Speaker 1: All right, everyone, welcome back to the deep Dive. Today,
Speaker 1: we're tackling a really big topic, one that shapes so
Speaker 1: much of our modern world, neural networks and deep learning.
Speaker 1: Our mission, as always is to cut through the complexity,
Speaker 1: pull out the most important bits for you, and give
Speaker 1: you that shortcut to being truly well informed. We're diving
Speaker 1: into this fantastic textbook by Charusi Aggriwall Neural Networks and
Speaker 1: Deep Learning. Seriously, this thing is packed. It covers everything
Speaker 1: from the foundational ideas all the way to super cutting
Speaker 1: edge applications, and it even touches on some architectures that
Speaker 1: have been sort of well forgotten. So our goal is
Speaker 1: to make sense of this intricate world, give you a
Speaker 1: clear picture of what these powerful technologies are and maybe
Speaker 1: more importantly, why they matter so much.
Speaker 2: Yeah, and what's really fascinating, I think, is just tracing
Speaker 2: how this field has evolved over time. We're going to
Speaker 2: explore not just what these networks are, but how they
Speaker 2: actually learn, why they work the way they do, which
Speaker 2: can be pretty powerful, and of course the incredible ways
Speaker 2: they're being used today. I mean everything from self driving
Speaker 2: cars to generating creative art. It's kind of amazing. We
Speaker 2: really want you to walk away with those aha moments,
Speaker 2: you know, yeah, feeling like you've really unlocked some profound insights.
Speaker 1: Okay, so let's unpack this right from the start. When
Speaker 1: we hear neural networks, the first thing that pops into
Speaker 1: mind is, well, the human brain. But how close is
Speaker 1: that comparison? Really?
Speaker 2: That's a great question. The book points out that, yeah,
Speaker 2: there was definitely initial biological inspiration. You know, things like
Speaker 2: convolutional neural networks or CNNs were partly inspired by Huble
Speaker 2: and Bisles work on how the cats visual cortex processes images.
Speaker 2: But this is important. The book also mentions that comparison
Speaker 2: is often criticized. It's seen as maybe a poor caricature,
Speaker 2: a really simplified version of the actual brain.
Speaker 1: Okay, so a loose inspiration, not a direct copy.
Speaker 2: Exactly, though neuroscience principles has certainly been useful along the way.
Speaker 1: But here's something I found really interesting in the book.
Speaker 1: At their core, these networks aren't some completely alien tech.
Speaker 1: They're actually built from like basic computational units inspired by
Speaker 1: algorithms we already knew from traditional machine learning, things like
Speaker 1: least squares regression or logistic regression.
Speaker 2: Right.
Speaker 1: The power comes from how they combine tons of these
Speaker 1: simple units. Right.
Speaker 2: Precisely, they learn how to connect these basic building blocks
Speaker 2: in really intricate ways, all working together to minimize the
Speaker 2: prediction error. It's kind of like building something really complex
Speaker 2: and amazing, like a cathedral, but using very simple, powerful bricks.
Speaker 1: Okay, so what does that basic brick look like? Then?
Speaker 2: Good question. We can start with the simplest one, really,
Speaker 2: the perceptron. Imagine it is a tiny decision maker. It
Speaker 2: takes in different features pieces of information. Each feature gets
Speaker 2: multiplied by a weight, which basically says how important it is.
Speaker 2: Then it sums all those weighted features up, and finally
Speaker 2: that sum goes through something called an activation function. Think
Speaker 2: of it like a gait, which produces the final output.
Speaker 2: Often say a class label like cat or dog, and.
Speaker 1: I read about a biased neuron. What's that?
Speaker 2: Ah? Right, that's sort of a neat trick. It's like
Speaker 2: adding a constant offset to the sum before it hits
Speaker 2: the activation function. You can do that by having a
Speaker 2: special input that always has a value of one with
Speaker 2: its own weight. It just gives the model a bit
Speaker 2: more flexibility.
Speaker 1: Gotcha, And these activation functions. You said they're like gates,
Speaker 1: Why are they so important?
Speaker 2: They're absolutely crucial. They introduce nonlinearity. Without them, stacking layers
Speaker 2: wouldn't actually add much power. It would still just be
Speaker 2: a linear model. Early on, people used a simple sign function,
Speaker 2: just outputting plus one or angs one. Basically yes, no,
Speaker 2: but that's hard to train mathematically because it's not smooth,
Speaker 2: not differentiable. Okay, so functions like sigmoid and tan became popular.
Speaker 2: Sigmoid squishes the output between zero and one, which are
Speaker 2: great for probabilities. Tan is similar as shaped, but squishes
Speaker 2: between negative one and one.
Speaker 1: But the book mentioned something else has taken over now
Speaker 1: re LU.
Speaker 2: Yes, and this is a really key aha moment for
Speaker 2: understanding modern deep learning. Re LU, which stands for a
Speaker 2: rectified linear unit, sounds fancy, but it's incredibly simple. It's
Speaker 2: just max v zero, so if the input is negative,
Speaker 2: the output is zero. Otherwise the output is just the input. Yeah,
Speaker 2: deceptively simple. ReLU and variations like Hardtan have largely replaced
Speaker 2: sigmoid and soft hand. Why because they're a piece wise linear.
Speaker 2: This makes the math, specifically the gradients used in training
Speaker 2: much much easier to handle. They suffer way less from
Speaker 2: a huge problem called vanishing gradients, which we should definite
Speaker 2: talk more about. This change was fundamental in allowing us
Speaker 2: to train much much deeper networks.
Speaker 1: Okay, so we have these basic units perceptrons, with these
Speaker 1: crucial nonlinear activation functions like ReLU. How do we go
Speaker 1: from that to well deep learning?
Speaker 2: Right? Connecting it to the bigger picture. It's fascinating actually
Speaker 2: that many traditional machine learning models, the ones that people
Speaker 2: use for decades, can be seen as shallow neural networks.
Speaker 2: Think about least squares regression, logistic regression, even support VIC
Speaker 2: machines as v You could represent all of them as
Speaker 2: simple neural architectures, maybe just one or two layers deep.
Speaker 1: Really like SVMs too.
Speaker 2: Yeah, The main difference is often just boil down to
Speaker 2: the specific loss function they're trying to minimize, and maybe
Speaker 2: the activation function in the output layer. For example, logistic
Speaker 2: regression for binary classification uses that sigmoid function we mentioned
Speaker 2: to output or probability. Its loss function comes from maximizing
Speaker 2: the likelihood of the data. The book also contrasts the
Speaker 2: original perceptron learning rule, which would be a bit unstable,
Speaker 2: with the Hinge loss used by SVMs, which provides better stability.
Speaker 2: It shows this kind of shared ancestry.
Speaker 1: Okay, so those are the shallow ones. But the real magic,
Speaker 1: the deep and deep learning that comes from adding more
Speaker 1: layers right stacking them up.
Speaker 2: Exactly, That's where the power really scales up. Multi layer
Speaker 2: neural networks introduce what we call hidden layers. These are
Speaker 2: layers of computation sandwich between the input and the final output.
Speaker 2: You don't directly see their results, hence hidden. Typically, information
Speaker 2: flows forward through these layers, one feeding into the next.
Speaker 2: We call these feed forward networks.
Speaker 1: And what happens inside those hidden layers.
Speaker 2: This is where the concept of hierarchical feature engineering comes in.
Speaker 2: It's a really powerful idea. Imagine you feed an image
Speaker 2: into the network. The first hidden layer might learn to
Speaker 2: detect very simple, primitive characteristics, things like horizontal lines, vertical lines,
Speaker 2: maybe simple curves or edges.
Speaker 1: Okay, basic stuff, right.
Speaker 2: Then the next hidden layer takes those simple features as
Speaker 2: its input and learns to combine them into slightly more
Speaker 2: complex shapes or patterns, maybe corners, circles, simple.
Speaker 1: Textures, ah building blocks.
Speaker 2: Exactly, And as you go deeper, subsequent layers combine those
Speaker 2: features into even more complex semantically significant characteristics. So maybe
Speaker 2: a later layer recognizes combinations that look like an eye
Speaker 2: or a wheel, or in the book's example, hexagons or honeycombs.
Speaker 2: By the time the information reached the final layers, it's
Speaker 2: represented in a way that makes classification much easier. The
Speaker 2: network has learned to see the important patterns.
Speaker 1: That makes a lot of sense. It's like learning progressively
Speaker 1: more abstract concepts.
Speaker 2: Precisely, and a key advantage here is flexibility. You can
Speaker 2: adjust the model's complexity, its learning capacity by just adding
Speaker 2: or removing neurons or entire layers, depending on how much
Speaker 2: data you have or the convocational resources available.
Speaker 1: That brings up another point from the book, the AI winters.
Speaker 1: Why did it take so long for neural networks to
Speaker 1: really take off if the ideas were around earlier.
Speaker 2: Yeah, that's another aha moment. The core concepts from many
Speaker 2: of these networks existed decades ago, but they were held back.
Speaker 2: The book really emphasizes that the crucial factors were the
Speaker 2: massive increase in data availability, the big data and the
Speaker 2: parallel explosion and computational power, especially.
Speaker 1: With GPUs GPUs the graphics cards.
Speaker 2: Exactly they happen to be incredibly good at the kind
Speaker 2: of parallel matrix multiplications that neural networks rely on. So
Speaker 2: it was really after maybe twenty ten twenty eleven when
Speaker 2: we finally had enough data and enough computing power that
Speaker 2: these deeper, more complex models could finally be trained effectively
Speaker 2: and show what they were capable of. The resources caught
Speaker 2: up with the ideas.
Speaker 1: Right, Okay, so training these deep networks sounds like a beast.
Speaker 1: How does that learning part actually happen? You mentioned gradients before.
Speaker 2: Yeah. The core algorithm, the engine driving the learning, is
Speaker 2: called back propagation. It's essentially a clever way to figure
Speaker 2: out how much each connection, each weight in the network
Speaker 2: contributed to the overall error on a given training example.
Speaker 2: Works in two phases. First, there's a forward pass. You
Speaker 2: feed the input data through the network layer by layer
Speaker 2: until you get an output. Then you compare that output
Speaker 2: to the correct answer and calculate the error or loss.
Speaker 1: Okay, see how wrong.
Speaker 2: It was exactly. Then comes the backward pass. Using calculus,
Speaker 2: specifically the chain rule, back propagation calculates the gradient of
Speaker 2: the loss with respect to each weight. It figures out
Speaker 2: how changing each weight would affect the error. This gradient
Speaker 2: information is then propagated backward through the network layer by layer.
Speaker 2: It's like an assigning blame or credit for the error
Speaker 2: back to the connections that caused.
Speaker 1: It, and then you use that information to adjust the
Speaker 1: weights precisely.
Speaker 2: The most common method is to cast a gradient descent
Speaker 2: or ASGD. Instead of calculating the error over the entire
Speaker 2: massive data sent which would be incredibly slow. SGD takes
Speaker 2: a single training example or maybe a small batch of them,
Speaker 2: calculates the gradients and makes a small adjustment to the
Speaker 2: weights in the direction that reduces the error. Then it
Speaker 2: moves to the next example or batch. It's stochastic because
Speaker 2: each update is based on just a small sample, making
Speaker 2: it a bit noisy, but much much faster overall.
Speaker 1: Okay, that makes sense, but you mentioned a problem earlier,
Speaker 1: something about gradients, ugh, vanishing and exploding gradients. That sounds bad.
Speaker 1: What's going on there right?
Speaker 2: This is a huge challenge, especially when you start building
Speaker 2: really deep networks. It's a stability issue. Remember how backpropagation
Speaker 2: uses the chain rule that involves multiplying many small numbers
Speaker 2: together as you go backward through the layer. If those
Speaker 2: numbers related to the derivatives of the accivation functions are
Speaker 2: consistently less than one, their product can become incredibly tiny,
Speaker 2: almost zero by the time it reaches the early layers.
Speaker 2: That's the vanishing gradient problem. The signal just fades.
Speaker 1: Away, so the early layers stop learning effectively.
Speaker 2: Yes, they don't get useful information about how to adjust
Speaker 2: their weights. Conversely, if those numbers are consistently greater than one,
Speaker 2: their product can blow up, becoming astronomically large. That's the
Speaker 2: exploding gradient problem. The updates become huge and unstable and
Speaker 2: the network diverges.
Speaker 1: Yikes. Okay, so how do we fix that? How do
Speaker 1: we train these deep things reliably?
Speaker 2: Well? Thankfully, researchers have developed a whole toolkit of techniques
Speaker 2: to combat these issues and also to prevent another big
Speaker 2: problem overfitting.
Speaker 1: Overfitting that's when the model just memorizes the training data right,
Speaker 1: but doesn't work well on new stuff.
Speaker 2: Exactly, it fails to generalize. So first we have regularization techniques.
Speaker 2: Think of these as ways to impose discipline on the
Speaker 2: network during training. Weight decay using L one or L
Speaker 2: two penalties is common. It adds a cost to having
Speaker 2: large weights, encouraging the network to find simpler solutions that
Speaker 2: are less likely to overfit. Another simple but effective one
Speaker 2: is early stopping. You monitor the network's performance on a
Speaker 2: separate data set, a validation set that it doesn't train on.
Speaker 2: When the error on that validation set starts to increase,
Speaker 2: even if the training error is still decreasing, you just
Speaker 2: stop training. The model is starting to overfit.
Speaker 1: Makes sense, stop before it gets worse.
Speaker 2: Right. Then there are techniques aimed more directly at the
Speaker 2: learning dynamics. Dropout is a really clever one. During training,
Speaker 2: for each input or a mini batch, you randomly drop
Speaker 2: out temporarily said to zero a certain percentage of the neurons,
Speaker 2: and the hidden layers.
Speaker 1: Just switch them off randomly.
Speaker 2: Yep. This forces other neurons to learn more robust features
Speaker 2: because they can't rely too much on any single other
Speaker 2: neuron always being there. It's like training a team where
Speaker 2: players might randomly be unavailable. Everyone has to be more versatile.
Speaker 2: It acts like training many different smaller networks simultaneously. And
Speaker 2: batch normalization is another life saver, especially for very deep networks.
Speaker 2: It normalizes the activations within each mini batch during training,
Speaker 2: basically rescaling them to have a consistent mean in variance.
Speaker 1: Like tuning the signal kind of yeah.
Speaker 2: It helps keep the signals flowing through the network in
Speaker 2: a healthy range, preventing them from becoming too large or
Speaker 2: too small, which he'll stabilize training and allows for faster learning.
Speaker 1: Okay, wow, that's a lot of tricks. Anything else?
Speaker 2: Oh? Yeah, we also have adaptive learning rate methods. Instead
Speaker 2: of using one fixed learning rate for the entire network,
Speaker 2: algorithms like ATTIGRAD, RMS, PROP and the very popular ATOM
Speaker 2: dynamically adjust the learning rate for each parameter individually. They
Speaker 2: can speed up learning for slow parameters and slow it
Speaker 2: down for fast ones, helping convergence. Weight initialization is also
Speaker 2: surprisingly important. If you start all weights at zero, all
Speaker 2: neurons in a layer will learn the exact same thing,
Speaker 2: So you need randomized initialization like xavier or Gloro initialization
Speaker 2: to break that symmetry and get things going. And finally,
Speaker 2: especially for things like images, data augmentation is huge. You
Speaker 2: create more training data by applying random transformations to your
Speaker 2: existing data, rotating images, shifting them, changing brightness, stuff like that.
Speaker 2: It makes the model more robust variations.
Speaker 1: That's quite a toolbox. So putting it all together, what
Speaker 1: does deploying these models actually involve in practice.
Speaker 2: Well, it means a lot of careful hyper parameter tuning,
Speaker 2: finding the right learning rate, the right amount of regularization,
Speaker 2: the best network architecture. That often involves experimenting and using
Speaker 2: those validation sets to see what works best. The book
Speaker 2: mentions that for the huge data sets we have today,
Speaker 2: people might use splits like ninety eight percent for training,
Speaker 2: one percent for validation, and one percent for final testing,
Speaker 2: which is different from older rules of thumb for smaller
Speaker 2: data sets.
Speaker 1: And you mentioned GPUs earlier.
Speaker 2: Absolutely critical training these models involves tons and tons of
Speaker 2: matrix multiplications. GPUs are designed for parallel processing and have
Speaker 2: high memory ban with making them orders of magnitude faster
Speaker 2: than traditional CPUs For this kind of work, training deep
Speaker 2: models without GPUs would be practically impossible or at least incredibly.
Speaker 1: Slow, And sometimes you need more than one.
Speaker 2: GPU for really big models or data sets. Yes, you
Speaker 2: might use data parallelism where you split the data across
Speaker 2: multiple GPUs, each training a copy of the model, or
Speaker 2: even model parallelism where different parts of the neural network
Speaker 2: itself are spread across different GPUs because the whole model
Speaker 2: is too big to fit.
Speaker 1: On one Okay, that gives a much clearer picture of
Speaker 1: the training process and challenges. So we've got the basics,
Speaker 1: the depth the training. Now let's dive into some specific
Speaker 1: types of networks. The book talks about architectures designed for
Speaker 1: different kinds of data.
Speaker 2: Right exactly. Neural networks are incredibly versatile, partly because we
Speaker 2: could design specialized architectures. Let's start with probably the most
Speaker 2: famous one for images, convolutional neural networks or CNNs, right.
Speaker 1: The ones inspired by the visual cortex.
Speaker 2: Loosely, yes, the key idea in CNN this is how
Speaker 2: they process spatial data like images. They typically work with
Speaker 2: layers that have three dimensions height, width, and depth. Depth
Speaker 2: here refers to the number of channels like red, green,
Speaker 2: blue in the input or different feature maps in the
Speaker 2: hidden layers. The core operation is the convolution. You have
Speaker 2: these small filters. You can think of them as pattern detectors.
Speaker 2: Maybe one looks for vertical edges, another for horizontal edges,
Speaker 2: another for specific texture. These filters slide across the input
Speaker 2: image or the feature map from the previous layer and
Speaker 2: compute activations. Where the filter finds its specific pattern, it
Speaker 2: produces a strong activation in the output feature map.
Speaker 1: So each filter creates its own map, highlighting where it
Speaker 1: found its.
Speaker 2: Pattern precisely, and a key aspect is parameter sharing. The
Speaker 2: same filter is used across the entire image, which makes
Speaker 2: CNNs efficient and helps them recognize patterns regardless of where
Speaker 2: they appear. These convolutional layers are usually paired with ray
Speaker 2: lu activations and then often followed by pooling layers. Max
Speaker 2: pooling is common. It downsamples the feature map, making it
Speaker 2: smaller by taking the maximum value in small regions. This
Speaker 2: helps reduce computation and makes the learned features more rowe
Speaker 2: busts to small shifts or distortions.
Speaker 1: And these are the networks behind image.
Speaker 2: Recognition, absolutely, image classification, object detection. CNNs have driven huge
Speaker 2: breakthroughs there. The book mentioned some landmark architectures that came
Speaker 2: out of research and competitions. There was alex net, which
Speaker 2: really kicked off the deep learning revolution in images around
Speaker 2: twenty twelve. Then zf net improved on it. Google net
Speaker 2: introduced these clever inception modules that process features at different
Speaker 2: scale simultaneously and reduce the number of parameters, and ResNet
Speaker 2: or residual networks introduced skip connections. Skip connection Yeah they
Speaker 2: allowed the gradient information to flow more easily through very
Speaker 2: deep networks by creating shortcuts, essentially letting the signal bypass
Speaker 2: some layers. This allowed researchers to train networks with hundreds,
Speaker 2: even over one thousand layers.
Speaker 1: Wow. Okay, so CNN's are four images. What about data
Speaker 1: that comes in sequences like text or speech where the
Speaker 1: order is critical.
Speaker 2: That's the domain of recurrent neural networks or RNNs. Their
Speaker 2: defining feature is a kind of memory. They process sequences
Speaker 2: step by step, and at each step the output depends
Speaker 2: not only on the current input, but also on a
Speaker 2: hidden state that summarizes information from previous.
Speaker 1: Steps, so they remember what came before.
Speaker 2: In a sense. Yes, you can visualize an RNN as
Speaker 2: having a loop. The hidden state from one time step
Speaker 2: feeds back into the network at the next time step.
Speaker 2: And useful way to think about it, especially for training,
Speaker 2: is to unfurl or unroll this loop over time. It
Speaker 2: looks like a very deep feed forward network, but with
Speaker 2: a crucial difference. The same set of weights is used
Speaker 2: at every single comm step. This weight sharing is key
Speaker 2: for learning patterns that apply across the sequence.
Speaker 1: What's a typical use case.
Speaker 2: Language modeling is a classic one predicting the next word
Speaker 2: in a sentence. The book mentions a cool example by
Speaker 2: Andre's Karpathy, who trained an RNN character by character on
Speaker 2: Shakespeare's plays. After just a few training it areas, it
Speaker 2: produced complete gibberish, but after many more iterations it started
Speaker 2: generating text that looked syntactically like Shakespeare, correctly spelled words, punctuation,
Speaker 2: line breaks. Even though the meeting was nonsensical, it showed
Speaker 2: the RNN was learning the structure of the language.
Speaker 1: That's pretty cool. But do RNNs have issues too, like
Speaker 1: the gradient problems?
Speaker 2: Oh? Definitely. Those vanishing and exploding gradients we talked about
Speaker 2: are a major problem for basic RNNs, especially when dealing
Speaker 2: with long sequences. Trying to propagate information over many time
Speaker 2: steps is difficult. This led to the development of more
Speaker 2: sophisticated recurrent units, most famously the long short term memory
Speaker 2: or LSTM LCM.
Speaker 1: Heard of that one, Yeah.
Speaker 2: LSTMs are a type of R and N cell designed
Speaker 2: specifically to combat the vanishing gradient problem and capture long
Speaker 2: range dependencies. They have internal mechanisms called gates, an input gate,
Speaker 2: a forget gate, and an output gate, and a separate
Speaker 2: cell state that acts like a conveytor belt for information.
Speaker 2: These gates learn to control what information is added to
Speaker 2: the cell state, what's removed, and what affects the output
Speaker 2: at each step. It allows them to maintain important information
Speaker 2: over much longer periods. More recently, things like layer normalization
Speaker 2: have also helped improve RNN.
Speaker 1: Stability, so LSTMs are better at remembering long term patterns.
Speaker 2: Much better generally speaking, and they've been crucial for many
Speaker 2: applications machine translation, often using any encoder decoder structure where
Speaker 2: one RNN reads the foot sequence and another generates the
Speaker 2: output sequence. Google Translate US. This heavily also building conversational
Speaker 2: AI systems chatbots doing things like named entity recognition and
Speaker 2: text like identifying names or locations, and even powering recommender systems.
Speaker 1: Okay, CNN's for space, RNs slstms for time or sequence.
Speaker 1: What if the goal is different, like compressing data or
Speaker 1: finding a new way to represent it.
Speaker 2: That's where auto encoders come into play. The fundamental idea
Speaker 2: is pretty elegant, and auto encoder is a neural network
Speaker 2: trained to reconstruct it its own input.
Speaker 1: Reconstruct its input. What's the point of that?
Speaker 2: Ah? The trick is in the middle. The network usually
Speaker 2: has a bottleneck layer, a hidden layer with fewer neurons
Speaker 2: than the input or output layers. To successfully reconstruct the input,
Speaker 2: the network is forced to learn a compressed representation, a
Speaker 2: sort of code. In that bottleneck layer. It has to
Speaker 2: figure out the most essential features of the data to
Speaker 2: squeeze it through the bottleneck and then reconstruct it. They're
Speaker 2: sometimes called replicator.
Speaker 1: Networks, so it's learning a compressed version like dimensionality reduction exactly.
Speaker 2: Basic auto encoders with the linear activation function essentially learn
Speaker 2: the same subspace as principal component analysis PCA, but the
Speaker 2: real power comes when you make them deep auto encoders
Speaker 2: with multiple hidden layers and nonlinear activation functions like RYLU.
Speaker 2: These can learn much more complex nonlinear transformations of the data,
Speaker 2: effectively disentangling data that might lie on a complicated manifold.
Speaker 1: Better than something like PCA.
Speaker 2: Then, for complex nonlinear structures, often yes, the booknotes that
Speaker 2: can provide better class separation than linear methods, and while
Speaker 2: something like TSN is great for visualization, auto encoders are
Speaker 2: generally better if you need to apply the learned transformation
Speaker 2: to new unseen data points.
Speaker 1: Are there different kinds of auto encoders.
Speaker 2: Yes several interesting variants. Sparse auto encoders add a penalty
Speaker 2: to encourage most hidden units to be inactive, outputting zero,
Speaker 2: leading to sparse representations. Denoising auto encoders are trained to
Speaker 2: reconstruct the original clean input from a version that has
Speaker 2: been artificially corrupted with noise. This forces them to learn
Speaker 2: robust features that aren't sensitive to noise, and variational auto
Speaker 2: encoders or vaes are a more probabilistic take. They learn
Speaker 2: a distribution in the bottleneck layer, which is really useful
Speaker 2: for generating new data samples that look similar to the
Speaker 2: training data.
Speaker 1: Before we jump to the really cutting edge stuff, the
Speaker 1: book mentions some forgotten architectures, ones that were important historically.
Speaker 2: Yeah, it's good to acknowledge the stepping stones. Radio basis
Speaker 2: function networks or RBF networks, for example, they typically have
Speaker 2: a hidden layer where neurons compute the similarity of the
Speaker 2: input to certain prototype factors. This makes them related to
Speaker 2: methods like kernel machines or CAE, nearest neighbors and restricted
Speaker 2: Boltzmann machines RBMs. These were quite important for a while,
Speaker 2: especially for pre training deep networks, before modern techniques made
Speaker 2: end to end training feasible. Ourbms are energy based models,
Speaker 2: borrowing ideas from statistical physics. They're good at learning patterns,
Speaker 2: especially in binary data, and can be stacked to form
Speaker 2: deep believe networks. They were used for tasks like collaborative
Speaker 2: filtering and initializing deeper networks. While less common as primary
Speaker 2: models now, their ideas were influential.
Speaker 1: It's fascinating how many different ways there are to structure
Speaker 1: these networks. Okay, so beyond just learning from data, networks
Speaker 1: are now doing things that seem more intelligent, like making
Speaker 1: decisions or even creating new things.
Speaker 2: Absolutely, this takes us into areas like deep reinforcement learning
Speaker 2: or deep RL. Here the learning paradigm shifts. Instead of
Speaker 2: learning from label examples supervised learning, the agent learns through
Speaker 2: reward guided trial and error, much like how humans or animals.
Speaker 1: Learn trial and error. How does that work?
Speaker 2: The agent takes actions in an environment. These actions change
Speaker 2: the state of the environment and potentially lead to rewards
Speaker 2: or penalties. The goal is to learn a policy, a
Speaker 2: strategy for choosing actions that maximizes the total cumulative reward
Speaker 2: over time. It involves balancing, exploration, trying new things to
Speaker 2: see what happens, and exploitation sticking with actions known to
Speaker 2: yield good rewards. The classic multi armed bandit problem is
Speaker 2: a simple illustration of this trade off.
Speaker 1: So it learns by doing exactly.
Speaker 2: Think of learning to play a game like Tic tac toe.
Speaker 2: The RL agent might start by making random moves. When
Speaker 2: it eventually wins, the sequence of moves leading to that
Speaker 2: win gets positively reinforced. Moves leading to losses get negatively reinforced.
Speaker 2: Over many games, it learns the value of different board
Speaker 2: positions and actions. The deep part comes from using deep
Speaker 2: neural networks to represent the policy or to estimate the
Speaker 2: value of states and actions, especially in complex environments with
Speaker 2: huge state spaces like video games or robotics.
Speaker 1: And this is what was used in AlphaGo.
Speaker 2: Yes, that's a prime example in a real aha moment.
Speaker 2: Alphag and later Alpha zero used deep RL to master
Speaker 2: Go and chess. What was remarkable wasn't just that they
Speaker 2: beat the best humans, but how they did it. They
Speaker 2: used deep networks to learn patterns and evaluate board positions
Speaker 2: from scratch, just by playing millions of games against themselves.
Speaker 2: Alpha zero discovered strategies and made moves like sacrificing material
Speaker 2: for positional advantage in chess there were novel and sometimes
Speaker 2: counterintuitive even to human grand masters. It demonstrated an ability
Speaker 2: to discover knowledge autonomously through experience, which is a hallmark
Speaker 2: of RL.
Speaker 1: That's incredible. It's not just following rules, it's finding new
Speaker 1: ones precisely.
Speaker 2: The core mechanisms often involve things like Q learning learning
Speaker 2: the expected future reward or quality of taking an action
Speaker 2: in a state, or policy gradients directly learning the policy
Speaker 2: function that map states to probabilities of actions. Besides games,
Speaker 2: dep rls being applied to robot control, like learning to
Speaker 2: walk or grasp objects, optimizing complex systems, and even potentially
Speaker 2: training conversational agents that can negotiate or complete tasks.
Speaker 1: Okay, that's learning by doing. What about generating completely new
Speaker 1: stuff like those realistic but fake images you hear about.
Speaker 2: Ah, that's the territory of generative adversarial networks or jams.
Speaker 2: This is another really clever idea. The bookies at a
Speaker 2: great analogy. It's like a gain between a counterfeitter and
Speaker 2: the police. You have two networks, the generator the counterfeitter
Speaker 2: tries to create fake data, say images of faces looks realistic.
Speaker 2: It starts by taking random noises input and transforming it
Speaker 2: the discriminator. The police is trained to distinguish between real data,
Speaker 2: actual face images from a data set, and the fake
Speaker 2: data produced by.
Speaker 1: The generator, so they're fighting each other exactly.
Speaker 2: They train in an adversarial loop. The generator gets better
Speaker 2: at fooling the discriminator, and the discriminator gets better at
Speaker 2: spotting the fakes. The process continues until ideally, the generator
Speaker 2: produces fakes that are so good the discriminator can't tell
Speaker 2: them apart from real data anymore. Its accuracy is around
Speaker 2: fifty percent. It's framed as a minimax game, reaching a
Speaker 2: kind of equilibrium.
Speaker 1: And this creates realistic images.
Speaker 2: Often stunningly realistic ones. Yeah yeah, but here's another aha moment.
Speaker 2: Conditional jams. These allow you to provide some context or
Speaker 2: condition to the generator, so instead of just generating any
Speaker 2: random phase, you could ask it to generate a face
Speaker 2: based on attributes like smiling, wearing glasses, or even generate
Speaker 2: an image based on a text description. The book gives
Speaker 2: examples like converting black and white photos to color, or
Speaker 2: creating different plausible photographs based on a simple sketch like
Speaker 2: a police sketch of a suspect.
Speaker 1: Wow.
Speaker 2: What's amazing here is the level of artistry or creativity involved.
Speaker 2: The chan isn't just reconstructing something. It's filling and missing
Speaker 2: information in a way that is plausible and esthetically coherent.
Speaker 2: It's extrapolating realistically from limited context.
Speaker 1: That's bordering on creative Okay. One last area. Models that
Speaker 1: can focus or have memory.
Speaker 2: Two important concepts there are attention mechanisms and neural turing machines.
Speaker 2: Attention is inspired by how we humans focus our cognitive resources.
Speaker 2: Instead of treating all parts of the input equally, attention
Speaker 2: mechanisms allow a model to dynamically focus on specific portions
Speaker 2: of the data that are relevant.
Speaker 1: To the task at hand, like paying attention to the
Speaker 1: important words.
Speaker 2: Exactly in machine translation. When generating a target word, the
Speaker 2: attention mechanism might focus heavily on the corresponding source words.
Speaker 2: In image captioning, as the model generates the caption word
Speaker 2: by word, the attention might shift to different regions of
Speaker 2: the image relevant to the word being generated. A dog
Speaker 2: focus on dog catches a frisbee focus on frisbee. It's
Speaker 2: made a big difference in sequence to sequence tasks and
Speaker 2: then neural Turing machines or NTMs. These are really fascinating
Speaker 2: because they try to bridge the gap between neural networks
Speaker 2: and traditional Most neural networks intertwine computation and memory. The
Speaker 2: network state is its memory, and it's often transient. NTMs
Speaker 2: introduce an external persistent memory component like the tape of
Speaker 2: a Turing machine, that the neural network controller can learn
Speaker 2: to read from.
Speaker 1: And write to, separating memory and processing.
Speaker 2: Yes, this separation potentially allows them to learn to simulate
Speaker 2: algorithms just from examples. The book mentions the possibility of
Speaker 2: an NTM learning to sort a list of numbers simply
Speaker 2: by seeing many examples of scrambled lists and their sorting
Speaker 2: versions without being explicitly programmed with a sorting algorithm. They
Speaker 2: represent a step towards models that can learn more general
Speaker 2: computational processes closer to how a programmable computer works, but
Speaker 2: learn through optimization.
Speaker 1: Wow. Okay, so stepping back from all this detail, what
Speaker 1: does it all mean? We've gone from these simple perceptron
Speaker 1: bricks all the way to systems that can play, go,
Speaker 1: generate art, maybe even learn algorithms.
Speaker 2: It's really quite a journey. It's a testament I think
Speaker 2: to the power of combining relatively simple computational ideas, scaling
Speaker 2: them up with massive data and compute, and developing clever
Speaker 2: ways to train them effectively. The adaptability is just astounding.
Speaker 2: How different architectures like C and NS, R and NS
Speaker 2: and transformers now can be tailored to unlock insights from
Speaker 2: wildly different kinds of data. It provides this high level
Speaker 2: way to build systems that learn complex patterns.
Speaker 1: This deep dive into Aggerwall's book really highlights how far
Speaker 1: the field has come and how fast it's still moving.
Speaker 1: From getting the basic training to work dealing with vanishing
Speaker 1: gradients to building these incredibly speralized and capable systems.
Speaker 2: Absolutely and considering how systems like alphagos seem to discover
Speaker 2: strategies or how gans can generate novel creative outputs. It
Speaker 2: really does raise a fascinating question, doesn't it. If these
Speaker 2: networks can find complex solutions and exhibit something akin to
Speaker 2: creativity on their own, driven by data and optimization, what
Speaker 2: new forms of intelligence or problem solving, maybe even things
Speaker 2: we haven't conceived of, might they unlock in the future.
Speaker 1: That is definitely something to mull over. What might they
Speaker 1: discover that we with our human biases, might
Speaker 2: Miss exactly I thought to keep you company until our
Speaker 2: next deep dive