"Deep learning includes unsupervised learning, meaning an AI can analyze large amounts of data more deeply and reveal new insights for which it might not have been trained."
"Reasoning is underexplored in deep learning."
"Deep learning is creating a lot of value and will keep creating a lot of value."
"The deep learning revolution is a truly exciting enabler that we're seeing today in so many aspects of so many real-world problems."
"This is just an incredible time...we're looking at a technology, deep learning, that has made it possible for software to write software."
"Deep learning should be accessible to as many people as possible without fussing around with infrastructure."
"The biggest opportunities for deep learning... once it hit that point, it would do that in kind of just about every domain."
"Every data scientist working with deep learning needs to recognize they have an incredibly high leverage tool."
"To me, artificial intelligence is much bigger than deep learning, bigger than computing; it is our civilization's journey into understanding the human mind and creating echoes of it in the machine."
"Deep learning has enabled such tremendous applications in a variety of fields from autonomous vehicles to medicine and healthcare to advances in reinforcement learning, generative approaches, robotics, and a whole host of other applications and areas of impact."
"The possible effects of overhype of deep learning and artificial intelligence can be very dangerous."
"What happens when we look at these places where the model has insufficient or no training data and how can we as implementers and users of deep learning have a sense of when the model doesn't know?"
"Deep learning can do more than just beat us at board games; it finds applications anywhere from self-driving vehicles to fake news detection to even predicting earthquakes."
"Deep learning is a machine learning technique that learns features and tasks directly from data by running inputs through a biologically inspired neural network architecture."
"The Transformer is unquestionably the most important deep learning model invented."
"Deep Learning is revolutionizing so many fields: from robotics to medicine and everything in between."
"Deep learning goes One Step Beyond this and is a subset of machine learning which focuses explicitly on what are called neural networks."
"This entire speech and video are not real and were created using deep learning and artificial intelligence."
"We want to provide you with a solid foundation of how you can understand these algorithms under the hood but also provide you with the practical knowledge and skills to implement state-of-the-art deep learning algorithms."
"The idea of deep learning is that you don't need to manually engineer the features; instead, you learn these features just from raw data."
"Deep learning is an algorithm inspired by how the human brain works, and as a result it's an algorithm which has no theoretical limitations on what it can do."
"When Elon made a big bet on using deep learning for FSD, that's a 10x to me."
"One thing I still do when I'm trying to study something really deeply is take handwritten notes."
"The key insight of deep learning is that let's not hand-engineer these features; instead, let's learn them directly from raw data."
"Deep learning is revolutionizing so many things from robotics to medicine and everything in between."
"The ability to generate these types of dynamic moving videos from only a single image is remarkable to me and it's a testament to the true power of deep learning."
"Deep learning takes this idea even further and it's a subset of machine learning that focuses on using neural networks to automatically extract useful patterns in raw data."
"The key idea of deep learning is to learn these features directly from data in a hierarchical manner."
"Deep learning can build powerful computer vision systems capable of solving extraordinarily complex tasks that maybe just 15 years ago would have not even been possible to solve."
"It's really remarkable to see how deep learning is being applied to some of these problems focused on really doing good and just helping people."
"It did amazingly well, and it sort of kick-started this whole deep learning revolution in computer vision."
"We need to make available tools that are accessible to anyone because you shouldn't need to be an expert in order to start leveraging deep learning to solve big problems."
"Which is why studying TensorFlow is sort of almost necessary in order to make progress in deep learning, just because it can facilitate your research and your projects."
"I think there'll be a lot of focus on doing this thinking thing with deep learning with language models."
"There's always deeper and deeper they're going to, I think, you know, how are we going to focus on knowing when you're, you're kind of stagnating or to our level and you don't understand what's going on."
"Hopefully, this discussion of these approaches gives you a sense of how we can use deep learning to not only learn patterns and data but to use this information in a rich way to achieve generative modeling."
"Our brain is wired up as a deep network so it can learn, understand, solve problems that have this compositional structure."
"Learning deeper and more rapidly through distinctions."
"His victory is a profound reminder that no matter how good deep learning data-driven AI looks when it is trained on an immense amount of data, we can never be sure that systems of this sort really can extend what they know to novel circumstances."
"Do you think that deep learning folks could learn a little bit from philosophy?"
"Deep learning and machine learning are not magic."
"The craft of deep learning is all about creating a model that has a proper fit."
"It requires deep understanding and it also requires the teacher to teach you."
"Listen to more Quran... find a reciter you enjoy... pick one Surah to study deeply."
"NVIDIA's new CPU isn't targeted at x86 consumers, but it's still very interesting, especially for the deep learning space."
"Using deep learning algorithms are super effective in all kinds of areas of science not just study and astronomy pretty much everywhere."
"We thought that this will be an interesting solution to this kind of problem right in SETI."
"What is deep learning? Well, the media thinks that deep learning is this, you know, they seem to think that it's sort of like Terminator meets some sort of oppression system that's gonna take over the world."
"Education is a lot deeper than what we're taught on a regular basis."
"Understanding the 'why' makes all the difference in learning."
"An opportunity for us to go deeper and... dive deep into what we've learned and how you can apply it into your own life."
"I just wanted to move to make the biggest bet possible on deep learning."
"Segmentation is where we color in every pixel according to what it's a part of."
"This is a year for profound and deep learning."
"Deep learning could turbo charge every industry."
"Combining reinforcement learning with deep learning builds extraordinary applications."
"Backpropagation is the workhorse of deep learning."
"Reading slowly and digesting the content deeply helps get all the nutrients out of the book."
"YouTube is just surface level information; my Academy is where you learn everything from beginning to end."
"I love hyper fixating on things, falling down the rabbit hole of learning everything about a subject."
"It's about solving three or four problems but really extracting the key lessons."
"The main idea behind deep learning is that we often want to learn the way that we represent the data that we use."
"The brain's computational complexity rivals that of a whole convolutional deep neural network."
"He's one of the world's leading visionaries in deep learning and computer vision."
"Deep learning is not magic, but it's really good at finding patterns."
"Machine learning involves statistical techniques such as deep learning that are inspired by theories about how the human brain processes information."
"The efficient fitting of deep learning models is startling, and their generalization is dumbfounding."
"A priori, deep learning should not work, and yet it does."
"Deep learning models typically have many layers of neurons which allows them to learn more complex patterns than traditional machine learning models."
"Deep learning is getting very popular."
"The course goes from zero to intermediate level and teaches you all the fundamentals you have to know to be confident with this deep learning framework."
"Artificial intelligence focuses on accomplishing smart tasks combining machine learning and deep learning to emulate human intelligence."
"Deep neural networks could do much better than the existing technology."
"Deep learning has become so much bigger in just the past 10 years."
"If you have 100 layers deep, and every second step you put a ReLU, that network's going to learn a lot more things."
"Really applying this category Theory to deep learning is one part of what category Theory does but once you start thinking about this, all things start looking like category Theory."
"A hacker's guide to language models: a code-first approach to understanding."
"It's going to make more sense if you know the basics of deep learning."
"Geometric deep learning serves two purposes: to provide a common mathematical framework to derive the most successful neural network architectures, and to give a constructive procedure to build future architectures in a principled way."
"Geometric deep learning is not just a single method or architecture but a mindset, a way of looking at machine learning problems from the first principles of symmetry and invariance."
"This is the start of what at least one way in which we can take the black box of deep learning which often is viewed as completely inscrutable and actually start to open it and start to understand how the pieces connect."
"Generative AI works with this kind of expansion from the deep learning principles."
"The software used by OpenAI utilizes Nvidia's CUDA deep neural network library and as such a training is only really happening on the GPUs, the CPUs are more of a supporting actor."
"A lot of people have been calling you The Godfather of deep learning."
"I'm going to show you how to actually build this deep learning model for object detection."
"The good bit, the juicy bit, installing TensorFlow for deep learning."
"Hey Nick, you do deep learning, right? Is Lewis Hamilton the greatest driver of all time?"
"Scaling up deep learning isn't the path to AGI."
"Machine learning is a technique to achieve AI, and deep learning is a subset of machine learning."
"Google's TensorFlow is currently the most popular deep learning library in the world."
"Deep learning platforms like TensorFlow enable us to rely on neural networks to predict input shapes."
"TensorFlow 2.0 supports eager execution by default. It allows you to build your models and run them instantly."
"Deep learning models are essentially like really big interpolators of arbitrary manifolds."
"Deep learning has its limits. You can use deep learning for continuous problems where the data is interpolative and has a learnable manifold."
"We wanted to bring together deep learning and reinforcement learning."
"The application of deep learning is non-obvious. It's not going to work in the naive way that most people apply it to, for example, predicting market prices."
"So this is why the Transformer is so important and so seminal in the research of deep learning because it is simply applicable to any almost any domain you can think about."
"Deep learning is representation learning."
"The key idea of deep learning is to compose a simple set of General modules into highly complex functions."
"Deep neural network architectures can get way deeper than people might have imagined."
"Residual learning encourages building blocks to make small and conservative incremental changes."
"...deep learning and deep neural networks are very effective tools for us to address the representation learning problem."
"...the deep learning idea can provide us general tools to address different problems in different areas."
"Deep learning provides general tools to address representation learning problems across different areas."
"Deep recurrent neural networks can be built by stacking many recurrent neural network units together."
"Residual connections are essential for training very deep LSTM models, enabling up to 16 layers."
"The reliance on human knowledge is largely reduced with deep learning."
"Deep learning can be a general toolbox for us to solve many problems in many areas."
"Impractical Deep Learning for Coders" in the sense that we are certainly not going to be spending all of our time seeing exactly how to do important things with deep learning.
"Transformers is deep learning or AI architecture that ingests a lot of data and knows exactly what to do with it."
"Deep learning, it's called deep learning because you can stack the layers of a neural network much, much deeper and still get good results out of it."
"The solution to problems with deep learning so far has always been to use more deep learning not less."
"There is not an obvious limit to deep learning itself because deep learning is not just three algorithms, deep learning is a programming paradigm."
"In most of deep learning research, we do not know why things work in the way they do."
"We give it the query, the key, the value, the mask, and the dropout layer."
"The geometry of deep learning networks tells us about their inner workings. It's basically a very intensive form of trial and error."
"The big deal right now is deep learning. Deep learning is, in essence, it's fitting a highly nonlinear model."
"But what is absolutely non-controversial is that deep learning is the most fundamental advance in artificial intelligence research since we started, since that summer of 1956."
"Imputing this kind of knowledge into a deep learning model, this is called an inductive prior."
"All the approaches we've seen were instances of a common blueprint of geometric deep learning where the architecture emerged from the assumptions on the domain underlying our data and its symmetry group."
"...so what people actually do now is build essentially deep model predictive controllers where they use deep learning for the model very very fast model evaluations and then you wrap this model predictive control around a deep learning architecture."
"Deep learning has really taken computer vision systems by storm because of their ability to learn directly from raw pixels and directly from data."
"If we want to get to human level intelligence, we need both deep learning and symbolic reasoning capabilities."
"I'm working on an approach to combine symbolic AI with deep learning."
"Deep learning walks through the wall."
"Parallel processors work quite well for problems such as deep learning."
"Deep genius models are also very useful for many scientific applications."
"So, deep learning ends up providing this sort of very flexible, almost universal learning framework which is just great for representing all kinds of information."
"The real reason why deep learning is exciting to most people is it has been working."
"But going even beyond that, what has just been totally stunning is over the last six or seven years, there's just been this amazing ramp in which deep learning methods have been keeping on being improved and getting better at just an amazing speed."
"Deep networks are programs without a programmer. Which makes them different from regular software."
"You can take data science and deep learning from the notebook into the field and solve some real-world problems."
"...what I would love for everyone to take away from this webinar is that deep learning is a tool and then what matters most about tools is the people wheeling them right what'd you make with that."
"We've seen rapid progress in deep learning, especially in computer vision."
"The number one factor that determines the accuracy and quality of your deep learning model is really the quality of your training samples."
"Deep learning is a subset of machine learning, where you use the concept of layers to perform tasks."
"We really aim to advance the future of deep learning and AI."
"This is why that today for the rest of this lecture I really want to focus on some of the limitations of deep learning algorithms that you've learned about in this over the course of this class."
"This time will be different. This time, the algorithm is based on a set of algorithms, but fundamentally, the most core one is called deep learning."
"It's more like if you just optimize this one thing and deep learning lets you very effectively optimize that one thing this is what comes out of it."
"...it's not part of the conversation now as much as it used to but they would say oh well the deep learning system learns the rule that's there and what you as a physicist understand or what a philosopher would understand is the rules are underdetermined by data..."
"Recently, deep reinforcement learning got a lot of press when Google's DeepMind put together a Go player that could beat the world's human."
"Bayesian deep learning is basically grounded on learning probability distributions over our parameters rather than point estimates."
"Considerable performance going to a deep net."
"Deep learning perception in the loop."
"We as scientists should use deep learning to find new insights rather than just make black box predictions."
"Evidential deep learning really gives us the ability to express a form of 'I don't know' when it sees something in its input that it doesn't know how to predict confidently."
"The power of deep learning comes from its differentiability."
"The question is not whether deep learning can be general, but whether it can be competent."
"Deep convolutional networks, the architecture themselves, are iterative optimization schemes for compressible data."
"The primary takeaway when talking about deep learning is it learns from large volumes of structured and even unstructured data and uses complex algorithms to train neural network."
"Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans - learn by example."
"TensorFlow is a library for developing deep learning applications, especially using neural networks."
"There's a really big push in deep learning for molecular sciences."
"Deep learning in the context of time series, in my opinion, is not as successful as it appears to be at first glance."
"That is how you do deep Q learning from scratch."
"This deep learning powered pipeline... obviously has some amazing capabilities."
"PyTorch is basically very similar to TensorFlow in that it's a deep learning library and it offers like neural network building blocks, auto-differentiation, GPU training, and a lot more."
"...there's a very real sense in which as amazing as deep learning is and I'm gonna keep saying this deep learning is amazing but some of the gains in the sort of declarations of victory are a little bit overstated."
"Deep learning is making this function that is in the computer out of many many sub-functions that can be put together automatically."
"Batch normalization improves the speed, performance, and stability of your training."
"These are actually very commonly used in practice to train not only linear models but also a lot of deep learning models as well."
"You don't have to know many facts, but it's about to answer that question you need to know some Physics but you need to really understand it quite deeply."
"Deep learning provides artificial intelligence the ability to mimic a human brain's neural network."
"In almost all commercial cases of deep learning, we have this problem that we don't have enough data."
"Deep learning is, in a way, a subset of machine learning, and we use primarily neural networks in deep learning."
"The underlying technology behind artificial intelligence is deep learning."
"One of the core components of deep learning is neural networks."
"TensorFlow is an open source library developed by Google, primarily for deep learning development."
"The execution mechanism is in the form of graphs."
"Deep learning is actually going to enable people in rural health centers where a kind of pair of physician personnel can now become more engaged in bringing the care to healthcare."
"AI and deep learning could add as much as 3.5 trillion to 5.8 trillion in annual value to companies."
"Deep learning is the transformative subtype of AI which is just rocking it in the world of health care in terms of what it can do."
"One of the most exciting areas in deep learning is sequence models."
"One of the most powerful ideas in deep learning is the attention model."
"The transformer network is an architecture that has completely taken the NLP world by storm."
"The world today has challenges, but with the power of AI and deep learning, I think we can make it a much better place."
"The basic building block of doing anything with deep learning in Keras is the sequential model."
"Keras is a deep learning library in Python that enables you to very quickly build any kind of deep learning model you may need."
"Gain a deep understanding of SwiftUI and SwiftUI app architecture."
"I found that the deep learning models that are currently available often trained on a specific dataset."
"Deep learning... you can train essentially a network of very simple computing elements that are parametrized and you can basically train it with surprising efficacy to approximate any function you want from a whole bunch of samples."
"PyTorch essentially is a Python package that allows you, among many things, to train deep learning models in Python."
"You'll be able to take away these skills and use them to build deep learning models with TensorFlow."
"We're going to discuss some of the limitations of deep learning algorithms that we've been learning about."
"Deep learning is so powerful and so awesome."
"Uncertainty in deep learning is a very emerging research direction that's important to a number of safety-critical applications."
"This is one of those videos where you're just going to get a massive download on how to spin up and generate your own custom deep learning models using state-of-the-art technology."
"U-Net is a deep learning architecture, and why do we use it? Well, here is a quick snapshot for especially for images that are kind of busy."
"Deep learning models are very smart in understanding complicated relationships between the different series and the data and the exogenous variables."
"What deep learning is, is the process of alchemy: we take the raw materials of data plus the energy source of compute, and we get this intelligence."
"AI is the singular technology of deep learning and related technologies that is going to take us very far."
"We have developed our speech enhancement algorithms to increase the voice quality based on advanced deep learning technologies."
"What's cracking, guys? This is the second video of the series where I'll teach you everything you need to know about building your own deep learning rig."
"Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville is a really good overview of everything there is to know in deep learning."
"One of the most interesting applications of deep learning has been robotic control from visual inputs."
"Deep learning involves using large neural networks to perform machine learning."
"By incorporating sparsity into deep learning systems, we've been able to improve the efficiency by several orders of magnitude."
"We're constantly surprised as we start to incorporate stuff into deep learning."
"Deep learning algorithms can learn very complicated and complex tasks because they are using most novel algorithms called neural networks."