"The coolest part is that as programmers, we're not really telling this model any specifics."
"The beauty here is that as you train this model if you visualize these little vectors, it might be that they start aligning."
"Model training involves passing data, making guesses, measuring performance, and optimizing parameters to improve accuracy."
"The craft of deep learning is all about creating a model that has a proper fit."
"Directly learning this model is simpler and easier."
"It's often the training data and iterating on it and labeling and managing it that actually gives you success or failure."
"A trained model is just another thing that maps inputs to results."
"We fine-tune our models to best determine when to call these tools."
"Fine-tuning a model requires far fewer examples compared to training a large language model from scratch."
"So if the models already been trained, then the weights that it has learned and optimized for are going to be in place within this saved model on disk."
"Early stopping will stop our model training once it sees overfitting occur, since we've given an evaluation set."
"We want to be careful that it doesn't overfit."
"Write to dev feature tables make sure that works with the model training pipeline."
"The model doesn't even need any explicit training to do the tasks. You just present the tasks to it in natural language and it does it."
"Since our average loss is decreasing, we can conclude that our model is training properly."
"The model does a very good job of being trained here."
"The development of llama 2 included safety measures such as pre-training, fine-tuning, and model safety approaches."
"Label images with all the elements that your model will see…"
"It's time to create and train our model."
"Here all the libraries with the required version numbers are mentioned so you can actually install all of them in a single line of code and then using the same template you can actually train any model."
"In a time series split, you want to make sure that none of the information about the future is fed into your model."
"When a model fits the training data perfectly, it probably means it is overfit and will not perform well with new data."
"So this is like after looking at all of these sites, this model is going to have a trained model where it should tell you whether or not this is good or this is an artifact."
"Now this technique is very very good because you are giving variety of samples to your models."
"The general principle is pretty clear: you collect data, you learn a model, you train a policy against that model."
"The steps are: collect data, train model, then prediction, which is also sometimes referred to as inference."
"You also learned about converting models, building models, training and saving models, and even reusing models, all common practices in AI."
"If we run the training for, let's say, ten or twenty epochs, I think the quality of the bounding box is going to get better."
"The biggest cost for AI companies is the massive amount of computing power they use to train and operate their increasingly complex models."
"Do not put too many hidden layers because then this will be overfitting on your training data."
"Once you define the model, we need to compile it using our optimizer that we just defined."
"Fine-tuning is a great way to take new data, pop it into your model, and then get it to do something that you care about."
"In ensemble learning we train multiple models on the same data set and then combine that result somehow to get our final output."
"It's a much faster tool to learning better models."
"The primary question is where is the bottleneck... data is much more likely to be the bottleneck on the construction of ever more capable models."
"They don't seem to have these big peaks and that of course means that they're not going to be susceptible to these adversarial attacks."
"That concludes how to train your own custom models."
"I'm going to make my model from 90% of the data, and I'm saving 10% that it's never seen before to help me understand how well I've done with my modeling."
"It is possible to train models that are utility-preserving and at the same time satisfy privacy requirements."
"Once you train the model with reinforcement learning from human feedback, it becomes much better at following instructions than the base GPT model."
"Once you have an instance of your ML context, you can actually load, transform the data, choose your algorithm, train the model, evaluate the model, and also save and deploy and consume your model."
"Training models is an iterative process, and you have to try different things until you find out what works best."
"Model.fit is the function that actually runs the whole training process."
"These pre-trained parameters were really good and you want to make the minimal change from the pre-trained model to the model that does what you want."
"Estimator is a machine learning algorithm that takes a Data Frame to train a model."
"The main worry is that in picking the parameters of your model, you do a really good job of capturing that training data, but it doesn't generalize."
"And second benefit is you can apply various type of transformation which you typically need to train your deep learning model."
"Now I want to show you how you can create and train your own model according to your own specifications."
"We're going to build and train a model to classify different species of iris flowers."
"Once you train this model, you can actually use it to make interesting interpretations."
"The process of model learning is what we typically call training, and when the model is making predictions about data is what we call inference."