Speaker 1: Welcome to the deep dive. We're taking on a really
Speaker 1: crucial area today, deep learning specifically tailored for you, the
Speaker 1: data architect. You shared some great material and our focus
Speaker 1: is this book Deep Learning for Data Architects by Shikhar Condowall.
Speaker 2: Yeah, it looks like a really solid resource, good for
Speaker 2: understanding how these advanced techniques fit into data infrastructure.
Speaker 1: It's pretty current too, right BPB Online twenty twenty four.
Speaker 2: That's right ISBN nine seven eight nine three five five
Speaker 2: five one five three nine one. So yeah, very up
Speaker 2: to date.
Speaker 1: Absolutely, and the author, Shikhar Kundawal, he seems to really
Speaker 1: know with stuff. Senior AI and data scientists in Hamburg,
Speaker 1: got a master's in data science specialized in computer vision
Speaker 1: and wow, over fifteen years in AI and machine learning.
Speaker 2: And experience with all the big cloud platforms aws, Google
Speaker 2: Cloud as your IBM cloud. That's important.
Speaker 1: Definitely brings that practical, real world angle. And apparently he's
Speaker 1: also into marathons and CrossFit huh.
Speaker 2: Yeah, saw that because that drive carries over and seriously,
Speaker 2: that real world experience is invaluable, especially for bridging that
Speaker 2: gap between complex AI theory and the practical stuff data
Speaker 2: architects deal with. Yeah, the book's dedication is quite nice too,
Speaker 2: to his wife, daughter, his uncle Danesh, a bookseller.
Speaker 1: A bookseller uncle, that's a nice touch.
Speaker 2: Yeah, and parents, PPB publications, colleagues, readers. Gives it a
Speaker 2: human feel.
Speaker 1: Okay, so let's unpack this for you, our listener. The
Speaker 1: mission here is to pull out the key knowledge from
Speaker 1: this book that's well directly relevant to your work as
Speaker 1: a data architect. We'll look at how deep learning concepts
Speaker 1: get implemented using Python, focusing on the practical side without
Speaker 1: getting too bogged down in dense theory.
Speaker 2: Think of it as like your fast track to understanding
Speaker 2: the deep learning bits that matter for your field exactly.
Speaker 1: We want to give you a clear picture of the
Speaker 1: main principles the tools, so you can see how deep
Speaker 1: learning could maybe be integrated strategically into the architectures you're
Speaker 1: designing or managing.
Speaker 2: Sounds good. Where does the book start?
Speaker 1: Right at the foundation Python for data science? Chapter one.
Speaker 1: There's a quote there that kind of sets the stage.
Speaker 1: You can't build a great building on a weak foundation,
Speaker 1: you know the one.
Speaker 2: You must have a solid foundation if you are going
Speaker 2: to have a super strong structure.
Speaker 1: Yeah, It's the perfect analogy, isn't it totally Just like
Speaker 1: a building needs that solid base. Any serious deep learning
Speaker 1: setup relies heavily on Python and its whole library ecosystem.
Speaker 2: So the book jumps right into the essential libraries.
Speaker 1: YEP kicks off with pandas for data handling, NUMBPI for
Speaker 1: all the numerical stuff, the real.
Speaker 2: Workhorses, you know, right basics.
Speaker 1: Then matt plot Live and Seaborn for visualization, super important
Speaker 1: for understanding data flows or how models are doing you.
Speaker 2: Can't really see inside the black box out of wise exactly.
Speaker 1: Then there's psyche learn, the big toolkit for general machine
Speaker 1: learning tasks, and of course TensorFlow and Cares.
Speaker 2: The deep learning powerhouses definitely.
Speaker 1: And for images it mentions psychic image and open cv too,
Speaker 1: And it.
Speaker 2: Gives you the install commands like pip install. Yeah, it
Speaker 2: provides the basic PIP install commands and actually a useful
Speaker 2: tip for notebook users using cyst dot executable and m
Speaker 2: pip install an su library name oh.
Speaker 1: Right, to make sure it installs in the.
Speaker 2: Right place exactly avoid some common headaches.
Speaker 1: Okay, so beyond us installing, what about using them Pandas
Speaker 1: for data io.
Speaker 2: Yeah, It covers reading and writing data extensively. You know
Speaker 2: the usual suspects, CSV with talks and read CSV standard
Speaker 2: Excel using two excel READEXL Jason two Two's dreets in
Speaker 2: pretty comprehensive for a data architect needing to connect different systems.
Speaker 1: That broad format support is key, and it gets.
Speaker 2: Into some interesting ones too, like read clipboard for quick
Speaker 2: copy pasting.
Speaker 1: Data, pandy for testing small things, and.
Speaker 2: Read ATML to pull tables straight from websites, which is
Speaker 2: pretty cool.
Speaker 1: Wait, multiple tables on a page.
Speaker 2: Yeah. It mentions the match parameter. You can tell it
Speaker 2: like look for a table with specific text in it
Speaker 2: or near it. Yeah. Really useful for automating data scraping pipelines.
Speaker 1: Okay, that is useful, And it.
Speaker 2: Even points to a blog post by the author about
Speaker 2: other formats like parquet and pickle. So PANDAS really positions
Speaker 2: itself as a central hub for data movement.
Speaker 1: But it's not just about reading data right. Efficiency matters,
Speaker 1: especially with big data sets.
Speaker 2: Oh absolutely, the book stress is optimizing pandas dot reacsv
Speaker 2: huge for data architects worried about resources, performance, cost, How
Speaker 2: well the d type parameter. First you can tell pand
Speaker 2: is exactly what data type each column should be, like
Speaker 2: this one's an integer, this one's a smaller float.
Speaker 1: Instead of letting pandas guests and maybe use more memory
Speaker 1: than needed precisely.
Speaker 2: The book gives an example with housing data setting rooms
Speaker 2: to MP dot nine ten thirty two distance to MP
Speaker 2: dot float sixteen. Stuff like that saves a noticeable chunk
Speaker 2: of memory.
Speaker 1: Okay, I see sense.
Speaker 2: Then there's use calls. Just tell which columns you actually need.
Speaker 1: Ah, so don't even load the rest into memory.
Speaker 2: Right if you only need five columns out of fifty,
Speaker 2: why load all fifty again? The example shows a big
Speaker 2: memory drop just by specifying.
Speaker 1: The columns, and you can come those d type and
Speaker 1: use calls.
Speaker 2: Yeah, that's where the real magic happens. For optimization, the
Speaker 2: book shows combining them can bring memory usage way down,
Speaker 2: sometimes to just like a few thousand kbs. That's significant
Speaker 2: for pipeline performance definitely.
Speaker 1: What about data sets that are just too big, like
Speaker 1: won't fit in memory at all.
Speaker 2: That's where chunk size comes in critical for architects designing
Speaker 2: for massive data volumes unless you process the file piece
Speaker 2: by piece in manageable chunks.
Speaker 1: Okay, So solid foundation with Python efficient data handling. Where
Speaker 1: does it go next? It seems like chapter two tackles
Speaker 1: real world data challenges.
Speaker 2: Yeah, it shifts focused to you know, the practical side
Speaker 2: of turning that raw data into something useful, into insights,
Speaker 2: because just having the data store isn't the endgame.
Speaker 1: Right right.
Speaker 2: You need to understand it, prepare it exactly, and especially
Speaker 2: before feeding it into say a deep learning model. So
Speaker 2: the book introduces some automated tools for exploratory data analysis da.
Speaker 1: AH tools to speed up that initial data understanding phase.
Speaker 1: Useful for an architect looking at a new source definitely.
Speaker 2: First one mentioned is panned.
Speaker 1: Is profiling heard of that one?
Speaker 2: Yeah? Generates reports right yep, pretty comprehensive reports, gives you stats, distributions,
Speaker 2: potential issues like missing values, correlations all in one go.
Speaker 2: Great for getting a fast assessment of a data set
Speaker 2: you need to integrate.
Speaker 1: Saves a lot of manual plotting and checking for.
Speaker 2: Sure, and it can generate interactive widgets in Jupiter notebooks
Speaker 2: using dfprofile dot to widgets. Good for collaboration. It also
Speaker 2: mentions a minimal mode for really huge data sets so
Speaker 2: it doesn't choke practical. What else next is sweet viz
Speaker 2: similar goal DA, but it really focuses on creating nice
Speaker 2: interactive HTML reports.
Speaker 1: HTML reports so easy to share exactly you can.
Speaker 2: Share them with people who aren't running Python. And a
Speaker 2: key feature is comparing two data sets side by side.
Speaker 1: Wooh, that sounds useful, like for data migration, validation or
Speaker 1: ab test results.
Speaker 2: Precisely, the commands are simple sv dot analyzed df dot
Speaker 2: SHOWTML for one data set as vadas compare DF ONEDF
Speaker 2: two do showtmail compare report dot HGMO for two. The
Speaker 2: visual comparison could be really powerful.
Speaker 1: Okay, cool, what's next? Autoviz sounds fast.
Speaker 2: That's the idea. One line of code for visualizations av
Speaker 2: dot autovis your data dot CSV deepvar target column.
Speaker 1: One line. What does it show?
Speaker 2: It tries to automatically figure out the important relationships and
Speaker 2: generates a bunch of relevant plots, scatterplots, distributions, box plots,
Speaker 2: heat maps, whatever seems appropriate for the variables. Quick visual
Speaker 2: scans for patterns or problems.
Speaker 1: Efficient. Then there's a LUX that integrates with Jupiter widgets.
Speaker 2: Yeah. Lux is interesting because it works within the notebook.
Speaker 2: You get a toggle Panda Seleux button on your data frame.
Speaker 1: So you switch between table and visuals.
Speaker 2: Kind of when you display a data frame. LUX ads
Speaker 2: recommendations for visualizations based on the current data, or you
Speaker 2: can state in intent like DF dot intent age fair,
Speaker 2: and it generates plots relevant to.
Speaker 1: Those columns intent based.
Speaker 2: I like that it tries to be smarter about what
Speaker 2: you might want to see. You can save reports to
Speaker 2: HTML too, DF save EACHTML clear the intent even get
Speaker 2: more advanced with vislist. It's quite interactive.
Speaker 1: Lots of EDA tools. What about modeling good transition?
Speaker 2: The next tool is lazy predict. It shifts gears towards
Speaker 2: quickly trying out lots of different machine learning models.
Speaker 1: Lazy like it does the work for you pretty much.
Speaker 2: If you're thinking about adding mL but aren't sure which
Speaker 2: algorithm might work best. Lazy predict runs your data through
Speaker 2: a whole bunch of standard classifiers or regressors.
Speaker 1: How does that work?
Speaker 2: You use lazy classifier or lazy regressor, Give it your
Speaker 2: training and testing data and call fit. It then spits
Speaker 2: out a table comparing the performance metrics accuracy F one
Speaker 2: score are squared whatever for dozens of models.
Speaker 1: Wow. Okay, so a quick benchmark to see what directions.
Speaker 2: Look promising exactly helps you understand potential complexity and performance
Speaker 2: trade offs early on before you commit architecturally.
Speaker 1: And the last one in this chapter, Pi Carrot.
Speaker 2: PI Carrot is a no other automated mL library, maybe
Speaker 2: a bit more comprehensive than Lazy Predict. It covers more
Speaker 2: of the workflow, model building, tuning, evaluation, even some deployment.
Speaker 1: Aspects, so more end to end.
Speaker 2: Yeah, you can do things like compare models to get
Speaker 2: a leader board and create model to build say an
Speaker 2: extra trees model and plot model to visualize confusion matrices,
Speaker 2: future importance learning curves.
Speaker 1: So it helps understand the whole life cycle.
Speaker 2: Right for an architect, Understanding that full process helps inform
Speaker 2: decisions about deployment, monitoring, scaling the mL components within the
Speaker 2: larger system.
Speaker 1: Okay, that's a powerful set of tools for understanding and
Speaker 1: initially modeling data. What's next? Chapter three gets into the
Speaker 1: core right, building neural networks.
Speaker 2: Yes, Chapter three dives into artificial neural networks an NS.
Speaker 2: It starts conceptually explaining the inspiration from biology, our.
Speaker 1: Brains, the whole neurons and connections idea exactly.
Speaker 2: It provides that helpful mental model Then it breaks down
Speaker 2: the basic building blocks, the artificial neurons, the weights connecting them,
Speaker 2: the BUI, and importantly, activation functions.
Speaker 1: Crucial for an architect to understand the components if they're
Speaker 1: supporting the infrastructure.
Speaker 2: Absolutely. It briefly covers the feed forward process how data
Speaker 2: flows through in the basic neuron math. Then yeah, activation functions,
Speaker 2: big emphasis there.
Speaker 1: Why are they so important?
Speaker 2: The introduced nonlinearity. Without them, the network could only learn
Speaker 2: linear relationships no matter how many layers you stack. The
Speaker 2: book mentions the common ones sigmoid.
Speaker 1: Re lu, real use seems everywhere.
Speaker 2: It's computational efficient. Also ten softmas for multi class outputs
Speaker 2: and variations like leaky reilu e lu. That nonlinearity is
Speaker 2: key for learning complex patterns.
Speaker 1: Got it? So data goes forward, nonlinearity is added, how
Speaker 1: does it learn?
Speaker 2: That's where the loss function comes in. It measures how
Speaker 2: wrong the network's predictions are compared to the actual answers.
Speaker 2: Quantifies the error a performance metric basically right, And once
Speaker 2: you can measure the error, you use backward propagation backprop
Speaker 2: to figure out how to just the weights and biases
Speaker 2: to reduce that error. That's the learning part.
Speaker 1: Okay, and the book defines the training jargon epochs batches.
Speaker 2: YEP defines epoch one full pass through the training data batch,
Speaker 2: a subset of data used in one update step iteration
Speaker 2: one update step. Also optimizers the algorithms that do the
Speaker 2: weed adjustments like sgd armsprop atom atoms another common one
Speaker 2: very common, and the learning rate, which controls how big
Speaker 2: those adjustments are. Understanding these helps estimate resource needs training
Speaker 2: times within an architecture makes sense?
Speaker 1: Does it show how to build one?
Speaker 2: Yes? It uses Keras for practical examples. First, a binary
Speaker 2: classification model for breast cancer prediction using a standard data
Speaker 2: set from psychic.
Speaker 1: Learn, so predicting one of two outcomes.
Speaker 2: Exactly, walks through loading libraries looking at the data, then
Speaker 2: building the Keras model layer by layer input layer, a
Speaker 2: hidden layer with.
Speaker 1: Re lu how many neurons.
Speaker 2: The example uses, I think sixteen in the hidden layer,
Speaker 2: then an output layer with one neuron and a sigmoide
Speaker 2: activation because it's binary, then compiling it, choosing the atom
Speaker 2: optimizer the right loss function like sparse categor a cross entropy,
Speaker 2: then training with model.
Speaker 1: Out fit and evaluating.
Speaker 2: Yeah looks at loss and accuracy. Yeah shows how to plot,
Speaker 2: a confusion matrix, get a classification report gives you the
Speaker 2: whole assessment.
Speaker 1: Picture, which is vital if you're monitoring these models in production.
Speaker 2: Absolutely. Then interestingly it builds a deeper network for the
Speaker 2: same problem, adds another hidden.
Speaker 1: Layer to see if it improves, right, and.
Speaker 2: The book notes that the deeper network performed better. Highlights
Speaker 2: that trade off more complexity potentially better results, but also
Speaker 2: more computation architectural consideration.
Speaker 1: Good point. What about other types of problems?
Speaker 2: It follows up with a regression example, predicting Boston housing prices,
Speaker 2: again using a built.
Speaker 1: In data set, so predicting a number, not a category correct.
Speaker 2: And here it emphasizes preprocessing more train test split, of course,
Speaker 2: but also features scaling using standard scaler often crucial for
Speaker 2: regression with neural nets.
Speaker 1: Why scaling helps.
Speaker 2: The optimizer converge better when features have very different ranges.
Speaker 2: The model itself is similar infut layer matching the number
Speaker 2: of features a hidden layer maybe one hundred and twenty
Speaker 2: eight neurons with REALU, and then the output layer is
Speaker 2: just one neuron with a linear.
Speaker 1: Activation linear because the output is a continuous price.
Speaker 2: Exactly, and the lass function changes too. Uses mean squared
Speaker 2: error standard for regression, then trains and evaluates based on
Speaker 2: the MSSE on the test set.
Speaker 1: So two clear examples. Classification and regression covers the basics.
Speaker 2: Well yeah, provides a solid caras foundation for building these
Speaker 2: fundamental network types. Essential knowledge for architects dealing with different
Speaker 2: m model types.
Speaker 1: Okay, moving on to chapter four, Convolutional neural networks CNNs,
Speaker 1: big topic for images.
Speaker 2: Huge. The book starts by explaining why you need CNNs
Speaker 2: for images. Why just flattening the pixels and feeding them
Speaker 2: into a standard network isn't ideal?
Speaker 1: Right? You mentioned losing spatial info exactly.
Speaker 2: It's sensitive to shifts distortions. CNNs are designed to handle
Speaker 2: that spatial hierarchy in images. It introduces the core idea
Speaker 2: kernels or.
Speaker 1: Filters, the little squares that slide over.
Speaker 2: The image yep, and the convolution operation itself. How the
Speaker 2: filter multiplies in sums pixel values to create a feature map,
Speaker 2: highlighting specific patterns like edges or textures. Understanding this helps
Speaker 2: think about how image data needs to be handled architecturally.
Speaker 1: It also covers stride and padding.
Speaker 2: Right. Stride is how many pixels the filter jumps each time.
Speaker 2: Padding is adding borders to control the output size. Then
Speaker 2: it explains how convolution works on color images, RGB, multiple channels,
Speaker 2: and how using multiple filters lets the network learn different
Speaker 2: features simultaneously.
Speaker 1: Okay, so convolution extracts features. What else is in a CNN?
Speaker 2: Pooling layers usually max pooling, They downsample the feature maps,
Speaker 2: make the network more robust of variations, reduce computation, brings
Speaker 2: things down basically. Yeah, then flattening, taking the final two
Speaker 2: D feature maps and turn them into a one D.
Speaker 1: Vector to feed into a regular dense layer exactly.
Speaker 2: The final part is usually one or more dense layers
Speaker 2: for the actual classification, just like in the A and
Speaker 2: NS we discussed.
Speaker 1: Does it show an example, of course.
Speaker 2: The classic MNIST data set handwritten digits walks through loading
Speaker 2: at via keras preprocessing.
Speaker 1: Like reshaping for the color channel.
Speaker 2: YEP reshaping to add that channel dimension even though it's
Speaker 2: greyscale and one hot encoding the labels zero to nine.
Speaker 1: And the CNN architecture.
Speaker 2: It shows building a typical CNN convy two D layers
Speaker 2: with ReLU maybe batch normalization for stability, max pooling two
Speaker 2: D layers, the flatten layer, and dense layers with soft
Speaker 2: max at the end for the ten digit.
Speaker 1: Classes compiled with categorical cross entropy.
Speaker 2: Right an atom optimizer usually then training plotting the ACCURACYLS curves,
Speaker 2: making predictions, showing the confusion matrix. The whole workflow for
Speaker 2: image classification very practical.
Speaker 1: What about tuning? CNNs have lots of knobs.
Speaker 2: To turn good point. The chapter introduces hyper parameter tuning
Speaker 2: using Keros tuner, but switches to the fashion MNA. Yes
Speaker 2: data set similar idea, but images of clothing items. Why
Speaker 2: tuning because just picking the number of layers or filter
Speaker 2: sizes by guesswork is an optimal. Things like learning rate,
Speaker 2: activation functions, number of units, they all impact performance. Tuning
Speaker 2: finds the best.
Speaker 1: Combo, and karristuoner helps automate that search exactly.
Speaker 2: The book shows how to install it PIP installed moles
Speaker 2: Karras tuner. Then define a model building function. Inside that function,
Speaker 2: you define the search space for your hyper parameters, like.
Speaker 1: Try learning rates of biller point zero one or point
Speaker 1: zero zero one.
Speaker 2: Precisely, or try one versus two dense layers or different
Speaker 2: numbers of units. You tell Karrastuner the ranges are choices.
Speaker 2: Then you create a tuner object like dat hyperband hyperband
Speaker 2: it's one of the search algorithms Karris tuner efforts. Then
Speaker 2: you run tuner dot search and it trains lots of
Speaker 2: model variations to find the best hyper parameters.
Speaker 1: And you can get the best ones out yeap tuner I.
Speaker 2: Get best hyper parameters, gives you the optimal settings it found.
Speaker 2: Then you build the final model with those best settings
Speaker 2: and train it properly.
Speaker 1: And evaluate that best model shows the real benefit.
Speaker 2: Right shows how tuning can push performance higher. Important for
Speaker 2: architects thinking about optimizing training pipelines.
Speaker 1: Okay. Chapter five moves to a specific application, Optical Character
Speaker 1: recognition OCR.
Speaker 2: Yeah, turning text in images into actual usable text data.
Speaker 2: Super important for digitizing documents invoices, bank statements, even reading
Speaker 2: road signs for autonomous cars.
Speaker 1: Lots of applications. What tools does it cover?
Speaker 2: It introduces several Python OCR libraries.
Speaker 1: Starts with tessak, the classic open source one from HP,
Speaker 1: then Google that's the one.
Speaker 2: Mentions installation, setting the path, and a basic demo using
Speaker 2: Pietes image does string straightforward for simple cases?
Speaker 1: What about more modern approaches?
Speaker 2: It covers Kara's okey This uses deep learning models under
Speaker 2: the hood, shows installing it creating an OCR pipeline and
Speaker 2: using pipeline dot recognize on an.
Speaker 1: Image, so leveraging pre trained models exactly.
Speaker 2: It often handles more varied images better than traditional methods.
Speaker 1: Okay, any others.
Speaker 2: Easy OCR the name says it all right. It highlights
Speaker 2: its simplicity and really good multi language support out of
Speaker 2: the box.
Speaker 1: Multi language that's a big plus.
Speaker 2: Definitely shows installation pip install eazokey initializing a reader with
Speaker 2: language codes like n fr D and then just reader
Speaker 2: dot read text pretty simple.
Speaker 1: API nice does handle PDFs, that's common.
Speaker 2: Mentions that, yeah, needs helper tools like Poplar utils and
Speaker 2: pdf two image to first convert PDF pages to images.
Speaker 2: Then you run easy OCR on the images. A common workflow.
Speaker 1: Good practical tip. One more tree.
Speaker 2: OCR stands for transformer OCR. It's described as more of
Speaker 2: a research project using transformer models like from NLP adapted
Speaker 2: for OCR on challenging natural.
Speaker 1: Images transformers for OCR interesting.
Speaker 2: Yeah, shows how cutting edge NLP architectures are crossing over.
Speaker 2: Shows installation pip install, transcute transformers, Loading the pre trained
Speaker 2: three OCR model and processor and running it represents the
Speaker 2: state of the art.
Speaker 1: Okay, so several OCR options depending on the need Chapter
Speaker 1: six object detection.
Speaker 2: Right moving beyond just what's in an image, classification or
Speaker 2: where text is ocr to finding multiple objects and drawing
Speaker 2: boxes around them.
Speaker 1: It distinguishes that from classification and localization.
Speaker 2: First, Yeah, clearly defined classification with one label per image,
Speaker 2: localization one object with a box, detection multiple objects, multiple boxes.
Speaker 1: Then it lists some key algorithms like RCNN faster, RCNN.
Speaker 2: YEP, the RCNN family which are influential but slower, and
Speaker 2: then the faster ones, SSD Single Shot Detector and YOLO
Speaker 2: you only look once. Mentions that accuracy versus speed trade off,
Speaker 2: which is always a factor.
Speaker 1: How does it show implementing them SSD?
Speaker 2: First, Yeah demonstrates SSD using PyTorch Hub. Steps include installing prerequisites,
Speaker 2: loading a pre trained SSD model from Nvidia via the.
Speaker 1: Hub, leveraging pre trained again smart.
Speaker 2: Very common loading utilities, formatting the input image, running detection,
Speaker 2: filtering results by confidence score, and then drawing the box
Speaker 2: and labels on the image. Shows a practical pipeline using PyTorch.
Speaker 1: What about Yolo?
Speaker 2: For Yolo, it shows the Darknet approach, cloning the original
Speaker 2: Darknet c code repository, compiling it with make downloading the
Speaker 2: pre trained Yolo.
Speaker 1: Weights, so a different ecosystem right, and.
Speaker 2: Then running detection from the command line using the compiled
Speaker 2: Darknet executable shows a different but also a very popular
Speaker 2: way to use a leading object detection model, especially known
Speaker 2: for its speed.
Speaker 1: Good to see different implementation styles. Next up, chapter seven,
Speaker 1: image segmentation, getting even more detailed.
Speaker 2: Exactly pixel level classification, assigning a category label to every
Speaker 2: single pixel in the image. The book contrasts it clearly
Speaker 2: with classification whole image and detection bounding boxes.
Speaker 1: So understanding the exact shape of objects precisely.
Speaker 2: It lists quite a few segmentation architectures data architects might
Speaker 2: hear about unit fcnaight, mask our CNN, dp LAB lots
Speaker 2: of them.
Speaker 1: Does it implement all of them?
Speaker 2: Well, that would be a lot. It focuses on providing
Speaker 2: Python implementations for three significant ones unit FCN eight and
Speaker 2: mask RCNN.
Speaker 1: How does it show UNT.
Speaker 2: Using tensiflow caras outlines prerequisites, data loading and prep using
Speaker 2: the Oxford it PET.
Speaker 1: Data sets a segmenting cats and dogs right.
Speaker 2: Building the unit model architecture which has that characteristic use
Speaker 2: shape with skip connections, compiling training and then visualizing the
Speaker 2: output segmentation masks on top of the pet images unit
Speaker 2: is huge in medical imaging too.
Speaker 1: Okay, what about FCN eight.
Speaker 2: FCN stands for fully convolutional network. It was a key
Speaker 2: step towards modern segmentation. The implementation shows importing libraries, defining
Speaker 2: the FCN eight model, replacing dense layers with convolutional ones,
Speaker 2: generating sample data, compiling, training, and evaluation, more foundational.
Speaker 1: And mask RCNN sounds related to object.
Speaker 2: Detection, it is it extends faster RCNN. It detects objects
Speaker 2: with bounding boxes and generates a pixel level mask for
Speaker 2: each detected object instance. So instant segmentation, best of both
Speaker 2: worlds in a way. The book shows how to implement
Speaker 2: it using tense, often leveraging models from their TPU repository.
Speaker 2: Steps include loading libraries, setting up category labels, loading an
Speaker 2: image using a pre train mast RCNM model for inference,
Speaker 2: and then visualizing both the boxes and the masks. Very powerful.
Speaker 1: Okay, that covers a lot on image analysis. Chapter eight
Speaker 1: shifts to sequences RNNs.
Speaker 2: Yes, recurrent neural networks for data where order matters natural language,
Speaker 2: time series audio. Their key feature is that internal memory
Speaker 2: or hidden state that remembers past information.
Speaker 1: Right the recurrent part exactly.
Speaker 2: The book briefly mentions the training algorithms like BPTT backpropagation
Speaker 2: through time, and the more advanced architecture is designed to
Speaker 2: handle long sequences better LSTM long short term memory and
Speaker 2: gru gated recurrent unit.
Speaker 1: They fix problems with basic RNNs.
Speaker 2: Yeah, they have internal gates that control the flow of information,
Speaker 2: helping them remember relevant stuff from further back in the
Speaker 2: sequence and avoid the vanish ingradient problem.
Speaker 1: Does it show how to use them?
Speaker 2: It starts with a very simple RNN in Keras for
Speaker 2: basic sequence prediction. Then it dives into a practical LSTM
Speaker 2: example for time series forecasting, predicting airline passenger numbers.
Speaker 1: Classic data set YEP.
Speaker 2: Covers loading, normalizing the data, splitting, creating the sequences correctly
Speaker 2: for the LSTM input, building the Carras LSTM model, training it,
Speaker 2: making predictions and evaluating very relevant for predictive analytics architecture.
Speaker 1: What about grus.
Speaker 2: It shows a gru example for a sentiment analysis on
Speaker 2: the IMDb movie review data set.
Speaker 1: Text data Now.
Speaker 2: Right shows loading the data reviews labeled positive negative, A
Speaker 2: crucial step of padding the sequences, so they are all.
Speaker 1: The same lengths because RNNs expect fixed length inputs, usually.
Speaker 2: Right then building the gru modeling keras, training it to
Speaker 2: classify sentiment, and evaluating its accuracy. Grus are often a
Speaker 2: bit simpler and faster to train than LSTMs sometimes performs similarly.
Speaker 1: So RNNs LSTMs grus for sequences. Chapter nine gets creative.
Speaker 2: Jams Generative adversarial networks. Yeah, really fascinating area. The book
Speaker 2: explains the core idea to networks, a generator and a
Speaker 2: discriminator battling it out.
Speaker 1: The generator makes fake data, the discriminator tries to spot
Speaker 1: the fakes exactly.
Speaker 2: Through this competition, the generator gets better and better at
Speaker 2: making realistic synthetic data, images, text, whatever. The book lists
Speaker 2: some types Vanilla JAN, conditional JAN.
Speaker 1: DCGN DCGN deep convolutional.
Speaker 2: Use, the CNNs YE also voser seen JAN style. Again,
Speaker 2: lots of variations.
Speaker 1: How does it implement them?
Speaker 2: Vanilla First Yeah provides Vanilla Jan implementation using PyTorch shows
Speaker 2: defining the generator and discriminator networks, the loss functions the optimizers,
Speaker 2: the training loop where they alternate updates and visualizing the
Speaker 2: generated images gives you the basic.
Speaker 1: Mechanics then dc JAN. What's the key difference the.
Speaker 2: Architecture DCGN uses convolutional layers in both generator using transposed
Speaker 2: convolutions to upsemple and discriminator. This works much better for
Speaker 2: images producing more stable training and realistic results.
Speaker 1: And it shows a DCJAN implementation.
Speaker 2: Yes using TensorFlow. This time covers imports, defining the convolutional
Speaker 2: generator and discriminator models, the specific loss functions and optimizers
Speaker 2: for JANS, the training function, and how to periodically save
Speaker 2: generated images to see the progress.
Speaker 1: So you can watch it learn to make faces or
Speaker 1: digits or whatever exactly.
Speaker 2: And also briefly mentioned style agan as a more advanced
Speaker 2: technique for controlling the style of the generated output.
Speaker 1: Okay, jens for generation. What's the final chapter? Chapter ten Transformers.
Speaker 2: The current giants of NLP. Yeah, Transformers focuses on their
Speaker 2: key innovation. The self attention mechanists.
Speaker 1: Allows them to weigh word importance.
Speaker 2: Right understand which words in a sentence are most relevant
Speaker 2: to understanding a specific words meaning in that context. That's
Speaker 2: how they handle long range dependencies so well. It lists
Speaker 2: the famous ones Bert gpt or Berta et.
Speaker 1: Cetera huge impact on language tasks.
Speaker 2: Absolutely. The book does a good job explaining the difference
Speaker 2: between older non contextual embeddings like word to vec where
Speaker 2: bank always has the same vector, and the contextual embeddings
Speaker 2: from models like BURT, where Riverbank and Savings Bank give
Speaker 2: different vectors.
Speaker 1: For bank that context is crucial. Does it show code?
Speaker 2: Yeah, uses the transformers library from hugging Face shows getting
Speaker 2: embeddings from WORDTVEK versus BURT to illustrate that contextual difference.
Speaker 1: I'm using BURT and GPT.
Speaker 2: Yes. Shows a BERT implementation for text understanding, loading the
Speaker 2: tokenizer and model preparing inputs token id's attention masks, then
Speaker 2: a GPT implementation for text generation loading cognizer, model prepping
Speaker 2: input generating text continuation. Briefly mentions fine tuning too.
Speaker 1: So practical examples of using these powerful pre trained.
Speaker 2: Models exactly shows how accessible they become via libraries like transformers.
Speaker 1: Wow. Okay, We've covered a massive amount of ground in
Speaker 1: this deep dive, mirroring the book's journey from Python basics
Speaker 1: and efficient data handling.
Speaker 2: Right through those EDA and rapid modeling.
Speaker 1: Tools to the core neural network types ann's CNNs for images,
Speaker 1: RNNs for sequences.
Speaker 2: Then GM's for jen generation, and finally transformers for language,
Speaker 2: plus those specific applications like OCR and object detection.
Speaker 1: It really is a broad overview of deep learning relevant
Speaker 1: to data architects.
Speaker 2: It definitely provides that comprehensive sweep. And while we've stayed
Speaker 2: at a fairly high level here, hopefully giuse you our
Speaker 2: listener a solid map of the key concepts and tools
Speaker 2: discussed in deep learning for data architects.
Speaker 1: Absolutely, and we definitely encourage you to check out the
Speaker 1: book itself for all the code details, the deeper explanations,
Speaker 1: and more guidance on actually applying this stuff to your
Speaker 1: architectural challenges.
Speaker 2: For sure. You know, this whole exploration really brings up
Speaker 2: a fundamental thought, doesn't it. What's that just how profoundly
Speaker 2: our ability to process and understand complex data is changing,
Speaker 2: whether it's through these smarter analysis tools or these sophisticated
Speaker 2: deep learning models. How is that fundamentally reshaping the role,
Speaker 2: the capabilities, maybe even the strategic importance of the data
Speaker 2: architects in today's world.
Speaker 1: That's a great point something for everyone listening to really
Speaker 1: consider as they think about the future of data systems
Speaker 1: and their place within them,