"We want to understand what intelligence is and then we can build intelligent machines of all different scales, all different capabilities."
"This AI learns each task from training examples, essentially programming themselves."
"These machines evolved intelligence without anybody telling them how to do it... they just played against each other and got better and better and better."
"I'm convinced that machines can and will think in our lifetime."
"We will define and train a neural net, and you'll get to see everything that goes on under the hood."
"Backpropagation allows you to efficiently evaluate the gradient of some kind of a loss function with respect to the weights of a neural network."
"This backpropagating signal, which is carrying the information of what is the derivative of l with respect to all the intermediate nodes, can be imagined almost like flowing backwards through the graph."
"The loss is a single number that... measures how well the neural net is performing."
"In gradient descent, we are modifying p data by a small step size in the direction of the gradient."
"Neural nets are these mathematical expressions that take input as the data and the parameters of the neural net for the forward pass, followed by a loss function that tries to measure the accuracy of the predictions."
"Machine learning expands the class of tasks that machines can start to perform."
"The TD error is an unbiased sample of the advantage function."
"Machine learning is a form of AI... it's the idea of computer systems that can learn for themselves from examples and data and experience."
"Error analysis in machine learning is what debugging is to actual programming."
"The breakthrough of the year last year for 2021 was a machine learned system called AlphaFold, which could learn to fold proteins from its amino acid sequence, a 50-year challenge in biology."
"By using machine learning that's able to detect patterns from large amounts of data, can we find patterns that are otherwise overlooked by mathematicians?"
"I think it's an extra tool that mathematicians can use, which is genuinely different and new from anything that's gone before."
"Figuring out how to make machine learning secure against an adversary who wants to interfere with it is one of the most important things researchers today could solve."
"One of the primary reasons we evaluate machine learning models is so that we can choose the best available model."
"Cross-validation generates a more accurate estimate of out-of-sample accuracy, which is what we need in order to choose the best model."
"The essence of how cross-validation works is to reduce the variance in testing accuracy by averaging the results of multiple train/test splits."
"The best model is found in the middle because it appropriately balances bias and variance and thus is most likely to generalize to out-of-sample data."
"The point of machine learning systems is that we want to know how our algorithm will perform on unseen data after training."
"We don't care about fitting the training data, we really care about how our classifier, or how our method, will perform on unseen data."
"Data augmentation is a good way of increasing your dataset without really increasing it."
"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 coolest part is that as programmers, we're not really telling this model any specifics."
"TensorFlow is an open-source library that helps you develop and train ML models."
"One of the most commonly used optimizers is Adam."
"Our loss is decreasing; this is good. And our accuracy seems to be increasing, which is also good."
"Our accuracy seems to be increasing almost to 90, okay, that's also a really good sign."
"We're going to be focusing on machine learning."
"Supervised learning uses labeled inputs to train models and learn outputs."
"Let's hop over to some more theoretical aspects of machine learning."
"Classification means predicting discrete classes."
"Those who can actually build learning machines will solve some of the most important biological problems."
"You're interested in machine learning? You want to do some networking? Try to combine the two. Just figure out how it works, figure out what the limitations are so you will be able to trip every vendor sales engineer with a PowerPoint deck, and you're good to go."
"AlphaFold's success is indicative of a machine learning system that is truly better than the more conventional biophysics-based methods."
"Scikit-Learn is a gigantic library, and there's a lot of features that, to this day, I still discover basically just by reading the documentation."
"Scikit-Learn isn't just a package at this stage; you could also say it's an ecosystem."
"Scikit-learn is possibly the most used machine learning tool in the world."
"The beauty of scikit-learn is that even though this is a completely different machine learning model, the API is still exactly the same."
"If the output of a machine learning model is your responsibility, then so is the data going in."
"It is really nice that we can add our own little logic in front of an existing machine learning model because that allows us to give a behavior that we are interested in."
"Machine learning is the most exciting field of all of computer science. I'm actually always excited about teaching this class."
"Machine learning topped the list of the most desirable skills in all of IT and information technology."
"Machine learning has advanced so rapidly in the last few years that there are so many opportunities."
"The meaningful work we could do with machine learning gives us a unique opportunity to really remake some piece of the world."
"Field of study that gives computers the ability to learn without being explicitly programmed."
"Machine learning is to evolve from a black magic, tribal knowledge, experience-based thing to a systematic engineering process."
"Most of the recent wave of economic value created by machine learning is through supervised learning."
"Machine learning is all about trying to take our AI and trying to get it to learn from data or learn from experience, much in the same way that humans might learn."
"Giving more epochs does not always necessarily increase the accuracy of your model."
"When you wire [residual networks] up in a slightly different way, you monotonically get better performance as you add more layers."
"Learning rate is one of the most important hyperparameters, and it's something that you want to adjust first."
"Their total top five error was 3.6% for classification, which is actually better than human performance on the ImageNet challenge."
"You will do portfolio optimization, you will do optimal control, you will do machine learning."
"Machine learning came about to begin with because whatever machine learning is doing and allowing for predictions, it doesn't seem easily decomposable to a set of listable rules and a map of their interactions. The world doesn't seem to work that way."
"Train test split provides a much better estimate of out-of-sample performance."
"The confusion matrix is a table that describes the performance of a classification model."
"The ROC curve is a plot of the true positive rate against the false positive rate for all possible classification thresholds."
"AUC is often used as a single number summary of the performance of a classifier."
"The NVIDIA Twitter account posted a mesmerizing visualization of the animal robot learning to walk using deep reinforcement training."
"Every problem might be a machine learning problem in the near future or today."
"But the good thing is that if we retrain the remaining weights, the accuracy can fully recover here."
"Machine learning is not just computational statistics."
"Statistics has somewhat evolved into machine learning in recent years."
"In machine learning, having a good degree of experience when it comes to implementing machine learning algorithms is vital."
"The sigmoid function is used because it takes any input, any number, passes it through the sigmoid function, and you will get a number between 0 and 1."
"We're using machine learning to recognize your handwriting and index it in Spotlight."
"Studying adversarial examples could tell us how to significantly improve our existing machine learning models."
"Attacking machine learning models is extremely easy, and defending them is extremely difficult."
"Adversarial training and virtual adversarial training also make it possible to regularize your model and even learn from unlabeled data."
"Deep learning goes One Step Beyond this and is a subset of machine learning which focuses explicitly on what are called neural networks."
"If you actually understand how machine learning works, you will realize, as I have, that the limits of AI are not foreseeable right now."
"The actual learning is based on some simple components: first, the machine has to identify patterns."
"Machine learning is teaching a computer how to learn from data."
"Machine learning is a combination of techniques from statistics, computer, and computer science communities. And it's the idea of getting computers to program themselves."
"Neural network training is essentially adjusting weights until the function represented by the neural network essentially does what you would like it to do."
"The balance in machine learning is not stopping too late or too early but finding the right moment where the model is just right."
"Like it or not, machine learning is the future."
"A machine learning engineer has to be a good software engineer first."
"It was really inspiring, the thought that we could create content to help so many people learn about machine learning."
"If 80% of our work is preparing high-quality data, then I think preparing that data is a core part of the work of a machine learning engineer."
"We studied machine learning workflow where we learn to define an objective, prepare the data, collect the data, select the algorithm, train the model, go back and test the model."
"Machine learning model learns from the past input and makes future prediction as output."
"Siri, Cortana, Iris — all of them would not be there to help you if there was no machine learning."
"Machine learning is just optimization based on data."
"Machine learning is understanding our psychology in some ways better than we understand ourselves."
"If you can teach a computer about primitives and give it the ability to guess outside of the system that you've taught it, you would have a system that would be potent enough to explore not only the theory that you feed it but the theories that it can build from that theory."
"Rather than writing down the features ahead of time, we're going to learn the features directly from the data."
"The beauty here is that as you train this model if you visualize these little vectors, it might be that they start aligning."
"It's a very simple algorithm and the weights...are all shared."
"This is actually called Bayes theorem and it's one of the most important theorems in probability and machine learning."
"You can just have the machine figure out how to do it on its own, and it comes up with this prediction that's better than running physics in a compute cluster."
"Companies like Amazon and Netflix use machine learning to suggest products that you might like to buy, movies that you might like to watch."
"The Machine Learning Revolution is going to be very different from the Industrial Revolution because the Machine Learning Revolution, it never settles down."
"Machine learning models are the obvious place to put [computational power]...they're this total bottomless pit of computation that you can throw at the problem because they scale and they just keep getting better."
"We let sort of this whole system... learn by itself what is it going to be that Alpha Star should be doing at all times."
"So given the gradient, we now take a step in the direction of the gradient in order to update our weight, our parameters, right?"
"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."
"In machine learning we want to learn a model that accurately describes our test data not the training data."
"We never told the car what a lane marker was, what a road was, or how to even turn right or left or what's an intersection. It's able to learn all of this from data using convolutional neural networks."
"But it's a crash course to get developers up to speed on machine learning and deep learning and neural networks."
"We'll go on this dataset trying to build a network that recognizes this hand-written digits from the simplest possible network all the way to 99% accuracy."
"We are going to train this neural network to actually figure out the correct weights and biases by itself."
"This idea of managing gradient flow in your models is actually super important everywhere in machine learning."
"Processing very large amounts of data, machine learning is all the rage."
"My personal belief is that we've seen something of a turning point where we're starting to understand that many abilities like intuition and creativity that we've previously thought were in the domain only of the human mind are actually accessible to machine intelligence as well."
"The first real scaled version of machine learning was this thing that Facebook introduced called 'People You May Know.'"
"It's an exceptional deal, especially in those cases where people are going to use this for machine learning and AI."
"Machine learning is teaching computers to recognize patterns in the same way that our brains do."
"My prediction is that there will be at least one, potentially several, Nobel Prizes that will result in derivative work launched directly with these computational methods."
"Turing's deeper motivation for modeling the brain was to create structures useful to learning that could advance or evolve machines."
"This kind of an image would not have been possible before. We use machine learning to take this photo in low to medium light."
"All of this makes iPhone11Pro the best Machine Learning platform in any smartphone."
"There's no way to teach a machine how to spot a defect in a piece of wood."
"The field of study that gives computers the ability to learn without being explicitly programmed."
"Linear algebra is the backbone of calculations behind neural network algorithms."
"Transfer learning has had the- Self-Attention has been beneficial in transfer learning, GPT from OpenAI and BERT are two classic examples."
"Artificial neural networks and machine learning are improving every single facet of our lives right now."
"There has never been a better time to study machine learning because you're now able to build products that have tremendous potential and impact across any industry or area that you might be excited about."
"Machine learning gives us the ability to learn things about the world from large amounts of data that we as human beings can't possibly study or appreciate."
"Our machine learning models are improving to better detect new types of spam."
"We have the internet and machine learning... it's difficult to get around."
"Keras is a very beautiful, nice API that sits on top of TensorFlow."
"PyTorch is newer, there's less existing code, it's still subject to change."
"This is the backpropagation algorithm. It's what's at the heart of deep learning."
"Most of the knowledge in the world in the future is going to be extracted by machines and will reside in machines."
"So how do computers discover new knowledge? This is, of course, the province of machine learning."
"You can retrain on your data or you can use a pre-trained model of someone else's train."
"It's so easy to lose sight of the fact that sometimes you need a really good human expert in the process of machine learning."
"3D printing can use a lot of machine learning to improve design and reduce waste."
"Artificial intelligence is such a hot topic...machine intelligence...machine should do things that human being cannot do."
"Training a model for rock, paper, and scissors resulted in roughly 97% accuracy in just a few minutes."
"Traditional programming expresses rules in code, while machine learning provides answers and infers the rules."
"Model training involves passing data, making guesses, measuring performance, and optimizing parameters to improve accuracy."
"Machine learning turns this around. Instead of expressing rules in code, we provide answers and let the machine infer the rules."
"AI encompasses many fields including machine learning, which processes large amounts of data to derive a pattern and predict outcomes."
"By allowing computers to learn how to solve problems on their own, machine learning has made a series of breakthroughs that once seemed nearly impossible."
"Linear regression: laying a foundation for future neural networks."
"Understanding error: the key to refining machine learning models."
"I'm going with chat GPT... it really elucidated where these kind of machine learning tools can take us."
"Gradient descent is used everywhere in machine learning."
"Back propagation is the process of taking the error and basically like feeding backwards the error through the network..."
"It's obvious to everyone that ML engineers are now helpful...the difference between someone who's successful gets ahead speeds up that learning curve versus someone who doesn't is a person who takes intentional action..."
"For developers who don't have a PhD in deep learning, it's really easy to get started."
"Ultra shot uses machine learning algorithms to preserve highlights and shadow details, producing natural images that feel true to life."
"After training, use the trained generator network to create to sample and create new data that's not been seen before."
"We want to write code that finds these rules for us and improves over time through examples and experience."
"Access pre-trained machine learning models with a single API call."
"Our goal is to learn a function that's mapping from our data X to our labels Y."
"Unsupervised learning... our goal now is to learn some underlying hidden structure of the data."
"Machine learning in general, you do something, maybe it increases it, maybe it doesn't. Try to be smart about how you do things."
"Convolutional nets are used for any kind of image classification or even generation."
"This is object detection... So, this is an object detection loss function."
"You want to penalize more errors on small bounding boxes rather than big bounding boxes."
"AI and machine learning is the biggest revolution today."
"The XG Boost algorithm is one of the most popular algorithms on Kaggle when it comes to winners."
"So that's a decision tree, right? So what XG Boost does, it's a gradient boosting technique."
"But that's not enough. Now that we know that XG Boost is the best model, now we can say, okay, let's optimize this model."
"Another way to improve the model is to just use way, way, way more quality data."
"Machine learning has become quite pervasive today."
"Machine learning algorithms are designed to learn such complex nonlinear relationships."
"Unsupervised learning groups the data based on some measure of similarity."
"Machine learning provides support for code generation for deployment to embedded systems."
"Training a CNN involves learning weights for filters and fully connected layers."
"Generating cool images and getting a sense of what features networks are looking at."
"Machine learning is all about optimizing for an objective function... we are minimizing a loss or error function."
"Classification: identifying which category an object belongs to."
"Clustering: taking an automatic grouping of similar objects into sets."
"The SVM tends to work better on smaller numbers."
"We've never before built machines that operate in ways the creators don't understand."
"Animators now have an experimental machine learning-based framework for lightweight character rig approximations."
"Trainium provides about 50% improvement in terms of price performance relative to any other way of training machine learning models on AWS."
"Graphs naturally generalize objects such as grids or sequences, where machine learning has already made very significant strides."
"So, for example, transformers fit within this paradigm."
"Microsoft researchers are using machine learning to help oncologists figure out the most effective individualized cancer treatment for their patients."
"Supervised learning was this machine learning problem where I said we're going to tell the algorithm what the 'right answer' is for a number of examples and then we want the algorithm to replicate more of the same."
"Gradient descent will automatically take smaller and smaller steps as you approach a local minimum."
"Python is mostly used on the web and in machine learning, artificial intelligence, and data science-related tasks."
"What's interesting about a neural network is that it can learn and adapt to different scenarios."
"FSD10 predicts height from video pixels directly without needing to classify groups of pixels into objects."
"Hypothetically, a pooled testing system designed by machine learning could lower the cost of a test to just $3 to $5, meaning the frequency of testing could increase dramatically."
"By next month July it will have the start of Dojo... optimized for machine learning... being used by Tesla."
"The choice is an illusion. You already know what you have to do."
"Machine learning allows systems to naturally learn and improve themselves by using experiences to predict outcomes without being explicitly programmed."
"Neural networks, when given a lot of data and computing power, tend to outperform all other models most of the time."
"What Rhonda Santos is doing is what needs to be done; everyone needs to fight tooth and nail."
"Reflex models are synonymous with models in machine learning."
"Gradient descent is surprisingly effective for continuous optimization problems."
"The craft of deep learning is all about creating a model that has a proper fit."
"Every time we add another layer, we get a better model of the training data."
"And Vapnik, who nobody ever heard of until the early '90s, becomes famous for something that everybody knows about today who does machine learning."
"Learning from step-by-step explanations could significantly improve the quality of models."
"Quantum computers exponential speed could expedite machine learning model training and improve natural language processing."
"Quantum computers have the potential to solve complex optimization problems, factor large numbers efficiently, simulate Quantum systems, and perform Advanced machine learning tasks."
"What tesla is doing as providing this additional guidance to the car to the neural network when it's training... so it takes less training and less data and less computation to get to the result that you want."
"Neural networks have to figure out the two-dimensional nature of an image. They have to go beyond that to understanding constructs like there are objects in the world and there is space that the objects move through."