.
Speaker 1: Some are, but others yeah, like RBM or espressly karas
Speaker 1: often need a bit more worre Keras usually relies on
Speaker 1: TensorFlow running in the background, often in a separate Python
Speaker 1: environment like Conda or a virtual environment.
Speaker 2: So you might need div tools or need to point
Speaker 2: R to the right Python installation exactly.
Speaker 1: And mx net, for instance, might even need external libraries
Speaker 1: installed in your system first, like OpenCV for image stuff
Speaker 1: or open believes for linear algebra. It can get a
Speaker 1: little complex, but what's really insightful here, I think, is
Speaker 1: understanding the different strengths of each library within our karras
Speaker 1: gives you incredibly broad support for almost any neural network
Speaker 1: architecture you can think of and end CNN's MLPs, you
Speaker 1: name it. H two Ozero is fantastic when you're dealing
Speaker 1: with really really large data sets because you can store
Speaker 1: objects out of memory across a cluster if needed. An
Speaker 1: mx net it provides a really robust, efficient set of algorithms.
Speaker 2: Powerful stuff in the book shows examples for each Yeah,
Speaker 2: we saw how to get a basic example running with
Speaker 2: each one, including a practical demo of pre processing the
Speaker 2: adult census data set, converting character columns to numbers using
Speaker 2: one hottened coding, scaling everything between zero and one standard
Speaker 2: but crucial steps.
Speaker 1: Okay, let's dig into the deep part, How exactly does
Speaker 1: deep learning get that name? And what's really at its core?
Speaker 2: Right? The deep comes from using multiple hidden layers made
Speaker 2: up of these artificial neuros. These layers are stacked and
Speaker 2: they mimic in a very very simplified way, how our
Speaker 2: brains process information. The real key insight is that each
Speaker 2: layer can learn progressively more complex features from the data.
Speaker 1: How does that work in practice? For an image?
Speaker 2: So imagine the first layer might identify basic edges or
Speaker 2: corners in an image. The next layer might combine those
Speaker 2: edges to detect simple shapes. A layer deeper might recognize
Speaker 2: textures or parts of objects, and so on. This hierarchical
Speaker 2: learning building complexity layer by layer.
Speaker 1: That's what makes it deep and what does this structure
Speaker 1: mean for how they actually learn?
Speaker 2: Well, the process starts with random weights. These are just
Speaker 2: numbers assigned to the connections between neurons, representing the strength
Speaker 2: of the connection. Then these weights are adjusted over and
Speaker 2: over again iteratively to minimize the difference the error between
Speaker 2: the network's predictions and the actual answers in your training data.
Speaker 1: So it's constantly refining itself based on feedback.
Speaker 2: Exactly, it's a continuous refinement process, very much like how
Speaker 2: those boosting algorithms learn from the errors of previous iterations.
Speaker 1: Actually that makes sense, but okay, zooming in on those
Speaker 1: individual neurons, Yeah, how do they decide whether to fire
Speaker 1: or pass a signal forward? Ah?
Speaker 2: Good question. That's where two key things come in, bias
Speaker 2: functions and activation functions. Bias functions you can think of
Speaker 2: them as shifting the decision boundary, allowing the model to
Speaker 2: better separate different classes of data. And activation functions they're
Speaker 2: the real decision makers inside each neuron. They take the
Speaker 2: weighted sum of inputs plus the bias and decide if
Speaker 2: and how strongly that neuron should fire and pass the
Speaker 2: signal to the next layer.
Speaker 1: Right, and we looked at a whole range of these
Speaker 1: activation functions, didn't we From the simple on off heavy side.
Speaker 2: Yeah, the heavy side is very basic, just a step,
Speaker 2: But the non linear ones are where things get interesting.
Speaker 2: There's the sigmoid function, which squishes values into a range
Speaker 2: between zero and one, really useful for probabilities or binary outcomes.
Speaker 2: Then its cousin the hyperbolic tangent or ten, which is
Speaker 2: similar but ranges from moneybo one to.
Speaker 1: One and read lu seems really popular. Rectified linear units.
Speaker 2: Oh yeah, ReLU is huge. It's simple. It outputs the
Speaker 2: input directly if it's positive and zero otherwise. That simplicity
Speaker 2: makes training much faster in many cases. But it has
Speaker 2: a potential issue called the eyeing ReLU problem, where neurons
Speaker 2: can get stuck outputting zero, So leaky ReLU is developed.
Speaker 2: It gives a tiny slope for negative inputs just to
Speaker 2: keep things flowing.
Speaker 1: And swish was another one.
Speaker 2: Swish Yeah, a more recent one that's shown good results.
Speaker 2: It's a smoother function, lots of options really.
Speaker 1: And for classification tasks where you have like multiple categories dogs, cats, birds,
Speaker 1: the softmax function is key.
Speaker 2: Right, absolutely essential. Softmax takes the outputs for each class
Speaker 2: and converts them into probabilities that all add up to one.
Speaker 2: So the model tells you I think it's seventy percent
Speaker 2: likely a cat, twenty percent a dog, ten percent a bird. Okay,
Speaker 2: And you know, the book even walks you through building
Speaker 2: a very basic network from scratch in just basar, just
Speaker 2: to illustrate how weights get updated and how a LIGNE
Speaker 2: can separate classes. Then it scales up using the neural
Speaker 2: net package for the Wisconsin cancer data set, which is
Speaker 2: a classic and importantly it shows the backpropagation.
Speaker 1: Step backpropagation, that's how the error gets used to update
Speaker 1: the weights exactly.
Speaker 2: The error is calculated at the out and then it's
Speaker 2: propagated backward through the network layer by layer, telling each
Speaker 2: weight how much it needs to adjust to reduce that error.
Speaker 2: It's the core learning mechanism.
Speaker 1: Fascinating stuff. Okay, let's move to applications. Image recognition is
Speaker 1: a huge one for deep learning. Can we use traditional
Speaker 1: machine learning for images at all?
Speaker 2: You absolutely can. Yeah. Using what are sometimes called shallow nets,
Speaker 2: things like random forests or simple neural networks. You can
Speaker 2: apply them to data sets like fashion mnists Fashion.
Speaker 1: Mnist, that's the clothing images instead of handwritten digits. Right.
Speaker 2: It's a bit more challenging than the original mnist digits.
Speaker 2: But shallow nets their limitations become pretty clear when you
Speaker 2: move to larger, more complex real world images. They just
Speaker 2: struggle to efficiently capture all the intricate patterns.
Speaker 1: And this is where convolutional neural networks CNNs really come
Speaker 1: into their own, isn't it? How do they manage it?
Speaker 2: What really sets CNN's apart is their architecture specifically designed
Speaker 2: for grid like data like images, they automatically learn the
Speaker 2: right features directly from the pixels. They use specialized layers
Speaker 2: of convolution layers that apply filters across the image to
Speaker 2: detect specific patterns like edges, corners, textures, maybe even simple shapes, so.
Speaker 1: They're not just looking at individual pixels anymore, not at all.
Speaker 2: Then they often use pooling layers, which reduce the size
Speaker 2: the dimensionality, making the process more efficient and helping the
Speaker 2: network focus on the most important features. And techniques like
Speaker 2: adding padding say padding same can help control how quickly
Speaker 2: the dimensions shrink, letting you build deeper networks without losing
Speaker 2: information too fast.
Speaker 1: And you can build really deep CNNs right stacking, multiple
Speaker 1: convolution and pooling layers.
Speaker 2: Oh yes, that allows the network to learn this hierarchy
Speaker 2: of features. We talked about simple features in early layers
Speaker 2: combined into more complex ones and deeper layers. It's kind
Speaker 2: of analogous to how our own visual system works in
Speaker 2: a way.
Speaker 1: So with these complex models, how do we optimize them effectively?
Speaker 2: Good question? Optimization is key. We discussed various algorithms called optimizers,
Speaker 2: things like stochastic gradient descent SGD, which is a basic workhourse.
Speaker 2: Then RM's prop and ATAM is a very popular one nowadays.
Speaker 2: It sort of combines the ideas of arms PROP with momentum,
Speaker 2: often leading to faster convergence.
Speaker 1: And choosing the right loss function is important.
Speaker 2: To crucial for binary classification, binary cross entropy for multiple classes,
Speaker 2: categorical cross entropy for regression problems where you predict a
Speaker 2: number maybe means squared error, and sometimes you need metrics
Speaker 2: beyond just accuracy like cosine similarity or CHL divergence, especially
Speaker 2: if you're comparing probability distributions or embeddings.
Speaker 1: Okay, and you mentioned ways to prevent overfitting like dropout layers.
Speaker 2: Yeah, dropout is a really clever technique. During training, it
Speaker 2: randomly sets a fraction of neuron outputs to zero for
Speaker 2: each training example.
Speaker 1: So it forces the network not to rely too heavily
Speaker 1: on any single neuron exactly.
Speaker 2: It encourages redundancy and makes the network more robust and
Speaker 2: early stopping. Like we mentioned before, halting training when performance
Speaker 2: on a validation set stops improving is another vital tool
Speaker 2: against over fitting. Helps find that sweet spot for the
Speaker 2: number of training epochs right.
Speaker 1: Okay, let's ship here's a bit. Multilayer perceptions or MLPs.
Speaker 1: What about them, particularly for signal detection tasks? What makes
Speaker 1: them distinct?
Speaker 2: MLPs are kind of the classic foundational feed forward neural network.
Speaker 2: Their defining feature is that they only use fully connected layers.
Speaker 1: Meaning every neuron in one layer connects to every single
Speaker 1: neuron in the.
Speaker 2: Next layer, precisely. Unlike CNNs with their specialized convolution layers
Speaker 2: or RNNs with their recurrent connections, MLPs are just stacks
Speaker 2: of these dense fully connected layers. They're good general purpose learners,
Speaker 2: maybe less specialized than cms for images or LSTMs for sequences,
Speaker 2: and for MLPs.
Speaker 1: We looked at some specific data prep steps, didn't we
Speaker 1: like trimming white space from categories. Why is that important? Ah?
Speaker 2: Yes, it sounds trivial, But if you have mail with
Speaker 2: a leading space and mail without, the computer sees them
Speaker 2: as two totally different categories, so cleaning that up is essential.
Speaker 1: And rescaling numeric values to a zero one range. Why
Speaker 1: do we do that rescale step?
Speaker 2: Again, It's really about efficiency and stability. If you have
Speaker 2: one feature ranging from zero to one and another from
Speaker 2: zero to one million, the larger future can dominate the
Speaker 2: learning process. Scaling brings everything into the same range, so
Speaker 2: they contribute more equally, and it often helps the model's
Speaker 2: optimization process converge faster and more reliably.
Speaker 1: Makes sense, and it was a rule of thumb for
Speaker 1: hidden layer size.
Speaker 2: Yeah, a common juristic, just a starting point really is
Speaker 2: to try setting the number of nodes in a hidden
Speaker 2: layer to about two thirds of the input layer size.
Speaker 2: We saw how you could write functions in R using
Speaker 2: the mx net syntax in the book to easily test
Speaker 2: different node counts and even experiment with adding more hidden layers.
Speaker 1: Okay, now let's talk about something we all encounter daily.
Speaker 1: Recommender systems. Yeah, streaming movies, online shopping. How do they
Speaker 1: actually work and where does deep learning fit in?
Speaker 2: Right recommenders? Broadly, there are three main types. Collaborative filtering,
Speaker 2: which finds users similar to you and recommends what they liked,
Speaker 2: content based filtering, which recommends items similar to ones you've
Speaker 2: liked before based on their attributes, and habrid systems, which
Speaker 2: tried combine the best to both worlds.
Speaker 1: Had a big challenge. Is the cold start problem right
Speaker 1: for new users or new items?
Speaker 2: Exactly? If you're a new user, the system knows nothing
Speaker 2: about your tastes. If it's a brand new movie, nobody
Speaker 2: has rated it yet. That makes recommendations difficult.
Speaker 1: Initially, what seemed really fascinating here was the idea of embeddings.
Speaker 1: How do these low dimensional vectors help?
Speaker 2: Embeddings are a really powerful concept in deep learning, not
Speaker 2: just for recommendations. They basically learned dense low dimensional vector
Speaker 2: representations for things like users and items. Instead of dealing
Speaker 2: with huge, sparse matrices of user item interactions. You map
Speaker 2: users and items into this shared latent space, a coordinate system,
Speaker 2: if you will.
Speaker 1: And closeness in that space means similarity.
Speaker 2: Precisely users close to items they like and similar users
Speaker 2: close to each other. It captures these affinities efficiently, making
Speaker 2: it easy to calculate similarity like with a dot product,
Speaker 2: even when you don't have explicit ratings for everything.
Speaker 1: And we looked at the Steam two hundred k do
Speaker 1: CSV data set, which uses implicit feedback.
Speaker 2: Yeah, that was a great example. Instead of star ratings,
Speaker 2: it uses hours played. For video games. Sid Meier's Civilization
Speaker 2: V had huge hours logged by some users. This implicit
Speaker 2: data clicks, views, purchase history, playtime is often much more
Speaker 2: abundant and sometimes more revealing than explicit ratings.
Speaker 1: So we saw preparing that data, doing some exploratory data
Speaker 1: analysis EDA to understand those interactions.
Speaker 2: Yep, understanding who plays what for how long?
Speaker 1: And then building a custom caris model using both user
Speaker 1: and bettings and item embttings.
Speaker 2: Right, But then there's another layer. How do you account
Speaker 2: for inherent biases? Some users just play games way more
Speaker 2: than others, regardless of the specific game, and some games
Speaker 2: are just universally popular.
Speaker 1: Ah, so you need to model those baseline tendons exactly.
Speaker 2: Adding specific bias e bettings one for the average user's
Speaker 2: tendency and one for the average items popularity can really
Speaker 2: improve the model. It lets the main embeddings focus on
Speaker 2: the interaction effect the specific user item affinity separate from
Speaker 2: these general biases. In the books example, adding biases nearly
Speaker 2: doubled the trainable parameters, but led to much better recommendations.
Speaker 1: Very clever. Okay, let's pivot to time series data. Stock
Speaker 1: price forecasting is the classic example. How does deep learning
Speaker 1: tackle this? Given that the order of events is so critical.
Speaker 2: Time series is definitely unique, Unlike say, image classification, where
Speaker 2: you can shuffle the images with time series. The sequence
Speaker 2: is everything. You absolutely have to maintain chronological order when
Speaker 2: splitting data for training and testing.
Speaker 1: Because the past predicts the future.
Speaker 2: Basically, fundamentally, yes, the patterns are in the sequence. We
Speaker 2: compared this deep learning approach to traditional methods like ARIMA models.
Speaker 2: Arima can be good, but often struggles to predict complex
Speaker 2: patterns far beyond the training data it saw.
Speaker 1: This is where recurrent neural networks are and ends and
Speaker 1: especially long short term memory LSTM networks come in. These
Speaker 1: are the game changers.
Speaker 2: They really are for sequential data. LSTMs in particular are
Speaker 2: designed to have memory. They have internal mechanisms, these gates
Speaker 2: that allow them to retain information from previous steps in
Speaker 2: the sequence and use it for current predictions.
Speaker 1: So they can remember relevant past.
Speaker 2: Events exactly, they can learn long range dependencies. A crucial
Speaker 2: step we saw was transforming the raw stock prices using
Speaker 2: log differences. This helps achieve stationarity.
Speaker 1: Stationarity does that mean again?
Speaker 2: It means the statistical properties of the time series, like
Speaker 2: its average and variants, don't change over time. Most time
Speaker 2: series models, including lstm's, work much better with stationary data.
Speaker 2: Raw stock prices usually aren't stationary, they tend to trend
Speaker 2: upwards over time log differences often stabilize them.
Speaker 1: Okay, and we use a time series generator to prepare
Speaker 1: the data.
Speaker 2: Yeah, that's a handy tool and caras it automatically creates
Speaker 2: batches of sequential data for the LSTM. You tell it
Speaker 2: how many past days to look back at, say ten days,
Speaker 2: to predict the next day's value. It handles creating those
Speaker 2: sliding windows for you.
Speaker 1: And then we built the actual LSTM.
Speaker 2: Model in caress right sequential model, defining the LSTM layers,
Speaker 2: specifying the number of units or memory cells in each layer,
Speaker 2: and crucially the input shape which has to match the
Speaker 2: look back window and number of features. And of course
Speaker 2: tuning is vital here too, experimenting with the lookback window
Speaker 2: size maybe three days works better than ten or vice versa,
Speaker 2: adding multiple LSTM layers, maybe with dropout in between to
Speaker 2: prevent overfitting on the sequence.
Speaker 1: And refining the optimizer like the ATOM optimizer's learning rate definitely.
Speaker 2: Finding the right learning rate is often critical for stable training,
Speaker 2: especially with time series where things can fluctuate a lot.
Speaker 1: Okay, this next one is maybe the most mind bending
Speaker 1: generative adversarial networks chans creating synthetic images like faces totally
Speaker 1: from scratch. How does that even work?
Speaker 2: It is pretty amazing stuff. Jans are a really special
Speaker 2: type of unsupervised learning model. They're generative because their goal
Speaker 2: is to create new data that looks like the training data,
Speaker 2: and their adversarial because they involve two neural networks locked
Speaker 2: in a competition.
Speaker 1: The generator and the discriminator exactly.
Speaker 2: The generator takes random noise as input and tries to
Speaker 2: transform it into a realistic looking image like a face.
Speaker 2: The discriminator, meanwhile, is shown both real images from the
Speaker 2: training set and fake images from the generator and has
Speaker 2: to learn to tell the difference is this image real
Speaker 2: or fake?
Speaker 1: So it's like a counterfeitter, the generator trying to fool
Speaker 1: a detective, the discriminator.
Speaker 2: That's a great analogy, and the key is they both
Speaker 2: get better over time because of each other. The generator
Speaker 2: learns to make more convincing fakes to fool the discriminator.
Speaker 2: The discriminator gets better at spotting fakes, forcing the generator
Speaker 2: to improve further. It's this constant cat and mouse game.
Speaker 1: So how do you even know if a JAN is
Speaker 1: working well? Is there an accuracy score?
Speaker 2: That's the tricky. Unlike most models where you have clear
Speaker 2: metrics like accuracy or RMS, evaluating Jan's is often subjective.
Speaker 2: There's no single number that tells you how real the
Speaker 2: generated images are. Often you just have to look at
Speaker 2: the output and judge visually. Are the generated faces plausible?
Speaker 2: Do they look realistic? Although there are some more advanced
Speaker 2: metrics researchers use, visual inspection is still common.
Speaker 1: Okay, so how are these two networks actually built? The generator?
Speaker 2: The generator typically starts with a vector of random noise.
Speaker 2: It then uses layers like dense layers, reshaping layers, and crucially,
Speaker 2: two D transposed convolutions.
Speaker 1: Transposed convolutions. What do they do?
Speaker 2: They're essentially the opposite of regular convolutions. They upsample the
Speaker 2: feature maps, making the image larger. So you go from
Speaker 2: a small noise vector through layers that gradually increase the
Speaker 2: spatial dimensions, maybe from twenty five by twenty five pixels
Speaker 2: to fifty by fifty until you get the desired output
Speaker 2: image size. You'll also see things like Batche normalization to
Speaker 2: help stabilize training and activation. Functions like ReLU and.
Speaker 1: The discriminator it's basically a classifier pretty much. Yeah.
Speaker 2: The discriminator is usually a standard convolutional neural network CNN.
Speaker 2: It takes an image reel or fake as input. It
Speaker 2: uses regular two D convolution layers, often with strides greater
Speaker 2: than one, which helps reduce the image dimensions as you
Speaker 2: go deeper. It might use leaky reel you activations, which
Speaker 2: sometimes work well in discriminators. Then eventually it flattens the features,
Speaker 2: maybe applies some dropout for regularization, and outputs a single probability,
Speaker 2: usually via a sigmoid function, representing the likelihood that the
Speaker 2: input image was real.
Speaker 1: And preparing the image data for this, Yeah, that involves
Speaker 1: loading JPEGs resizing.
Speaker 2: Yeah, consistency is key. You need to load all your
Speaker 2: real images maybe JPEG files, into numerical arrays, re size
Speaker 2: them all to the exact same dimensions like fifty by
Speaker 2: fifty pixels in the books example, and then typically stack
Speaker 2: them all into a single large four dimensional array number
Speaker 2: of images height with color channels. That's the format the
Speaker 2: networks expect.
Speaker 1: You train them together, right.
Speaker 2: You alternate training steps. You train the discriminator on a
Speaker 2: batch of real images labeled as real and fake images
Speaker 2: from the generator are labeled as fake, then you freeze
Speaker 2: the discriminator's weights and train the generator based on whether
Speaker 2: the discriminator was fooled by its latest fakes. The generator's
Speaker 2: goal is to produce images that the discriminator labels as real.
Speaker 2: It's this back and forth that drives the learning process,
Speaker 2: and tweaking parts of this, like the network architectures or
Speaker 2: training process, can lead to wildly different results.
Speaker 1: Wow. Okay, that was an incredible deep dive into hands
Speaker 1: on deep learning with R. We've really covered a huge
Speaker 1: amount of ground, everything from those foundational machine learning concepts,
Speaker 1: setting up the R environment, getting into the nitty gritty
Speaker 1: of artificial neural networks, and then exploring all these diverse applications.
Speaker 2: Yeah, we really have. We've seen how deep learning is
Speaker 2: powering critical areas like image recognition with those CNNs, how
Speaker 2: personalized recommender systems work using embeddings, how LSTMs tackled i'm
Speaker 2: series forecasting, and yeah, that utterly fascinating world of Jens
Speaker 2: that can literally generate entirely new data from scratch.
Speaker 1: So what does this all mean for you the listener?
Speaker 1: Hopefully you've gained some really practical insights into how these
Speaker 1: complex models are actually build, how they're optimized, and how
Speaker 1: they're applied in real world scenarios. And crucially, you've seen
Speaker 1: the specific R libraries and techniques that make it all possible.
Speaker 1: Within that ecosystem, it's kind of a shortcut to understanding
Speaker 1: the nuances, right, the things that really set these powerful
Speaker 1: tools apart.
Speaker 2: Absolutely, and I think the true power of deep learning,
Speaker 2: when you boil it down, lies in this amazing ability
Speaker 2: to learn incredibly intricate patterns and generate really profound insights,
Speaker 2: often from just vast amounts of raw data. And maybe
Speaker 2: if we connect this to the bigger picture, consider that
Speaker 2: adversarial training concept from Jans, you know, where two components
Speaker 2: learned by competing against each other. Yeah, could that idea
Speaker 2: inspire completely new approaches to problem solving and fields way
Speaker 2: beyond just generating images. Maybe areas like I don't know,
Speaker 2: scientific disc discovery or designing complex systems, where you could
Speaker 2: set up competing agents or models and that iterative competition
Speaker 2: actually drives you towards optimal solutions.
Speaker 1: That is a thought provoking idea using that competitive dynamic. Yeah,
Speaker 1: very interesting, something to definitely mull over. Well, that's all
Speaker 1: the time we have for this deep dive.