Speaker 1: Welcome to the deep dive, your express lane to understanding
Speaker 1: what truly matters in today's most complex subjects. Forget digging
Speaker 1: through dense texts. We're cutting straight to the core of
Speaker 1: what you need to know today. We're diving into the
Speaker 1: fascinating world of deep learning, specifically using PyTorch. Our source
Speaker 1: material is a pretty comprehensive guide to PyTorch and our mission. Well,
Speaker 1: it's simple unpack these powerful concepts into clear, actionable insights
Speaker 1: for you.
Speaker 2: Yeah, and it's incredibly relevant right now. Deep learning isn't
Speaker 2: just theory anymore. It's actively reshaping entire industries. I think
Speaker 2: personalized medicine, autonomous cars. So understanding the fundamentals and how
Speaker 2: tools like PyTorch actually make it happen gives you a
Speaker 2: really critical perspective on the edge of AI. We're hoping
Speaker 2: to give you that foundational knowledge plus a practical feel
Speaker 2: for what's under the hood.
Speaker 1: Okay, let's unpack this. Then. When we talk about machine intelligence,
Speaker 1: you hear AI, machine learning, deep learning. They sometimes get
Speaker 1: used interchangeably. Can we clarify that relationship?
Speaker 2: For absolutely, it's helpful to think of it in layers.
Speaker 2: So artificial intelligence AI, that's the big overarching goal right,
Speaker 2: making machines intelligence grand ambition exactly now. Machine learning or
Speaker 2: mL is one major way to get there. It's where
Speaker 2: machines learn from data without being explicitly programmed for every
Speaker 2: single task.
Speaker 1: Okay, so they learn patterns right.
Speaker 2: And deep learning DL is a specific type within machine learning.
Speaker 2: It's a technique that's proven incredibly effective, especially for learning
Speaker 2: really complex patterns from unstructured data like images or sound.
Speaker 1: So AI is the goal, mL is the approach. DL
Speaker 1: is a powerful technique within that approach.
Speaker 2: You got it. And the reason DL has really taken
Speaker 2: off is its advantage in certain areas. Traditional mL often
Speaker 2: needs humans to carefully engineer features from the data first,
Speaker 2: like telling the algorithm what to look for in an image,
Speaker 2: a lot of manual work a ton. Deep learning kind
Speaker 2: of flips that the algorithm itself learns to extract the
Speaker 2: important features directly from the raw data builds up this
Speaker 2: hierarchical understanding in a non linear way.
Speaker 1: Nonlinear That's key, isn't it crucial?
Speaker 2: Because real world data isn't neat straight lines, and this
Speaker 2: ability to learn features means DL models tend to keep
Speaker 2: getting better the more data you give them traditional mL
Speaker 2: can sometimes plateau.
Speaker 1: Ah, so scale really matters for deep learning performance, like
Speaker 1: more data equals significantly better results.
Speaker 2: Generally generally, yes, significantly. Think of it like learning a language.
Speaker 2: Traditional mL might learn vocabulary lists. Deep learning with enough
Speaker 2: exposure starts to grasp the grammar and nuance the underlying structure.
Speaker 1: Okay, that makes sense. Now, if we want to build
Speaker 1: these systems, we need tools frameworks, and that brings us
Speaker 1: to PyTorch. What makes it stand out? I've heard about
Speaker 1: this defined by run.
Speaker 2: Thing, right, that's a big one. So some other frameworks
Speaker 2: use a define and run approach. You first build this
Speaker 2: entire computation graph like a blueprint, and then you run
Speaker 2: data through it. It's quite static, okay. Pritorch is defined
Speaker 2: by run. The compugation graph gets built dynamically on the
Speaker 2: fly as your Python code executes.
Speaker 1: So it's more flexible, like you can change things midstream exactly.
Speaker 2: You can use standard Python loops, conditionals, print statements, debuggers.
Speaker 2: It feels much more like regular programming. This makes it
Speaker 2: super popular for research and rapid prototyping where you're constantly experimenting.
Speaker 1: That sounds much more intuitive, especially if you're already comfortable
Speaker 1: with Python.
Speaker 2: It often is, and practically speaking, is just a Python package.
Speaker 2: You install it with pip Workonda easy setup.
Speaker 1: And GPUs graphics cards.
Speaker 2: I hear they're important, oh, absolutely crucial for serious work.
Speaker 2: Deep learning involves massive matrix multiplications, and GPUs, especially in video.
Speaker 2: Ones using CUDA, are specifically designed to crunch those numbers
Speaker 2: incredibly fast, way faster than a standard CPU.
Speaker 1: What if you don't have a powerful GPU sitting around?
Speaker 2: Cloud computing is your friend? Services like Google Cloud, Aws, Azure.
Speaker 2: They offer instances with powerful GPUs you can rep by
Speaker 2: the hour. Very accessible.
Speaker 1: Good to know. So let's get into the nitty gritty.
Speaker 1: If we're building something, what are the absolute core components?
Speaker 1: Starting with the neural network itself.
Speaker 2: Right at its heart. A neural network is an algorithm
Speaker 2: designed to learn relationships. It maps input variables like say,
Speaker 2: pixels in an image, to some target output like cat
Speaker 2: or dog.
Speaker 1: How does it learn that mapping?
Speaker 2: Let's stick a simpler example. Yeah, predicting college admission. Your
Speaker 2: inputs might be GPA gr score, university rank, okay. In
Speaker 2: the network, these inputs are connected to processing units sometimes
Speaker 2: called neurons. Each connection has a weight, basically a number
Speaker 2: indicating how important that input is for the prediction.
Speaker 1: And the network learns these weights exactly.
Speaker 2: It learns them from the data inside a neuron. There
Speaker 2: are typically two main operations. First, a dot product summing
Speaker 2: up all the inputs multiplied by their weights. It's like
Speaker 2: mixing the ingredients based on their importance.
Speaker 1: Okay, a weighted sum.
Speaker 2: Then, crucially, it applies a nonlinear transformation and activation function.
Speaker 1: Why nonlinear? Why can't it just be linear?
Speaker 2: Because if you just stack linear operations, no matter how
Speaker 2: many layers you have, the whole thing is still just
Speaker 2: one big linear transformation. It can only learn straightline relationships.
Speaker 1: Ah, and the real world is messy, not straight lines precisely.
Speaker 2: Think about recognizing a face or understanding language. Super complex
Speaker 2: nonlinear patterns. Those activation functions allow the network to learn
Speaker 2: these intricate curves and boundaries. Each layer builds on the
Speaker 2: previous one, learning more abstract features.
Speaker 1: And in PyTorch, how do you actually define these network structures?
Speaker 2: For simple feed forward networks, you can use torch dot
Speaker 2: nn sequential. It lets you just list the layers one
Speaker 2: after another. Very straightforward, okay. But for anything more complex,
Speaker 2: maybe networks with multiple inputs or outputs or custom connections.
Speaker 2: You'll typically define your own network class you inherit from
Speaker 2: torch dot nn dot module and define the layers in
Speaker 2: the init method and how data flows through them in
Speaker 2: the forward method. Gives you total.
Speaker 1: Control, right, more power, more flexibility. Now back to those
Speaker 1: non linear activation functions. You said they're crucial. What are
Speaker 1: some common ones?
Speaker 2: Yeah, there are a few main players. Historically, sigmoid was popular.
Speaker 2: It squashes any input value into a range between zero and.
Speaker 1: One, useful for probabilities, maybe like binary classification.
Speaker 2: Exactly is it a cat near one or not hear
Speaker 2: or one? The problem is when the output is very
Speaker 2: close to zero or one, the gradient the signal use
Speaker 2: for learning becomes tiny. It basically stops learning.
Speaker 1: Ah the vanish ingredient problem, leading to dead neurons.
Speaker 2: Precisely, parts of the network just stop updating. Then there's
Speaker 2: ton or hyperbolic tangent, similar to sigmoid, but it squashes
Speaker 2: values between man is one and one.
Speaker 1: Why is mata one to one better than zero to.
Speaker 2: One because its output is zero centered. This often helps
Speaker 2: the optimization process converge a bit faster and more reliably.
Speaker 2: It's generally preferred over sigmoid in many cases. Okay, what
Speaker 2: else The current crowd favorite really is real you rectified
Speaker 2: linear unit. It's super simple. If the input is negative,
Speaker 2: the output is zero. If it's positive, the output is
Speaker 2: just the input of value itself.
Speaker 1: That sounds really simple. Why is it so popular?
Speaker 2: It's computationally very cheap, much faster than sigmoid or ton,
Speaker 2: and in practice it often helps networks learn faster because
Speaker 2: it doesn't saturate for positive values, but it can still die.
Speaker 2: If a neuron consistently gets negative input, it just outputs
Speaker 2: zero and the gradient becomes.
Speaker 1: Zero, so it has its own dying real you problem.
Speaker 2: It can, yeah, which led to variations like leaky reel you.
Speaker 2: Instead of outputting zero for negative inputs, it outputs a
Speaker 2: very small positive value like point zero one times the input,
Speaker 2: just enough to keep it alive basically exactly keeps the
Speaker 2: gradient flowing prevents the neuron from completely dying off.
Speaker 1: Okay, so we have networks with layers, weights and these
Speaker 1: activation functions. How do we measure if it's actually learning
Speaker 1: the right thing? How do we quantify good or bad predictions?
Speaker 2: That's the job of the loss function, sometimes called a
Speaker 2: cost function or objective function. Its whole purpose is to
Speaker 2: take the network's predictions and compare them to the actual
Speaker 2: correct answers the targets or labels, and spit out a
Speaker 2: single number at the loss.
Speaker 1: One number representing the error yep.
Speaker 2: A high loss means the predictions are bad. A low
Speaker 2: loss means they're good. The entire goal of training is
Speaker 2: to minimize this loss value.
Speaker 1: What are some examples.
Speaker 2: Well, if you're predicting a continuous value like the price
Speaker 2: of a house or a T shirt like in the
Speaker 2: book's example, that's regression. A common loss function is mean
Speaker 2: squared error MAS. It calculates the average of the squared
Speaker 2: differences between each prediction and the actual value.
Speaker 1: Squaring makes errors positive and penalizes larger rors.
Speaker 2: More right exactly now, for classification deciding between categories like
Speaker 2: cat versus dog versus panda, you often use cross entropy loss.
Speaker 2: It measures how different the network's predicted probability distribution is
Speaker 2: from the actual distribution, which is usually one for the
Speaker 2: correct class and o for others.
Speaker 1: So if network is very confident about the wrong class,
Speaker 1: the cross enterpy loss will be high, very high.
Speaker 2: It heavily penalizes confident wrong answers pushing the network towards
Speaker 2: predicting the correct class with high probability.
Speaker 1: Okay, so the loss function tells us how bad we are.
Speaker 1: How does the network use that information to get better?
Speaker 2: That's where optimizers come in. The loss function gives us
Speaker 2: the error signal. The optimizer is the algorithm that uses
Speaker 2: that signal to update the network's weights.
Speaker 1: It adjusts the knobs basically.
Speaker 2: Precisely, it figures out how to adjust each weight to
Speaker 2: reduce the loss. The most basic one is to cast
Speaker 2: a gradient descent SGD, but there are more advanced ones
Speaker 2: like atom or arms prop that often converge faster and
Speaker 2: more reliably by adapting the learning rate for each weight.
Speaker 1: And in PyTorch, how does that training loop look? You
Speaker 1: mentioned steps like zero grad backwards step right.
Speaker 2: It's a cycle for each batch of data. One you
Speaker 2: feed the data forward through the network to get predictions.
Speaker 2: Two you calculate the loss using your chosen loss function. Three, crucially,
Speaker 2: you call optimizer dot zerograd. This clears out any old
Speaker 2: gradient calculations from the previous batch. Very important. Four you
Speaker 2: call loss dot backward. This is where PyTorch automatically calculates
Speaker 2: the gradients how much each weight contributed to the loss
Speaker 2: using backpropagation five. Finally, you call optimizer dot step. This
Speaker 2: tells the optimizer to update the weights based on the
Speaker 2: gradients it just calculated.
Speaker 1: Predect, calculate loss, clear old gradients, calculate new gradients, update weights, repeat.
Speaker 2: That's the essence of training a neural network. You do
Speaker 2: this over and over, batch after batch, e back after
Speaker 2: APOC until the loss is low and the network performs well.
Speaker 1: Okay, before we get into specific applications like vision or language,
Speaker 1: we need to talk about how PyTorch actually handles the data.
Speaker 1: You mentioned tensors earlier.
Speaker 2: Yes, tensors are the absolute fundamental data structure in pytors.
Speaker 2: You can think of them as multidimensional arrays like numb
Speaker 2: pi arrays, but with superpowers, especially acceleration on GPUs.
Speaker 1: Multidimensional what does that mean exactly?
Speaker 2: It refers to the tensor's order or number of dimensions.
Speaker 2: A single number like five is a scaler a tensor
Speaker 2: of order zero. A list of numbers like one, two,
Speaker 2: three is a vector order one. A grid of numbers
Speaker 2: like a spreadsheet table is a matrix order two, and
Speaker 2: you can keep going. An image might be ordered three
Speaker 2: height with color channels, and a batch of images would
Speaker 2: be order four batch size, height, width channels.
Speaker 1: So the order tells you how many indices you need
Speaker 1: to access a specific element.
Speaker 2: Exactly to get element twenty one twenty two from that
Speaker 2: hypothetical fourth order tensor, you'd use four indicies like my
Speaker 2: tensor one zero, one one. The number of indices always
Speaker 2: matches the tensor's order.
Speaker 1: How do you know the shape or size of a tensor?
Speaker 2: You use the dot size or dot shape attribute. It
Speaker 2: returns a topal telling you the length of each dimension.
Speaker 2: For instance, a batch of thirty two images each two
Speaker 2: hundred and twenty four by two hundred twenty four pixels
Speaker 2: with three color channels would have a shape of thirty
Speaker 2: two two twenty four two twenty four three. Ken you
Speaker 2: change the shape, yes, using methods like dot view or
Speaker 2: dot reshape. This lets you rearrange the elements into a
Speaker 2: different configuration without changing the total number of elements or
Speaker 2: the underlying data. It's really useful, for example, flattening an
Speaker 2: image before feeding it into a simple linear.
Speaker 1: Layer, and you has that handy night of one trick.
Speaker 2: Yeah, if you specified night of one for one dimension.
Speaker 2: PyTorch automatically calculates its size based on the total number
Speaker 2: of elements and the sizes of the other dimensions you provided.
Speaker 2: Super convenient, and remember dot view usually returns a new
Speaker 2: tensor sharing the same data. It doesn't modify the original
Speaker 2: in place.
Speaker 1: Typically, what about basic math adding? Multiplying?
Speaker 2: Tensors support all the standard element wise operations addition, subtraction, multiplication, division.
Speaker 2: They work just like you'd expect of the race.
Speaker 1: Any gotcha's there? The book mentions something about division.
Speaker 2: Ah, yes, data types. If you have a tensor of integers,
Speaker 2: say Torch dot tensor five three, which defaults to in
Speaker 2: ten sixty four, and you divide five to three element wise,
Speaker 2: you might get one because it performs integer division right
Speaker 2: truncates the deskmall exactly to get the floating point result
Speaker 2: like one point sixty sixty six. You need to make
Speaker 2: sure at least one of the ten tensors has a
Speaker 2: floating point D type like torch dot float three two.
Speaker 2: You can specify the D type when creating the tensor
Speaker 2: or cast it later. Always be mindful of your data
Speaker 2: types makes sense.
Speaker 1: Tensors really seem like the core way Pietorch handles all
Speaker 1: numerical data from inputs to weights to gradients.
Speaker 2: They absolutely are everything flows as tensors. Getting comfortable manipulating them, indexing, reshaping,
Speaker 2: doing operations is key to working effectively with PyTorch.
Speaker 1: All right, so we have the network building blocks, we
Speaker 1: understand loss and optimization, and we know data is handled
Speaker 1: via tensors. Let's see the stuff in action. Computer vision
Speaker 1: seems like a huge area for depth learning.
Speaker 2: Definitely one of the fields where it first made massive breakthroughs.
Speaker 2: The problem with the images, as we touched on, is
Speaker 2: that if you just flatten them into a long vector
Speaker 2: for a standard fully connected network, you lose.
Speaker 1: All the spatial information like which pixels are next to
Speaker 1: each other.
Speaker 2: Precisely, and the number of weights needed becomes astronomically large
Speaker 2: even for moderately sized images. It just doesn't scale well
Speaker 2: and doesn't leverage the inherent structure of images.
Speaker 1: So what's the deep learning solution?
Speaker 2: Convolutional neural networks or CNNs. They are designed specifically to
Speaker 2: process grid like data like images.
Speaker 1: How did they work differently?
Speaker 2: Instead of connecting every input pixel to every neuron in
Speaker 2: the first layer, CNNs use filters or kernels. These are
Speaker 2: small windows of weight say three by three or five
Speaker 2: y five that slide across the.
Speaker 1: Input image like scanning the image with a small magnifying glass.
Speaker 2: Kind of a yeah. Each filter learns to detect a
Speaker 2: specific local feature, maybe a vertical edge, a horizontal line,
Speaker 2: a certain curve, or a texture. As the filter slides
Speaker 2: across the image, it creates an activation map showing where
Speaker 2: it found that feature.
Speaker 1: And because it's sliding, it detects that feature regardless of
Speaker 1: where it is.
Speaker 2: In the image exactly. That's called translation in variants, a
Speaker 2: key property, and crucially, it preserves the spatial relationships between features.
Speaker 2: Layers deeper in the CNN then learn to combine these
Speaker 2: simpler features into more complex ones edges combined to form corners, corners,
Speaker 2: and textures combined objects like eyes or wheels.
Speaker 1: Let's look at that journey the classic MNIST data set
Speaker 1: handwritten digits. How well do CNNs do there?
Speaker 2: Even a fairly simple CNN can achieve really high accuracy
Speaker 2: on MNIST, like ninety eight percent or ninety nine percent.
Speaker 2: It's a stand in benchmark and CNN's crush it.
Speaker 1: But then you take that same CNN, maybe trained on MNIST,
Speaker 1: and try it on something harder like the Dogs Versus
Speaker 1: Cats challenge from Cagle.
Speaker 2: And suddenly it might struggle, maybe only seventy five percent accuracy.
Speaker 2: The features learned for recognizing simple digits aren't necessarily complex
Speaker 2: enough or the right kind to distinguish between detailed photos
Speaker 2: of different animal breeds. It doesn't generalize well enough.
Speaker 1: Which brings us back to an important idea you mentioned,
Speaker 1: how do we tackle these harder tasks, especially if we
Speaker 1: don't have millions of labeled dog and cat photos ourselves.
Speaker 2: Trendsfer learning This is hugely powerful in computer vision. The
Speaker 2: idea is why start learning from scratch when others have
Speaker 2: already trained massive models on enormous data.
Speaker 1: Sets, like learning to drive a motorbike after knowing how
Speaker 1: to drive a car reusing the basic road knowledge.
Speaker 2: Perfect analogy, we take a pre trade model like VGG
Speaker 2: sixteen or ResNet, which has already been trained on image neet,
Speaker 2: a data set with millions of images across one thousand categories.
Speaker 2: These models have learned incredibly rich in general visual features
Speaker 2: in their early layers edge detectors, texture detectors, basic shape detectors.
Speaker 1: So you take that pre trained network and.
Speaker 2: You typically freeze the weights of those early convolutional layers,
Speaker 2: you don't let them train anymore. You basically treat them
Speaker 2: as fixed future extractors. Then you replace the final classification
Speaker 2: layer which was trained for the original one thousand imagh
Speaker 2: net classes, with a new one suited to your task
Speaker 2: like discriminating between dogs and cats.
Speaker 1: And you only train this new final layer, or maybe
Speaker 1: the last few layers exactly.
Speaker 2: You only train the small task specific part of the
Speaker 2: network using your relatively smaller data set, like the dogs
Speaker 2: versus cats images. The bulk of the network's knowledge is transferred, and.
Speaker 1: The result on dogs versus cats it's dramatic.
Speaker 2: Instead of seventy five percent accuracy, using transfer learning with
Speaker 2: a pre trained ResNet can easily push you up to
Speaker 2: ninety eight percent or higher. Massive improvement leveraging knowledge learn
Speaker 2: from a different, larger task.
Speaker 1: That's amazing. And you can even peak inside these CNNs
Speaker 1: visualize what they're learning.
Speaker 2: Yeah, it's fascinating. You can look at the activations the
Speaker 2: output maps from different filters at different layers. Early layers,
Speaker 2: you'll see activations responding to simple things like edges and corners.
Speaker 2: Go deeper, and you see activations responding to more complex textures, patterns,
Speaker 2: or even parts of objects like eyes or snouts.
Speaker 1: It really gives you sense that the network is building up,
Speaker 1: understanding hierarchically it does.
Speaker 2: It demystifies the black box a little bit. And beyond
Speaker 2: VGG and ResNet, there are other cool architectures you mentioned
Speaker 2: ResNet solving the vanishing grading issue with skip connections.
Speaker 1: Right letting information by pass layers.
Speaker 2: Then there's inception or Google net, which cleverly uses parallel
Speaker 2: convolutional filters of different sizes one by one, three by three,
Speaker 2: five by five at the same length layer and concatenates
Speaker 2: their outputs. It captures features at multiple scale simultaneously, and
Speaker 2: it uses one by one convolution smartly for dimensionality reduction,
Speaker 2: making it efficient and dense net. Dense net took connectivity
Speaker 2: even further. Each layer receives inputs from all preceding layers
Speaker 2: and passes its own feature maps to all subsequent layers.
Speaker 2: It sounds complex, but it actually encourages feature reuse and
Speaker 2: can lead to models with fewer parameters that are very effective.
Speaker 1: So if one of these powerful models gives great results,
Speaker 1: can you do even better by combining them?
Speaker 2: Yes, that's model ensembling. You train several different high performing models,
Speaker 2: maybe a res net and inception a dense net independently
Speaker 2: on the same task. Then for a new image, you
Speaker 2: get predictions from all of them and combine those predictions,
Speaker 2: often just by averaging their output probabilities or taking a
Speaker 2: majority vote, and.
Speaker 1: That actually improves accuracy further.
Speaker 2: Often, yes, it can smooth out the errors or biases
Speaker 2: of individual models. For dogs versus cats, ensembling can nudge
Speaker 2: accuracy even higher, maybe to ninety nine point three percent more.
Speaker 2: The downside is well, you have to train and run
Speaker 2: multiple models, so it's computationally more expensive.
Speaker 1: A trade off between performance and cost. Okay, let's switch
Speaker 1: gears now, from seeing to understanding language. Natural language processing
Speaker 1: or NLP text data is different.
Speaker 2: It's sequential absolutely. The meaning often depends on the order
Speaker 2: of words, so the first step is usually tokenization, breaking
Speaker 2: the text down into smaller units.
Speaker 1: Or tokens like words or characters.
Speaker 2: Could be either. For a review like just perfect, Splitting
Speaker 2: by spaces gives you word tokens just perfect. Using Python's
Speaker 2: list function on the frame would give you character tokens
Speaker 2: j sh she d. The choice depends on the task.
Speaker 1: Okay. Once you have tokens, you need numbers right vectorization.
Speaker 2: Right, we need to represent these tokens numerically. One old
Speaker 2: method is one hot encoding, where each unique word gets
Speaker 2: a huge vector that's all zeros except for a single
Speaker 2: one at its specific index.
Speaker 1: Sounds very sparse and doesn't capture meaning, does it like
Speaker 1: king and queen would be totally unrelated vectors exactly.
Speaker 2: It's rarely used in modern deep learning for NLP. Much
Speaker 2: more powerful are word embeddings. These represent words as dense,
Speaker 2: relatively low dimensional vectors, maybe one hundred or three hundred
Speaker 2: dimensions instead of millions.
Speaker 1: And these vectors capture meaning.
Speaker 2: Yes, that's the key. They are learned in such a
Speaker 2: way that words with similar meanings end up having similar
Speaker 2: vector representations, like the vector for king might be mathematically
Speaker 2: close to the vector for queen or monarch. They capture
Speaker 2: semantic relationships.
Speaker 1: How are these embeddings learned?
Speaker 2: Often they're learned as part of training a larger model
Speaker 2: on a specific task, like sentiment analysis on the IMDb
Speaker 2: movie review data set mentioned in the book, The network
Speaker 2: learns embeddings that help it predict whether a review is
Speaker 2: positive or negative.
Speaker 1: And, like with images, can you use pre trained embeddings?
Speaker 2: Absolutely? And it's very common. Models like Glove or fast
Speaker 2: text are trained on massive text corpora like all of
Speaker 2: Wikipedia or the Web to produce general word embeddings. If
Speaker 2: you don't have much text data for your specific task,
Speaker 2: using these pre trained embeddings gives your model a huge
Speaker 2: headstart on understanding language. Torch text is a useful library here, so.
Speaker 1: We have numerical representations of words that capture meaning. How
Speaker 1: do we process sequences were order matters.
Speaker 2: The classic approaches were current neural networks RNNs. They're designed
Speaker 2: to handle sequences by processing tokens one by one while
Speaker 2: maintaining an internal hidden state. This state acts like a memory,
Speaker 2: accumulating information from previous tokens in the sequence.
Speaker 1: Sounds good, but I remember reading. They have a weakness
Speaker 1: something about long sequences.
Speaker 2: Yeah, the long term dependency problem. Standard RNNs struggle to
Speaker 2: retain information from tokens seen much earlier. In a long sequence,
Speaker 2: the signal tends to fade or get overwritten. It's hard
Speaker 2: for them to connect, say the subject at the beginning
Speaker 2: of a long paragraph to a verb much later. Also,
Speaker 2: the vanish ingredient problem can hit them hard during training.
Speaker 1: So what's the fix?
Speaker 2: Long short term memory networks LSTMs. They are a special,
Speaker 2: more complex type of RNN, specifically designed to overcome these issues.
Speaker 1: How do they beew it?
Speaker 2: LSTMs have a more sophisticated internal structure. They introduce a
Speaker 2: cell state that runs through the entire chain, acting like
Speaker 2: a conveyor belt for information, making it easier for contexts
Speaker 2: to persist over long distances. And they use gates input gates,
Speaker 2: forget gates, output.
Speaker 1: Gates dates like little controllers exactly.
Speaker 2: They are small neural networks themselves that learn to control
Speaker 2: the flow of information. The forget gate learns what old
Speaker 2: information to throw away from the cell state. The input
Speaker 2: gate learns what new information from the current token to add.
Speaker 2: The output gate learns what part of the cell state
Speaker 2: to output as the hidden state for the next step.
Speaker 2: This selective memory management lets them handle long term dependencies
Speaker 2: much better.
Speaker 1: That sounds much more capable are LSTMs the only way?
Speaker 2: Not necessarily, Sometimes one Deconvolutional networks similar to the c
Speaker 2: and ns used for images, but applied along the sequence dimension,
Speaker 2: could be very effective for text tasks too. They can
Speaker 2: capture local patterns like phrases efficiently and are often faster
Speaker 2: to train than LSTMs interesting.
Speaker 1: So what are some big applications of these sequence models
Speaker 1: in n LP.
Speaker 2: A fundamental one is language modeling. Predicting the next word
Speaker 2: in a sequence given the preceding words.
Speaker 1: Seems simple, but I guess that's the basis for a lot.
Speaker 2: Huge applications autocomplete on your phone, for instance, but also
Speaker 2: machine translation predicting the next word in the target language,
Speaker 2: image captioning, generating a textual description word by word, summarization,
Speaker 2: and even creative text generation writing stories, poems, or code.
Speaker 1: And this is where models like BURT and GPT come in.
Speaker 2: Yes, exactly. The transformer architecture, which relies on a mechanism
Speaker 2: called self attention rather than recurrence, really revolutionized language modeling
Speaker 2: around twenty seventeen. It allowed for much better parallelization and
Speaker 2: capturing long range dependencies. This paved the way for massive
Speaker 2: pre trained language models like elmo Bert and the GPT
Speaker 2: series GPT two, GPT three and beyond.
Speaker 1: These models are trained on enormous amounts of text and
Speaker 1: can then be fine tuned for various specific NLT tasks,
Speaker 1: achieving state of the art results.
Speaker 2: Precisely, they have a remarkable grasp of language. Of course,
Speaker 2: the power of models like GPT two also raised important
Speaker 2: ethical discussions about potential misuse like generating fake news.
Speaker 1: A crucial point. Okay, we've covered networks that see and
Speaker 1: networks that understand language. What about models that create things
Speaker 1: or models that learn through trial and error? Ah?
Speaker 2: Yes, this gets us into generative models and reinforcement worning
Speaker 2: really exciting areas. Let's start with auto encoders modernencoders.
Speaker 1: What's their goal?
Speaker 2: They're generally unsupervised learning algorithms. Their goal is simple, learn
Speaker 2: to reconstrict their own input. They typically have two parts.
Speaker 2: And encoder that compresses the input data into a lower
Speaker 2: dimensional representation the bottleneck or latent space, and a decoder
Speaker 2: that tries to rebuild the original input from that compressed representation.
Speaker 1: Why compress it just to rebuild it? What's the use?
Speaker 2: Several things? That compressed representation the bottleneck forces the network
Speaker 2: to learn the most salient features of the data, so
Speaker 2: they can be used for dimensionality reduction or for data
Speaker 2: to noising if you train it on noisy images, but
Speaker 2: ask it to reconstruct clean ones. And importantly, they form
Speaker 2: the basis for some generative.
Speaker 1: Models, like variational auto encoders VIAES exactly.
Speaker 2: Vies are a type of generative auto encoder. Instead of
Speaker 2: mapping an input to a single fixed point in the
Speaker 2: latent space, the encoder maps it to a probability distribution,
Speaker 2: usually a Gaussian a distribution. Why because then, to generate
Speaker 2: new data, you can just sample a point from that
Speaker 2: learned distribution in a latent space and feed it to
Speaker 2: the decoder. Since it learned to decode points from that
Speaker 2: general area into realistic outputs, it can generate novel examples
Speaker 2: that look similar to the training data but aren't exact copies.
Speaker 2: Think generating new faces that look plausible. Features like smiling
Speaker 2: might be represented probabilistically across the latent space.
Speaker 1: How do you train something that involves sampling? Isn't that tricky?
Speaker 2: He uses a clever technique called the Rape parameterization trick,
Speaker 2: which allows gradients to flow back through the sampling process,
Speaker 2: making the vae trainable with standard backpropagation.
Speaker 1: Okay, cool. What about restricted Boltzmann machines RBMs and deep
Speaker 1: belief networks bbns? They sound a bit.
Speaker 2: Older school they are, but conceptually important. An RBM is
Speaker 2: a simple two layer network, a visible layer for input
Speaker 2: and a hidden layer that learns patterns in an unsupervised way.
Speaker 2: They were often used for things like collaborative.
Speaker 1: Filtering like movie recommendations.
Speaker 2: Exactly imagine, the visible layer represents movies you've liked or disliked.
Speaker 2: The RBM learns connections to hidden units that might represent
Speaker 2: underlying factors like genres or actor preferences, even if those
Speaker 2: aren't explicitly labeled. Then it can use these learned factors
Speaker 2: to predict if you'd like other movies. A deep belief
Speaker 2: network DBN is essentially a stack of RBMs trained layer
Speaker 2: by layer in an unsupervised fashion, often followed by supervised
Speaker 2: fine tuning.
Speaker 1: Interesting now for the big one in generative models, generative
Speaker 1: adversarial networks GNS sounds like a competition.
Speaker 2: It absolutely is, yeah. Popularized by Ian Goodfellow in twenty fourteen,
Speaker 2: gns involve two neural networks locked in a contest. You
Speaker 2: have a generator network that tries to create fake data
Speaker 2: like images that looks realistic, and you have a discriminator
Speaker 2: network that tries to distinguish between real data from the
Speaker 2: training set and the fake data created by the generator.
Speaker 1: The classic counterfeiter and police analogy, right.
Speaker 2: That's the one. The generator wants to fool the discriminator,
Speaker 2: the discriminator wants to catch the fakes. They train together.
Speaker 2: As the discriminator gets better at spotting fakes, the generator
Speaker 2: has to get better at creating more convinsing fakes to
Speaker 2: fool it. This adversarial process pushes both networks to improve,
Speaker 2: often resulting in generators that can create stunningly realistic synthetic data.
Speaker 2: How does the discriminator learn It's trained like a standard classifier,
Speaker 2: on a mix of real images labeled as real and
Speaker 2: fake images from the generator labeled as fake. It's feedback
Speaker 2: how well it classify them is used to train both
Speaker 2: itself and to guide the generator on how to improve
Speaker 2: its fakes. Dcgn's deep convolutional jans or an early successful
Speaker 2: architecture for generating decent images.
Speaker 1: Incredible stuff. Okay, finally, let's talk about machines learning to act.
Speaker 1: Reinforcement learning RL right.
Speaker 2: RL is about training an agent like a robot or
Speaker 2: a game AI to make sequences of decisions in an
Speaker 2: environment like the real world or a game level to
Speaker 2: maximize some notion of cumulative reward.
Speaker 1: So it learns by doing getting feedback exactly.
Speaker 2: The agent takes an action in a certain state of
Speaker 2: the environment. The environment responds by transitioning to a new
Speaker 2: state and giving the agent a reward positive for good
Speaker 2: actions negative for bad. The agent's goal is to learn
Speaker 2: a policy, a strategy for choosing actions that maximizes the
Speaker 2: total reward it collects over time. It's a continuous loop
Speaker 2: of observe, act, get rewarded learn.
Speaker 1: Is it always learning from direct interaction?
Speaker 2: Not always. There's model based RL, where the agent tries
Speaker 2: to learn a model of how the environment works, predicting
Speaker 2: next states and rewards, and then plans using that model.
Speaker 2: More common, perhaps, is model free RL, where the agent
Speaker 2: learns directly through trial and error, figuring out which actions
Speaker 2: lead to good rewards in which states without explicitly building
Speaker 2: a model of the world.
Speaker 1: How does deep learning fit into this RL sounds like
Speaker 1: it could involve really complex states like pixels on a
Speaker 1: game screen.
Speaker 2: That's where deep q networks dqns made a huge splash,
Speaker 2: particularly with playing Atari games directly from pixels. A DQN
Speaker 2: uses a deep neural network often a CNN for visual
Speaker 2: input to approximate the Q value. The Q value QS
Speaker 2: represents the expected future reward an agent can get if
Speaker 2: it takes action A in state as and then follows
Speaker 2: the optimal policy. Thereafter, the network learns to predict these
Speaker 2: Q values for all possible actions in a given state.
Speaker 2: The best action is simply the one with the highest
Speaker 2: predicted Q value.
Speaker 1: So the deep network learns to evaluate how good each
Speaker 1: action is exactly.
Speaker 2: DQNS introduced some key innovation. One is the DQN loss function.
Speaker 2: He uses a separate target network, a slightly older copy
Speaker 2: of the main Q network to provide more stable target
Speaker 2: Q values during training, preventing oscillations.
Speaker 1: Yeah experience replay crucial.
Speaker 2: Instead of learning only from the very last action, the
Speaker 2: agent stores its experiences state, action, reward, next state tuples
Speaker 2: in a large memory buffer. During training, it samples random
Speaker 2: mini batches from this buffer. This breaks correlations and sequential experiences,
Speaker 2: improves data efficiency, and prevents the agent from getting stuck
Speaker 2: in short term loops.
Speaker 1: There was also something about double deep q learning.
Speaker 2: Yes, double DQN is a refinement that helps reduce the
Speaker 2: tendency of standard tick dqns to overestimate Q values, leading
Speaker 2: to more stable and sometimes better policies.
Speaker 1: Are dqns the only way deep learning is used NURL.
Speaker 2: No, There are other major approaches. Policy gradient methods directly
Speaker 2: learn the policy function, a network that outputs probabilities for
Speaker 2: each action given a state, trying to directly optimize the
Speaker 2: policy to macmize rewards.
Speaker 1: And actor critic methods.
Speaker 2: Actor critic methods combine ideas from both value based like
Speaker 2: DQN and policy based methods. They typically have two networks,
Speaker 2: an actor network that learns the policy decides which action
Speaker 2: to take, and a critic network that learns a value
Speaker 2: function evaluates how good the chosen action or current state is.
Speaker 2: The critic helps train the actor more efficiently. A three
Speaker 2: C asynchronous advantage. Actor critic was a very influential algorithm
Speaker 2: in this family, using multiple parallel agents to explore the
Speaker 2: environment faster.
Speaker 1: Wow. So URL powered by deep learning can learn complex
Speaker 1: strategies and complex environments. What are the real world applications
Speaker 1: beyond games.
Speaker 2: Tons, robotics, teaching robots, manipulation skills, optimizing traffic, light control,
Speaker 2: creating personalized recommendation systems that adapt over time, resource management,
Speaker 2: and even generating creative content like images or music by
Speaker 2: framing it as a sequential decision problem.
Speaker 1: What an incredible journey we've taken through deep learning with
Speaker 1: PyTorch from the core concepts AI mL DL PyTorch defined
Speaker 1: by run through the anatomy of neural networks, activations, loss optimizers,
Speaker 1: the power of tensors.
Speaker 2: Then seeing it all applied in computer vision with CNNs,
Speaker 2: the magic of transfer learning and visualizing.
Speaker 1: What models learn, and shifting to language with NLP tokenization, embeddings,
Speaker 1: the challenges of sequences handled by LSTMs, and the rise
Speaker 1: of transformers and huge pre trained models.
Speaker 2: Finally touching on generative models like vaes and jams, creating
Speaker 2: new data and rl agents learning through interaction with dqns
Speaker 2: and actor critic methods.
Speaker 1: Yeah, hopefully you listening now have a much stronger foundation
Speaker 1: or real understanding of what's powering some of the most
Speaker 1: advanced AI out there.
Speaker 2: Absolutely. Yeah, And remember this knowledge is really most valuable
Speaker 2: when you start applying it, or at least digging deeper
Speaker 2: into areas that sparked your interest. This deep dive is
Speaker 2: a starting point.
Speaker 1: So where should someone go next if they want to
Speaker 1: continue learning?
Speaker 2: Well, reading research papers is always a good way to
Speaker 2: stay on the cutting edge. Sites like papers with cood
Speaker 2: dot com link papers to code to implementations, which is
Speaker 2: super helpful urxivdashanity dot com helps filter the fire hose
Speaker 2: of new papers.
Speaker 1: On our good resources. What about specific topics.
Speaker 2: If computer vision grabbed you, maybe you look into object
Speaker 2: detection models like SSD faster RCNN or YOLO, or image
Speaker 2: segmentation with mask RCNN. If language is your thing, exploring
Speaker 2: libraries like hugging faces, transformers or open source translation projects
Speaker 2: like open and mt could be great next steps.
Speaker 1: Fantastic suggestions.
Speaker 2: The key is to keep exploring and if possible, get
Speaker 2: your hands dirty with some code.
Speaker 1: Definitely. So as we wrap up, here's something to think about.
Speaker 3: We've talked about all these powerful tools, models that see,
Speaker 3: understand language, generate content, make decisions. How deeply are these
Speaker 3: technologies already woven into our daily lives, maybe in ways
Speaker 3: we don't even notice. And looking forward, what were possibilities
Speaker 3: or perhaps what new questions does that raise for you
Speaker 3: and for society?
Speaker 1: Something to mul over. Thanks for joining us on the
Speaker 1: deep dive.