Welcome to the deep dive. We're taking on a really crucial area today, deep learning specifically tailored for you, the data architect. You shared some great material and our focus is this book Deep Learning for Data Architects by Shikhar Condowall.
Yeah, it looks like a really solid resource, good for understanding how these advanced techniques fit into data infrastructure.
It's pretty current too, right BPB Online twenty twenty four.
That's right ISBN nine seven eight nine three five five five one five three nine one. So yeah, very up to date.
Absolutely, and the author, Shikhar Kundawal, he seems to really know with stuff. Senior AI and data scientists in Hamburg, got a master's in data science specialized in computer vision and wow, over fifteen years in AI and machine learning.
And experience with all the big cloud platforms aws, Google Cloud as your IBM cloud. That's important.
Definitely brings that practical, real world angle. And apparently he's also into marathons and CrossFit huh.
Yeah, saw that because that drive carries over and seriously, that real world experience is invaluable, especially for bridging that gap between complex AI theory and the practical stuff data architects deal with. Yeah, the book's dedication is quite nice too, to his wife, daughter, his uncle Danesh, a bookseller.
A bookseller uncle, that's a nice touch.
Yeah, and parents, PPB publications, colleagues, readers. Gives it a human feel.
Okay, so let's unpack this for you, our listener. The mission here is to pull out the key knowledge from this book that's well directly relevant to your work as a data architect. We'll look at how deep learning concepts get implemented using Python, focusing on the practical side without getting too bogged down in dense theory.
Think of it as like your fast track to understanding the deep learning bits that matter for your field exactly.
We want to give you a clear picture of the main principles the tools, so you can see how deep learning could maybe be integrated strategically into the architectures you're designing or managing.
Sounds good. Where does the book start?
Right at the foundation Python for data science? Chapter one. There's a quote there that kind of sets the stage. You can't build a great building on a weak foundation, you know the one.
You must have a solid foundation if you are going to have a super strong structure.
Yeah, It's the perfect analogy, isn't it totally Just like a building needs that solid base. Any serious deep learning setup relies heavily on Python and its whole library ecosystem.
So the book jumps right into the essential libraries.
YEP kicks off with pandas for data handling, NUMBPI for all the numerical stuff, the real.
Workhorses, you know, right basics.
Then matt plot Live and Seaborn for visualization, super important for understanding data flows or how models are doing you.
Can't really see inside the black box out of wise exactly.
Then there's psyche learn, the big toolkit for general machine learning tasks, and of course TensorFlow and Cares.
The deep learning powerhouses definitely.
And for images it mentions psychic image and open cv too, And it.
Gives you the install commands like pip install. Yeah, it provides the basic PIP install commands and actually a useful tip for notebook users using cyst dot executable and m pip install an su library name oh.
Right, to make sure it installs in the.
Right place exactly avoid some common headaches.
Okay, so beyond us installing, what about using them Pandas for data io.
Yeah, It covers reading and writing data extensively. You know the usual suspects, CSV with talks and read CSV standard Excel using two excel READEXL Jason two Two's dreets in pretty comprehensive for a data architect needing to connect different systems.
That broad format support is key, and it gets.
Into some interesting ones too, like read clipboard for quick copy pasting.
Data, pandy for testing small things, and.
Read ATML to pull tables straight from websites, which is pretty cool.
Wait, multiple tables on a page.
Yeah. It mentions the match parameter. You can tell it like look for a table with specific text in it or near it. Yeah. Really useful for automating data scraping pipelines.
Okay, that is useful, And it.
Even points to a blog post by the author about other formats like parquet and pickle. So PANDAS really positions itself as a central hub for data movement.
But it's not just about reading data right. Efficiency matters, especially with big data sets.
Oh absolutely, the book stress is optimizing pandas dot reacsv huge for data architects worried about resources, performance, cost, How well the d type parameter. First you can tell pand is exactly what data type each column should be, like this one's an integer, this one's a smaller float.
Instead of letting pandas guests and maybe use more memory than needed precisely.
The book gives an example with housing data setting rooms to MP dot nine ten thirty two distance to MP dot float sixteen. Stuff like that saves a noticeable chunk of memory.
Okay, I see sense.
Then there's use calls. Just tell which columns you actually need.
Ah, so don't even load the rest into memory.
Right if you only need five columns out of fifty, why load all fifty again? The example shows a big memory drop just by specifying.
The columns, and you can come those d type and use calls.
Yeah, that's where the real magic happens. For optimization, the book shows combining them can bring memory usage way down, sometimes to just like a few thousand kbs. That's significant for pipeline performance definitely.
What about data sets that are just too big, like won't fit in memory at all.
That's where chunk size comes in critical for architects designing for massive data volumes unless you process the file piece by piece in manageable chunks.
Okay, So solid foundation with Python efficient data handling. Where does it go next? It seems like chapter two tackles real world data challenges.
Yeah, it shifts focused to you know, the practical side of turning that raw data into something useful, into insights, because just having the data store isn't the endgame.
Right right.
You need to understand it, prepare it exactly, and especially before feeding it into say a deep learning model. So the book introduces some automated tools for exploratory data analysis da.
AH tools to speed up that initial data understanding phase. Useful for an architect looking at a new source definitely.
First one mentioned is panned.
Is profiling heard of that one?
Yeah? Generates reports right yep, pretty comprehensive reports, gives you stats, distributions, potential issues like missing values, correlations all in one go. Great for getting a fast assessment of a data set you need to integrate.
Saves a lot of manual plotting and checking for.
Sure, and it can generate interactive widgets in Jupiter notebooks using dfprofile dot to widgets. Good for collaboration. It also mentions a minimal mode for really huge data sets so it doesn't choke practical. What else next is sweet viz similar goal DA, but it really focuses on creating nice interactive HTML reports.
HTML reports so easy to share exactly you can.
Share them with people who aren't running Python. And a key feature is comparing two data sets side by side.
Wooh, that sounds useful, like for data migration, validation or ab test results.
Precisely, the commands are simple sv dot analyzed df dot SHOWTML for one data set as vadas compare DF ONEDF two do showtmail compare report dot HGMO for two. The visual comparison could be really powerful.
Okay, cool, what's next? Autoviz sounds fast.
That's the idea. One line of code for visualizations av dot autovis your data dot CSV deepvar target column.
One line. What does it show?
It tries to automatically figure out the important relationships and generates a bunch of relevant plots, scatterplots, distributions, box plots, heat maps, whatever seems appropriate for the variables. Quick visual scans for patterns or problems.
Efficient. Then there's a LUX that integrates with Jupiter widgets.
Yeah. Lux is interesting because it works within the notebook. You get a toggle Panda Seleux button on your data frame.
So you switch between table and visuals.
Kind of when you display a data frame. LUX ads recommendations for visualizations based on the current data, or you can state in intent like DF dot intent age fair, and it generates plots relevant to.
Those columns intent based.
I like that it tries to be smarter about what you might want to see. You can save reports to HTML too, DF save EACHTML clear the intent even get more advanced with vislist. It's quite interactive.
Lots of EDA tools. What about modeling good transition?
The next tool is lazy predict. It shifts gears towards quickly trying out lots of different machine learning models.
Lazy like it does the work for you pretty much.
If you're thinking about adding mL but aren't sure which algorithm might work best. Lazy predict runs your data through a whole bunch of standard classifiers or regressors.
How does that work?
You use lazy classifier or lazy regressor, Give it your training and testing data and call fit. It then spits out a table comparing the performance metrics accuracy F one score are squared whatever for dozens of models.
Wow. Okay, so a quick benchmark to see what directions.
Look promising exactly helps you understand potential complexity and performance trade offs early on before you commit architecturally.
And the last one in this chapter, Pi Carrot.
PI Carrot is a no other automated mL library, maybe a bit more comprehensive than Lazy Predict. It covers more of the workflow, model building, tuning, evaluation, even some deployment.
Aspects, so more end to end.
Yeah, you can do things like compare models to get a leader board and create model to build say an extra trees model and plot model to visualize confusion matrices, future importance learning curves.
So it helps understand the whole life cycle.
Right for an architect, Understanding that full process helps inform decisions about deployment, monitoring, scaling the mL components within the larger system.
Okay, that's a powerful set of tools for understanding and initially modeling data. What's next? Chapter three gets into the core right, building neural networks.
Yes, Chapter three dives into artificial neural networks an NS. It starts conceptually explaining the inspiration from biology, our.
Brains, the whole neurons and connections idea exactly.
It provides that helpful mental model Then it breaks down the basic building blocks, the artificial neurons, the weights connecting them, the BUI, and importantly, activation functions.
Crucial for an architect to understand the components if they're supporting the infrastructure.
Absolutely. It briefly covers the feed forward process how data flows through in the basic neuron math. Then yeah, activation functions, big emphasis there.
Why are they so important?
The introduced nonlinearity. Without them, the network could only learn linear relationships no matter how many layers you stack. The book mentions the common ones sigmoid.
Re lu, real use seems everywhere.
It's computational efficient. Also ten softmas for multi class outputs and variations like leaky reilu e lu. That nonlinearity is key for learning complex patterns.
Got it? So data goes forward, nonlinearity is added, how does it learn?
That's where the loss function comes in. It measures how wrong the network's predictions are compared to the actual answers. Quantifies the error a performance metric basically right, And once you can measure the error, you use backward propagation backprop to figure out how to just the weights and biases to reduce that error. That's the learning part.
Okay, and the book defines the training jargon epochs batches.
YEP defines epoch one full pass through the training data batch, a subset of data used in one update step iteration one update step. Also optimizers the algorithms that do the weed adjustments like sgd armsprop atom atoms another common one very common, and the learning rate, which controls how big those adjustments are. Understanding these helps estimate resource needs training times within an architecture makes sense?
Does it show how to build one?
Yes? It uses Keras for practical examples. First, a binary classification model for breast cancer prediction using a standard data set from psychic.
Learn, so predicting one of two outcomes.
Exactly, walks through loading libraries looking at the data, then building the Keras model layer by layer input layer, a hidden layer with.
Re lu how many neurons.
The example uses, I think sixteen in the hidden layer, then an output layer with one neuron and a sigmoide activation because it's binary, then compiling it, choosing the atom optimizer the right loss function like sparse categor a cross entropy, then training with model.
Out fit and evaluating.
Yeah looks at loss and accuracy. Yeah shows how to plot, a confusion matrix, get a classification report gives you the whole assessment.
Picture, which is vital if you're monitoring these models in production.
Absolutely. Then interestingly it builds a deeper network for the same problem, adds another hidden.
Layer to see if it improves, right, and.
The book notes that the deeper network performed better. Highlights that trade off more complexity potentially better results, but also more computation architectural consideration.
Good point. What about other types of problems?
It follows up with a regression example, predicting Boston housing prices, again using a built.
In data set, so predicting a number, not a category correct.
And here it emphasizes preprocessing more train test split, of course, but also features scaling using standard scaler often crucial for regression with neural nets.
Why scaling helps.
The optimizer converge better when features have very different ranges. The model itself is similar infut layer matching the number of features a hidden layer maybe one hundred and twenty eight neurons with REALU, and then the output layer is just one neuron with a linear.
Activation linear because the output is a continuous price.
Exactly, and the lass function changes too. Uses mean squared error standard for regression, then trains and evaluates based on the MSSE on the test set.
So two clear examples. Classification and regression covers the basics.
Well yeah, provides a solid caras foundation for building these fundamental network types. Essential knowledge for architects dealing with different m model types.
Okay, moving on to chapter four, Convolutional neural networks CNNs, big topic for images.
Huge. The book starts by explaining why you need CNNs for images. Why just flattening the pixels and feeding them into a standard network isn't ideal?
Right? You mentioned losing spatial info exactly.
It's sensitive to shifts distortions. CNNs are designed to handle that spatial hierarchy in images. It introduces the core idea kernels or.
Filters, the little squares that slide over.
The image yep, and the convolution operation itself. How the filter multiplies in sums pixel values to create a feature map, highlighting specific patterns like edges or textures. Understanding this helps think about how image data needs to be handled architecturally.
It also covers stride and padding.
Right. Stride is how many pixels the filter jumps each time. Padding is adding borders to control the output size. Then it explains how convolution works on color images, RGB, multiple channels, and how using multiple filters lets the network learn different features simultaneously.
Okay, so convolution extracts features. What else is in a CNN?
Pooling layers usually max pooling, They downsample the feature maps, make the network more robust of variations, reduce computation, brings things down basically. Yeah, then flattening, taking the final two D feature maps and turn them into a one D.
Vector to feed into a regular dense layer exactly.
The final part is usually one or more dense layers for the actual classification, just like in the A and NS we discussed.
Does it show an example, of course.
The classic MNIST data set handwritten digits walks through loading at via keras preprocessing.
Like reshaping for the color channel.
YEP reshaping to add that channel dimension even though it's greyscale and one hot encoding the labels zero to nine.
And the CNN architecture.
It shows building a typical CNN convy two D layers with ReLU maybe batch normalization for stability, max pooling two D layers, the flatten layer, and dense layers with soft max at the end for the ten digit.
Classes compiled with categorical cross entropy.
Right an atom optimizer usually then training plotting the ACCURACYLS curves, making predictions, showing the confusion matrix. The whole workflow for image classification very practical.
What about tuning? CNNs have lots of knobs.
To turn good point. The chapter introduces hyper parameter tuning using Keros tuner, but switches to the fashion MNA. Yes data set similar idea, but images of clothing items. Why tuning because just picking the number of layers or filter sizes by guesswork is an optimal. Things like learning rate, activation functions, number of units, they all impact performance. Tuning finds the best.
Combo, and karristuoner helps automate that search exactly.
The book shows how to install it PIP installed moles Karras tuner. Then define a model building function. Inside that function, you define the search space for your hyper parameters, like.
Try learning rates of biller point zero one or point zero zero one.
Precisely, or try one versus two dense layers or different numbers of units. You tell Karrastuner the ranges are choices. Then you create a tuner object like dat hyperband hyperband it's one of the search algorithms Karris tuner efforts. Then you run tuner dot search and it trains lots of model variations to find the best hyper parameters.
And you can get the best ones out yeap tuner I.
Get best hyper parameters, gives you the optimal settings it found. Then you build the final model with those best settings and train it properly.
And evaluate that best model shows the real benefit.
Right shows how tuning can push performance higher. Important for architects thinking about optimizing training pipelines.
Okay. Chapter five moves to a specific application, Optical Character recognition OCR.
Yeah, turning text in images into actual usable text data. Super important for digitizing documents invoices, bank statements, even reading road signs for autonomous cars.
Lots of applications. What tools does it cover?
It introduces several Python OCR libraries.
Starts with tessak, the classic open source one from HP, then Google that's the one.
Mentions installation, setting the path, and a basic demo using Pietes image does string straightforward for simple cases?
What about more modern approaches?
It covers Kara's okey This uses deep learning models under the hood, shows installing it creating an OCR pipeline and using pipeline dot recognize on an.
Image, so leveraging pre trained models exactly.
It often handles more varied images better than traditional methods.
Okay, any others.
Easy OCR the name says it all right. It highlights its simplicity and really good multi language support out of the box.
Multi language that's a big plus.
Definitely shows installation pip install eazokey initializing a reader with language codes like n fr D and then just reader dot read text pretty simple.
API nice does handle PDFs, that's common.
Mentions that, yeah, needs helper tools like Poplar utils and pdf two image to first convert PDF pages to images. Then you run easy OCR on the images. A common workflow.
Good practical tip. One more tree.
OCR stands for transformer OCR. It's described as more of a research project using transformer models like from NLP adapted for OCR on challenging natural.
Images transformers for OCR interesting.
Yeah, shows how cutting edge NLP architectures are crossing over. Shows installation pip install, transcute transformers, Loading the pre trained three OCR model and processor and running it represents the state of the art.
Okay, so several OCR options depending on the need Chapter six object detection.
Right moving beyond just what's in an image, classification or where text is ocr to finding multiple objects and drawing boxes around them.
It distinguishes that from classification and localization.
First, Yeah, clearly defined classification with one label per image, localization one object with a box, detection multiple objects, multiple boxes.
Then it lists some key algorithms like RCNN faster, RCNN.
YEP, the RCNN family which are influential but slower, and then the faster ones, SSD Single Shot Detector and YOLO you only look once. Mentions that accuracy versus speed trade off, which is always a factor.
How does it show implementing them SSD?
First, Yeah demonstrates SSD using PyTorch Hub. Steps include installing prerequisites, loading a pre trained SSD model from Nvidia via the.
Hub, leveraging pre trained again smart.
Very common loading utilities, formatting the input image, running detection, filtering results by confidence score, and then drawing the box and labels on the image. Shows a practical pipeline using PyTorch.
What about Yolo?
For Yolo, it shows the Darknet approach, cloning the original Darknet c code repository, compiling it with make downloading the pre trained Yolo.
Weights, so a different ecosystem right, and.
Then running detection from the command line using the compiled Darknet executable shows a different but also a very popular way to use a leading object detection model, especially known for its speed.
Good to see different implementation styles. Next up, chapter seven, image segmentation, getting even more detailed.
Exactly pixel level classification, assigning a category label to every single pixel in the image. The book contrasts it clearly with classification whole image and detection bounding boxes.
So understanding the exact shape of objects precisely.
It lists quite a few segmentation architectures data architects might hear about unit fcnaight, mask our CNN, dp LAB lots of them.
Does it implement all of them?
Well, that would be a lot. It focuses on providing Python implementations for three significant ones unit FCN eight and mask RCNN.
How does it show UNT.
Using tensiflow caras outlines prerequisites, data loading and prep using the Oxford it PET.
Data sets a segmenting cats and dogs right.
Building the unit model architecture which has that characteristic use shape with skip connections, compiling training and then visualizing the output segmentation masks on top of the pet images unit is huge in medical imaging too.
Okay, what about FCN eight.
FCN stands for fully convolutional network. It was a key step towards modern segmentation. The implementation shows importing libraries, defining the FCN eight model, replacing dense layers with convolutional ones, generating sample data, compiling, training, and evaluation, more foundational.
And mask RCNN sounds related to object.
Detection, it is it extends faster RCNN. It detects objects with bounding boxes and generates a pixel level mask for each detected object instance. So instant segmentation, best of both worlds in a way. The book shows how to implement it using tense, often leveraging models from their TPU repository. Steps include loading libraries, setting up category labels, loading an image using a pre train mast RCNM model for inference, and then visualizing both the boxes and the masks. Very powerful.
Okay, that covers a lot on image analysis. Chapter eight shifts to sequences RNNs.
Yes, recurrent neural networks for data where order matters natural language, time series audio. Their key feature is that internal memory or hidden state that remembers past information.
Right the recurrent part exactly.
The book briefly mentions the training algorithms like BPTT backpropagation through time, and the more advanced architecture is designed to handle long sequences better LSTM long short term memory and gru gated recurrent unit.
They fix problems with basic RNNs.
Yeah, they have internal gates that control the flow of information, helping them remember relevant stuff from further back in the sequence and avoid the vanish ingradient problem.
Does it show how to use them?
It starts with a very simple RNN in Keras for basic sequence prediction. Then it dives into a practical LSTM example for time series forecasting, predicting airline passenger numbers.
Classic data set YEP.
Covers loading, normalizing the data, splitting, creating the sequences correctly for the LSTM input, building the Carras LSTM model, training it, making predictions and evaluating very relevant for predictive analytics architecture.
What about grus.
It shows a gru example for a sentiment analysis on the IMDb movie review data set.
Text data Now.
Right shows loading the data reviews labeled positive negative, A crucial step of padding the sequences, so they are all.
The same lengths because RNNs expect fixed length inputs, usually.
Right then building the gru modeling keras, training it to classify sentiment, and evaluating its accuracy. Grus are often a bit simpler and faster to train than LSTMs sometimes performs similarly.
So RNNs LSTMs grus for sequences. Chapter nine gets creative.
Jams Generative adversarial networks. Yeah, really fascinating area. The book explains the core idea to networks, a generator and a discriminator battling it out.
The generator makes fake data, the discriminator tries to spot the fakes exactly.
Through this competition, the generator gets better and better at making realistic synthetic data, images, text, whatever. The book lists some types Vanilla JAN, conditional JAN.
DCGN DCGN deep convolutional.
Use, the CNNs YE also voser seen JAN style. Again, lots of variations.
How does it implement them?
Vanilla First Yeah provides Vanilla Jan implementation using PyTorch shows defining the generator and discriminator networks, the loss functions the optimizers, the training loop where they alternate updates and visualizing the generated images gives you the basic.
Mechanics then dc JAN. What's the key difference the.
Architecture DCGN uses convolutional layers in both generator using transposed convolutions to upsemple and discriminator. This works much better for images producing more stable training and realistic results.
And it shows a DCJAN implementation.
Yes using TensorFlow. This time covers imports, defining the convolutional generator and discriminator models, the specific loss functions and optimizers for JANS, the training function, and how to periodically save generated images to see the progress.
So you can watch it learn to make faces or digits or whatever exactly.
And also briefly mentioned style agan as a more advanced technique for controlling the style of the generated output.
Okay, jens for generation. What's the final chapter? Chapter ten Transformers.
The current giants of NLP. Yeah, Transformers focuses on their key innovation. The self attention mechanists.
Allows them to weigh word importance.
Right understand which words in a sentence are most relevant to understanding a specific words meaning in that context. That's how they handle long range dependencies so well. It lists the famous ones Bert gpt or Berta et.
Cetera huge impact on language tasks.
Absolutely. The book does a good job explaining the difference between older non contextual embeddings like word to vec where bank always has the same vector, and the contextual embeddings from models like BURT, where Riverbank and Savings Bank give different vectors.
For bank that context is crucial. Does it show code?
Yeah, uses the transformers library from hugging Face shows getting embeddings from WORDTVEK versus BURT to illustrate that contextual difference.
I'm using BURT and GPT.
Yes. Shows a BERT implementation for text understanding, loading the tokenizer and model preparing inputs token id's attention masks, then a GPT implementation for text generation loading cognizer, model prepping input generating text continuation. Briefly mentions fine tuning too.
So practical examples of using these powerful pre trained.
Models exactly shows how accessible they become via libraries like transformers.
Wow. Okay, We've covered a massive amount of ground in this deep dive, mirroring the book's journey from Python basics and efficient data handling.
Right through those EDA and rapid modeling.
Tools to the core neural network types ann's CNNs for images, RNNs for sequences.
Then GM's for jen generation, and finally transformers for language, plus those specific applications like OCR and object detection.
It really is a broad overview of deep learning relevant to data architects.
It definitely provides that comprehensive sweep. And while we've stayed at a fairly high level here, hopefully giuse you our listener a solid map of the key concepts and tools discussed in deep learning for data architects.
Absolutely, and we definitely encourage you to check out the book itself for all the code details, the deeper explanations, and more guidance on actually applying this stuff to your architectural challenges.
For sure. You know, this whole exploration really brings up a fundamental thought, doesn't it. What's that just how profoundly our ability to process and understand complex data is changing, whether it's through these smarter analysis tools or these sophisticated deep learning models. How is that fundamentally reshaping the role, the capabilities, maybe even the strategic importance of the data architects in today's world.
That's a great point something for everyone listening to really consider as they think about the future of data systems and their place within them,