Our previous article covered Llama 2 in detail, presenting the family of Large Language models (LLMs) that Meta introduced recently and made available for the community for research and commercial use. There are variants already designed for specific tasks; for example, Llama2-Chat for chat applications. Still, we might want to get an LLM even more tailored for our application.
Following this line of thought, the technique we are referring to is transfer learning. This approach involves leveraging the vast knowledge already in models like Llama2 and transferring that understanding to a new domain. Fine-tuning is a subset or specific form of transfer learning. In fine-tuning, the weights of the entire model, including the pre-trained layers, are typically allowed to adjust to the new data. It means that the knowledge gained during pre-training is refined based on the specifics of the new task.
In this article, we outline a systematic approach to enhance Llama2’s proficiency in Python coding tasks by fine-tuning it on a custom dataset. — Read More
Tag Archives: Transfer Learning
The Curse of Recursion: Training on Generated Data Makes Models Forget
Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT introduced such language models to the general public. It is now clear that large language models (LLMs) are here to stay, and will bring about drastic change in the whole ecosystem of online text and images. In this paper we consider what the future might hold. What will happen to GPT-{n} once LLMs contribute much of the language found online? We find that use of model-generated content in training causes irreversible defects in the resulting models, where tails of the original content distribution disappear. We refer to this effect as Model Collapse and show that it can occur in Variational Autoencoders, Gaussian Mixture Models and LLMs. We build theoretical intuition behind the phenomenon and portray its ubiquity amongst all learned generative models. We demonstrate that it has to be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of content generated by LLMs in data crawled from the Internet. — Read More
#training, #transfer-learningTransfer Learning for Time Series Forecasting
In this article, we will see how transfer learning can be applied to time series forecasting, and how forecasting models can be trained once on a diverse time series dataset and used later on to obtain forecasts on different datasets without training. We will use the open-source Darts library to do all this with in a few lines of code. A self-contained notebook containing everything needed to reproduce the results is available here.
Time series forecasting has numerous applications in supply chain, energy, agriculture, control, IT operations, finance and other domains. For a long time, the best-performing approaches were relatively sophisticated statistical methods such as Exponential Smoothing or ARIMA. However, since recently, machine learning and deep learning have started to outperform these classical approaches on a number of forecasting tasks and competitions.
One of the distinctive features of machine learning models is that their parameters can be estimated on a potentially large number of series; unlike classical methods, which are usually estimated on a single series at a time. Although machine learning shows great potential, its utilisation still poses a few practical challenges. Read More
Now that machines can learn, can they unlearn?
Companies of all kinds use machine learning to analyze people’s desires, dislikes, or faces. Some researchers are now asking a different question: How can we make machines forget?
A nascent area of computer science dubbed machine unlearning seeks ways to induce selective amnesia in artificial intelligence software. The goal is to remove all trace of a particular person or data point from a machine-learning system, without affecting its performance. Read More
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code. Read More
Transfer Learning and Data Augmentation applied to the Simpsons Image Dataset
Deep Learning application using Tensorflow and Keras
In the ideal scenario for Machine Learning (ML), there are abundant labeled training instances, which share the same distribution as the test data [1]. However, these data can be resource-intensive or unrealistic to collect in certain scenarios. Thus, Transfer Learning (TL) becomes a useful approach. It consists of increasing the learning ability of a model by transferring information from a different but related domain. In other words, it relaxes the hypothesis that the training and testing data are independent and identically distributed [2]. It only works if the features that are intended to be learned are general to both tasks. Another method to work with limited data is by using Data Augmentation (DA). It consists of applying a suite of transformations to inflate the dataset. Traditional ML algorithms rely significantly on feature engineering, while Deep Learning (DL) focuses on learning data by unsupervised or semi-supervised feature learning methods and hierarchical feature extraction. DL often requires massive amounts of data to be trained effectively, making it a strong candidate for TL and DA. Read More
Teachable Machine From Google Makes It Easy To Train And Deploy ML Models
Teachable Machine is an experiment from Google to bring a no-code and low-code approach to training AI models. Anyone with a modern browser and webcam can quickly train a model with no prior knowledge or experience with AI. Read More
#big7, #transfer-learningTransfer Learning in Deep Learning
What is Deep Learning? It is a branch of Machine Learning which uses a simulation of the human brain which is known as neural networks. These neural networks are made up of neurons that are similar to the fundamental unit of the human brain. The neurons make up a neural network model and this field of study altogether is named deep learning.
The end result of a neural network is called a deep learning model. Mostly, in deep learning, unstructured data is used from which the deep learning model extracts features on its own by repeated training on the data. Such models that are designed for one particular set of data when available for use as the starting point for developing another model with a different set of data and features, is known as Transfer Learning. In simple terms, Transfer Learning is a popular method where one model developed for a particular task is again used as the starting point to develop a model for another task. Read More
Google researchers investigate how transfer learning works
Transfer learning’s ability to store knowledge gained while solving a problem and apply it to a related problem has attracted considerable attention. But despite recent breakthroughs, no one fully understands what enables a successful transfer and which parts of algorithms are responsible for it.
That’s why Google researchers sought to develop analysis techniques tailored to explainability challenges in transfer learning. In a new paper, they say their contributions help clear up a few of the mysteries around why machine learning models transfer successfully — or fail to. Read More
Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources
Current transfer learning methods are mainly based on fine tuning a pretrained model with target-domain data. Motivated by the techniques from adversarial machine learning (ML) that are capable of manipulating the model prediction via data perturbations, in this paper we propose a novel approach, black-box adversarial reprogramming (BAR), that repurposes a well-trained black-box ML model (e.g., a prediction API or a proprietary software) for solving different ML tasks,especially in the scenario with scarce data and constrained resources. The rationale lies in exploiting high-performance but unknown ML models to gain learning capability for transfer learning.Using zeroth order optimization and multi-label mapping techniques, BAR can reprogram a black-box ML model solely based on its input-output responses without knowing the model architecture or changing any parameter. More importantly, in the limited medical data setting, on autism spectrum disorder classification, diabetic retinopathy detection, and melanoma detection tasks, BAR outperforms state-of-the-art methods and yields comparable performance to the vanilla adversarial reprogramming method requiring complete knowledge of the target ML model. BAR also out-performs baseline transfer learning approaches by a significant margin, demonstrating cost-effective means and new insights for transfer learning. Read More
Rick's Cafe AI 