Hands-On Deep Learning with R: A practical guide to designing, building, and improving neural network models using R

Welcome to the deep Dive. Today, we're marking on a journey into the powerful world of deep learning, seen specifically through the lens of R.

That's right.

Our mission is to extract the most important insights and practical applications from hands on deep learning with R by Michael Paulis and Roger Devine.

And this isn't just about theory, right, think of this as your guide to designing, building, and truly improving neural network models. We're distilling it down into core concepts and some surprising applications. It's kind of a shortcut really to being genuinely well informed in this complex space.

So, whether you're prepping for a big meeting, maybe just catching up on the latest in data science, or you're simply like insatiably curious, prepare for some serious aha moments. Let's start at the beginning. Then, deep learning it's a powerful subset of machine learning, but they fundamentally share a lot of common ground. What are some of those essential building blocks?

Well, at its core, it's all about preparing your data for modeling. The book uses a great example actually with

Right because bad guesses could throw the whole model.

Off exactly, and filtering out variables that don't add any real information. Think about columns where every single value is identical, like maybe a site idea. If you're only looking at one site or units, they just don't help.

And data quality isn't just about missing values, is it. We also saw the importance of checks like confirming provisional or ratified values, making sure you know the status of the data, and transforming data types too, ensuring numeric data isn't accidentally stored as text, which happens more than you'd think.

Oh definitely. Then comes the actual model training, so that means splitting your data into training and testing sets, typically a good chunk maybe seventy or eighty percent for training, and then choosing the right algorithm. The book highlights exch boost as a pretty robust gradient tree boosting method boosting.

That's different from something like a random.

Forest, right, Yeah, very different. What's important about boosting methods like xt boost is that they learn iteratively. Each new model essentially tries to correct the mistakes of the previous one. It's a refinement process. Random forests, on the other hand, use bagging. They build many independent models and sort of average their results. Both powerful but different approaches.

Okay, that makes sense. This brings us to a critical point. Then, how do you truly evaluate your model's results? Because simple accuracy can be really misleading? Can it?

Absolutely?

The credit card fraud example in the book perfectly illustrates this. If only say, zero point one percent of transactions are fraudulent, a model predicting no fraud every time gets ninety nine point nine percent accuracy, But it's completely useless. It misses every single instance of actual fraud precisely.

That's why just looking at accuracy it can be well dangerously deceptive. Sometimes it really forces you to think about the cost of being wrong. You need metrics like mean absolute error MA or root means squared error rmse E and RMS.

That's the one that squares the errors.

Yeah, and the real insight with RMSC isn't just the math. It's that it forces you to heavily penalize those big, potentially catastrophic mispredictions. So if missing badly is really really bad for your project, RMSE is often the better choice. It makes those big errors hurt more in the calculation.

Got it, So, once we have a model, how do we actually make it better? The book talks about strategies like cross validation.

Right cross validation. That's where you basically repeat the train to split multiple times with different slices of your data. It gives you a much more reliable estimate of how the model will perform on unseen data.

And early stopping. That sounds important too.

Yeah, early stopping is key. It means you monitor the model's performance on a valuelidation set during training, and you just stop the training if things haven't improved for say, twenty five rounds. Or EPOX prevents overfitting.

And grid searches for hyper parameter tuning, that's about finding the best settings.

Exactly systematically, trying out different combinations of settings like learning rates or tree depths to find that optimal configuration for your specific problem. And you know, we also briefly touched

All right, So, if you're listening and ready to get hands on with deep learning in R, what are the essential libraries we need to get started? Okay?

The primary work courses mentioned are H two O, mx NET and KERAS, and we also saw some more specialized packages like RBM for restricted Boltzmann machines and reinforcement.

Learning and installation. It's not always just installed packages, is it.

Some seem straightforward from KARAN the main R archives right.

Some are, but others yeah, like RBM or espressly karas often need a bit more worre Keras usually relies on TensorFlow running in the background, often in a separate Python environment like Conda or a virtual environment.

So you might need div tools or need to point R to the right Python installation exactly.

And mx net, for instance, might even need external libraries installed in your system first, like OpenCV for image stuff or open believes for linear algebra. It can get a little complex, but what's really insightful here, I think, is understanding the different strengths of each library within our karras gives you incredibly broad support for almost any neural network architecture you can think of and end CNN's MLPs, you

Powerful stuff in the book shows examples for each Yeah, we saw how to get a basic example running with each one, including a practical demo of pre processing the adult census data set, converting character columns to numbers using one hottened coding, scaling everything between zero and one standard but crucial steps.

Okay, let's dig into the deep part, How exactly does deep learning get that name? And what's really at its core?

Right? The deep comes from using multiple hidden layers made up of these artificial neuros. These layers are stacked and they mimic in a very very simplified way, how our brains process information. The real key insight is that each layer can learn progressively more complex features from the data.

How does that work in practice? For an image?

So imagine the first layer might identify basic edges or corners in an image. The next layer might combine those edges to detect simple shapes. A layer deeper might recognize textures or parts of objects, and so on. This hierarchical learning building complexity layer by layer.

That's what makes it deep and what does this structure mean for how they actually learn?

Well, the process starts with random weights. These are just numbers assigned to the connections between neurons, representing the strength of the connection. Then these weights are adjusted over and over again iteratively to minimize the difference the error between the network's predictions and the actual answers in your training data.

So it's constantly refining itself based on feedback.

Exactly, it's a continuous refinement process, very much like how those boosting algorithms learn from the errors of previous iterations.

Actually that makes sense, but okay, zooming in on those individual neurons, Yeah, how do they decide whether to fire or pass a signal forward? Ah?

Good question. That's where two key things come in, bias functions and activation functions. Bias functions you can think of them as shifting the decision boundary, allowing the model to better separate different classes of data. And activation functions they're the real decision makers inside each neuron. They take the weighted sum of inputs plus the bias and decide if and how strongly that neuron should fire and pass the signal to the next layer.

Right, and we looked at a whole range of these activation functions, didn't we From the simple on off heavy side.

Yeah, the heavy side is very basic, just a step, But the non linear ones are where things get interesting. There's the sigmoid function, which squishes values into a range between zero and one, really useful for probabilities or binary outcomes. Then its cousin the hyperbolic tangent or ten, which is similar but ranges from moneybo one to.

One and read lu seems really popular. Rectified linear units.

Oh yeah, ReLU is huge. It's simple. It outputs the input directly if it's positive and zero otherwise. That simplicity makes training much faster in many cases. But it has a potential issue called the eyeing ReLU problem, where neurons can get stuck outputting zero, So leaky ReLU is developed. It gives a tiny slope for negative inputs just to keep things flowing.

And swish was another one.

Swish Yeah, a more recent one that's shown good results. It's a smoother function, lots of options really.

And for classification tasks where you have like multiple categories dogs, cats, birds, the softmax function is key.

Right, absolutely essential. Softmax takes the outputs for each class and converts them into probabilities that all add up to one. So the model tells you I think it's seventy percent likely a cat, twenty percent a dog, ten percent a bird. Okay, And you know, the book even walks you through building a very basic network from scratch in just basar, just to illustrate how weights get updated and how a LIGNE

Step backpropagation, that's how the error gets used to update the weights exactly.

The error is calculated at the out and then it's propagated backward through the network layer by layer, telling each weight how much it needs to adjust to reduce that error. It's the core learning mechanism.

Fascinating stuff. Okay, let's move to applications. Image recognition is a huge one for deep learning. Can we use traditional machine learning for images at all?

You absolutely can. Yeah. Using what are sometimes called shallow nets, things like random forests or simple neural networks. You can apply them to data sets like fashion mnists Fashion.

Mnist, that's the clothing images instead of handwritten digits. Right.

It's a bit more challenging than the original mnist digits. But shallow nets their limitations become pretty clear when you move to larger, more complex real world images. They just struggle to efficiently capture all the intricate patterns.

And this is where convolutional neural networks CNNs really come into their own, isn't it? How do they manage it?

What really sets CNN's apart is their architecture specifically designed for grid like data like images, they automatically learn the right features directly from the pixels. They use specialized layers of convolution layers that apply filters across the image to detect specific patterns like edges, corners, textures, maybe even simple shapes, so.

They're not just looking at individual pixels anymore, not at all.

Then they often use pooling layers, which reduce the size the dimensionality, making the process more efficient and helping the network focus on the most important features. And techniques like adding padding say padding same can help control how quickly the dimensions shrink, letting you build deeper networks without losing information too fast.

And you can build really deep CNNs right stacking, multiple convolution and pooling layers.

Oh yes, that allows the network to learn this hierarchy of features. We talked about simple features in early layers combined into more complex ones and deeper layers. It's kind of analogous to how our own visual system works in a way.

So with these complex models, how do we optimize them effectively?

Good question? Optimization is key. We discussed various algorithms called optimizers, things like stochastic gradient descent SGD, which is a basic workhourse. Then RM's prop and ATAM is a very popular one nowadays. It sort of combines the ideas of arms PROP with momentum, often leading to faster convergence.

And choosing the right loss function is important.

To crucial for binary classification, binary cross entropy for multiple classes, categorical cross entropy for regression problems where you predict a number maybe means squared error, and sometimes you need metrics beyond just accuracy like cosine similarity or CHL divergence, especially if you're comparing probability distributions or embeddings.

Okay, and you mentioned ways to prevent overfitting like dropout layers.

Yeah, dropout is a really clever technique. During training, it randomly sets a fraction of neuron outputs to zero for each training example.

So it forces the network not to rely too heavily on any single neuron exactly.

It encourages redundancy and makes the network more robust and early stopping. Like we mentioned before, halting training when performance on a validation set stops improving is another vital tool against over fitting. Helps find that sweet spot for the number of training epochs right.

Okay, let's ship here's a bit. Multilayer perceptions or MLPs. What about them, particularly for signal detection tasks? What makes them distinct?

MLPs are kind of the classic foundational feed forward neural network. Their defining feature is that they only use fully connected layers.

Meaning every neuron in one layer connects to every single neuron in the.

Next layer, precisely. Unlike CNNs with their specialized convolution layers or RNNs with their recurrent connections, MLPs are just stacks of these dense fully connected layers. They're good general purpose learners, maybe less specialized than cms for images or LSTMs for sequences, and for MLPs.

We looked at some specific data prep steps, didn't we like trimming white space from categories. Why is that important? Ah?

Yes, it sounds trivial, But if you have mail with a leading space and mail without, the computer sees them as two totally different categories, so cleaning that up is essential.

And rescaling numeric values to a zero one range. Why do we do that rescale step?

Again, It's really about efficiency and stability. If you have one feature ranging from zero to one and another from zero to one million, the larger future can dominate the learning process. Scaling brings everything into the same range, so they contribute more equally, and it often helps the model's optimization process converge faster and more reliably.

Makes sense, and it was a rule of thumb for hidden layer size.

Yeah, a common juristic, just a starting point really is to try setting the number of nodes in a hidden layer to about two thirds of the input layer size. We saw how you could write functions in R using the mx net syntax in the book to easily test different node counts and even experiment with adding more hidden layers.

Okay, now let's talk about something we all encounter daily. Recommender systems. Yeah, streaming movies, online shopping. How do they actually work and where does deep learning fit in?

Right recommenders? Broadly, there are three main types. Collaborative filtering, which finds users similar to you and recommends what they liked, content based filtering, which recommends items similar to ones you've liked before based on their attributes, and habrid systems, which tried combine the best to both worlds.

Had a big challenge. Is the cold start problem right for new users or new items?

Exactly? If you're a new user, the system knows nothing about your tastes. If it's a brand new movie, nobody has rated it yet. That makes recommendations difficult.

Initially, what seemed really fascinating here was the idea of embeddings. How do these low dimensional vectors help?

Embeddings are a really powerful concept in deep learning, not just for recommendations. They basically learned dense low dimensional vector representations for things like users and items. Instead of dealing with huge, sparse matrices of user item interactions. You map users and items into this shared latent space, a coordinate system, if you will.

And closeness in that space means similarity.

Precisely users close to items they like and similar users close to each other. It captures these affinities efficiently, making it easy to calculate similarity like with a dot product, even when you don't have explicit ratings for everything.

And we looked at the Steam two hundred k do CSV data set, which uses implicit feedback.

Yeah, that was a great example. Instead of star ratings, it uses hours played. For video games. Sid Meier's Civilization V had huge hours logged by some users. This implicit data clicks, views, purchase history, playtime is often much more abundant and sometimes more revealing than explicit ratings.

So we saw preparing that data, doing some exploratory data analysis EDA to understand those interactions.

Yep, understanding who plays what for how long?

And then building a custom caris model using both user and bettings and item embttings.

Right, But then there's another layer. How do you account for inherent biases? Some users just play games way more than others, regardless of the specific game, and some games are just universally popular.

Ah, so you need to model those baseline tendons exactly.

Adding specific bias e bettings one for the average user's tendency and one for the average items popularity can really improve the model. It lets the main embeddings focus on the interaction effect the specific user item affinity separate from these general biases. In the books example, adding biases nearly doubled the trainable parameters, but led to much better recommendations.

Very clever. Okay, let's pivot to time series data. Stock price forecasting is the classic example. How does deep learning tackle this? Given that the order of events is so critical.

Time series is definitely unique, Unlike say, image classification, where you can shuffle the images with time series. The sequence is everything. You absolutely have to maintain chronological order when splitting data for training and testing.

Because the past predicts the future.

Basically, fundamentally, yes, the patterns are in the sequence. We compared this deep learning approach to traditional methods like ARIMA models. Arima can be good, but often struggles to predict complex patterns far beyond the training data it saw.

This is where recurrent neural networks are and ends and especially long short term memory LSTM networks come in. These are the game changers.

They really are for sequential data. LSTMs in particular are designed to have memory. They have internal mechanisms, these gates that allow them to retain information from previous steps in the sequence and use it for current predictions.

So they can remember relevant past.

Events exactly, they can learn long range dependencies. A crucial step we saw was transforming the raw stock prices using log differences. This helps achieve stationarity.

Stationarity does that mean again?

It means the statistical properties of the time series, like its average and variants, don't change over time. Most time series models, including lstm's, work much better with stationary data. Raw stock prices usually aren't stationary, they tend to trend upwards over time log differences often stabilize them.

Okay, and we use a time series generator to prepare the data.

Yeah, that's a handy tool and caras it automatically creates batches of sequential data for the LSTM. You tell it how many past days to look back at, say ten days, to predict the next day's value. It handles creating those sliding windows for you.

And then we built the actual LSTM.

Model in caress right sequential model, defining the LSTM layers, specifying the number of units or memory cells in each layer, and crucially the input shape which has to match the look back window and number of features. And of course tuning is vital here too, experimenting with the lookback window size maybe three days works better than ten or vice versa, adding multiple LSTM layers, maybe with dropout in between to prevent overfitting on the sequence.

And refining the optimizer like the ATOM optimizer's learning rate definitely.

Finding the right learning rate is often critical for stable training, especially with time series where things can fluctuate a lot.

Okay, this next one is maybe the most mind bending generative adversarial networks chans creating synthetic images like faces totally from scratch. How does that even work?

It is pretty amazing stuff. Jans are a really special type of unsupervised learning model. They're generative because their goal is to create new data that looks like the training data, and their adversarial because they involve two neural networks locked in a competition.

The generator and the discriminator exactly.

The generator takes random noise as input and tries to transform it into a realistic looking image like a face. The discriminator, meanwhile, is shown both real images from the training set and fake images from the generator and has to learn to tell the difference is this image real or fake?

So it's like a counterfeitter, the generator trying to fool a detective, the discriminator.

That's a great analogy, and the key is they both get better over time because of each other. The generator learns to make more convincing fakes to fool the discriminator. The discriminator gets better at spotting fakes, forcing the generator to improve further. It's this constant cat and mouse game.

So how do you even know if a JAN is working well? Is there an accuracy score?

That's the tricky. Unlike most models where you have clear metrics like accuracy or RMS, evaluating Jan's is often subjective. There's no single number that tells you how real the generated images are. Often you just have to look at the output and judge visually. Are the generated faces plausible? Do they look realistic? Although there are some more advanced metrics researchers use, visual inspection is still common.

Okay, so how are these two networks actually built? The generator?

The generator typically starts with a vector of random noise. It then uses layers like dense layers, reshaping layers, and crucially, two D transposed convolutions.

Transposed convolutions. What do they do?

They're essentially the opposite of regular convolutions. They upsample the feature maps, making the image larger. So you go from a small noise vector through layers that gradually increase the spatial dimensions, maybe from twenty five by twenty five pixels to fifty by fifty until you get the desired output image size. You'll also see things like Batche normalization to help stabilize training and activation. Functions like ReLU and.

The discriminator it's basically a classifier pretty much. Yeah.

The discriminator is usually a standard convolutional neural network CNN. It takes an image reel or fake as input. It uses regular two D convolution layers, often with strides greater than one, which helps reduce the image dimensions as you go deeper. It might use leaky reel you activations, which

And preparing the image data for this, Yeah, that involves loading JPEGs resizing.

Yeah, consistency is key. You need to load all your real images maybe JPEG files, into numerical arrays, re size them all to the exact same dimensions like fifty by fifty pixels in the books example, and then typically stack them all into a single large four dimensional array number of images height with color channels. That's the format the networks expect.

You train them together, right.

You alternate training steps. You train the discriminator on a batch of real images labeled as real and fake images from the generator are labeled as fake, then you freeze the discriminator's weights and train the generator based on whether the discriminator was fooled by its latest fakes. The generator's goal is to produce images that the discriminator labels as real.

Wow. Okay, that was an incredible deep dive into hands on deep learning with R. We've really covered a huge amount of ground, everything from those foundational machine learning concepts, setting up the R environment, getting into the nitty gritty of artificial neural networks, and then exploring all these diverse applications.

Yeah, we really have. We've seen how deep learning is powering critical areas like image recognition with those CNNs, how personalized recommender systems work using embeddings, how LSTMs tackled i'm series forecasting, and yeah, that utterly fascinating world of Jens that can literally generate entirely new data from scratch.

So what does this all mean for you the listener? Hopefully you've gained some really practical insights into how these complex models are actually build, how they're optimized, and how they're applied in real world scenarios. And crucially, you've seen the specific R libraries and techniques that make it all possible. Within that ecosystem, it's kind of a shortcut to understanding the nuances, right, the things that really set these powerful tools apart.

Absolutely, and I think the true power of deep learning, when you boil it down, lies in this amazing ability to learn incredibly intricate patterns and generate really profound insights, often from just vast amounts of raw data. And maybe if we connect this to the bigger picture, consider that adversarial training concept from Jans, you know, where two components learned by competing against each other. Yeah, could that idea inspire completely new approaches to problem solving and fields way

That is a thought provoking idea using that competitive dynamic. Yeah, very interesting, something to definitely mull over. Well, that's all the time we have for this deep dive.