Introducing Large Language Models (LLMs)12:56 with Treehouse
Large Language Models are rapidly transforming natural language processing and shaping the future of technology. With products like Alexa, GitHub Copilot, ChatGPT, and many more, the future is bright for this quickly evolving technology. This microcourse was written with the help of ChatGPT and produced using Synthesia, an AI video creation platform. We'd love your feedback! Send an email to email@example.com.
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Welcome to this Team Treehouse micro course, Introducing Large Language Models. 0:00 This micro course was written with the help of a large language model 0:05 called Chat GPT, let's get started. 0:10 Large Language Models or LLMs are a type of machine learning model, 0:16 specifically trained to understand and generate natural language text. 0:21 It's a subset of artificial intelligence, or AI, that allows computers to 0:26 automatically improve their performance in a task by learning from examples provided. 0:30 In order to understand this concept better, 0:36 let's first talk about machine learning. 0:38 Machine learning is a method of teaching computers to learn from data without 0:41 being explicitly programmed. 0:46 It's a type of AI that allows systems to automatically improve their 0:48 performance with experience. 0:52 Let me give you an example to help illustrate this idea. 0:54 Imagine you want to teach a computer to recognize pictures of cats. 0:57 You would start by providing the computer with pictures of cats along with 1:02 pictures of other animals like dogs and birds. 1:07 As the computer sees more and more pictures, it learns to recognize 1:10 the characteristics of a cat like its tail, ears, and whiskers. 1:14 This process is called training. 1:18 Once the computer has been trained and you show it a new picture, 1:20 it will be able to tell you if it's a picture of a cat or not. 1:25 Large Language Models began in the mid-20th century with the development of 1:31 artificial neural networks, 1:36 a type of machine learning model inspired by the way the human brain works. 1:37 Large Language Models are typically implemented as neural networks. 1:42 Neural networks are good at handling large amounts of data and 1:46 can be trained to perform well on a wide range of tasks, 1:50 so they're the most popular choice for building LLMs. 1:54 The parameters or values that are learned during the training process 1:58 are used to make predictions. 2:02 They are called large because they typically have a large number of 2:04 parameters which allows them to understand and 2:09 generate human language with a high degree of accuracy. 2:12 LLMs are used in natural language processing or NLP, a subfield of AI that 2:16 focuses on the interaction between human language and computers. 2:20 There are many types of machine learning, but 2:25 the two main categories are supervised and unsupervised. 2:28 In supervised learning, the computer is given a labelled data set, 2:32 which means that the correct answer is provided for each example. 2:37 In our cat recognition example, each picture is labeled as cat or not cat. 2:41 The computer learns to find the patterns in the data that 2:46 correspond to the correct labels. 2:50 In unsupervised learning, the computer is given an unlabeled data set, and 2:52 it must find the patterns and structure in the data on its own. 2:56 Unsupervised learning can be more challenging than supervised learning, 3:00 because there is no clear guidance on what the model should be learning, but 3:04 it can still be a powerful tool for uncovering patterns and insights in data. 3:09 LLMs are trained using unsupervised learning, because it allows them to learn 3:14 patterns in large amounts of text data, such as the vast amount of text data 3:19 available on the internet without the need for explicit supervision. 3:23 This makes them ideal for tasks such as natural language, understanding, and 3:28 generation. 3:33 But also raises concerns about their ability to perpetuate biases, 3:33 which we'll talk about a little later. 3:38 Here are some examples of unsupervised learning. 3:40 Clustering is a technique used to group similar data points together. 3:44 For example, a clustering algorithm can be used to group customers with similar 3:48 purchasing habits into the same cluster. 3:53 Dimensionality reduction is the process of reducing the number of features in 3:56 the data while preserving as much information as possible. 4:01 A real-world example is in the field of image compression. 4:04 Digital images are often large in size and can take up a lot of storage space. 4:08 Dimensionality reduction can be used to reduce the number of 4:13 pixels in an image while preserving its overall appearance. 4:17 Anomaly detection is a technique used to identify data points that deviate from 4:21 the normal or expected behavior. 4:26 For instance, this can be used to detect fraud. 4:28 If a credit card transaction is abnormal, it can be flagged as suspicious. 4:31 Unsupervised learning, can help to discover hidden patterns and 4:36 relationships in data even when we don't have any labeled data. 4:40 And it can be useful for many industrial applications such as quality control, 4:45 fraud detection, and more. 4:50 Next, let's take a look at the ways biases can occur in training data. 4:52 Bias in training data refers to the phenomenon where the training data 4:57 used to train a machine learning model does not accurately represent 5:02 the population of interest. 5:06 This can lead to models that perform well on the training data but 5:08 poorly on new unseen data causing inaccurate or unfair results. 5:13 Biases in training data may be introduced in a number of ways. 5:17 Sampling bias occurs when the training data is not randomly sampled from 5:21 the population, leading to over representation or 5:26 under representation of certain groups. 5:29 Measurement bias occurs when the features used to represent the data are not 5:32 relevant or are measured differently for different groups. 5:37 Demographic bias occurs when the majority of data used to train the model is 5:41 from a specific group and not representative of other minority groups. 5:46 It can lead to a model that performs well on the majority group, but 5:51 poorly on minority groups. 5:55 Temporal bias occurs when the data used to train the model is from 5:57 a specific time period and not representative of the current time. 6:01 This can lead to a model that performs well on historical data, but 6:06 poorly on current data. 6:10 It's important to be aware of potential biases in training data and 6:11 address them by collecting more representative data or 6:15 by using techniques such as resampling, data preprocessing, or reweighting. 6:19 Next, let's look at some LLMs in use today and the types of products that use them. 6:24 Some examples of Large Language Models in use today 6:31 include GPT-3, BERT, T5, and XLNet. 6:36 Which have been trained on massive amounts of text data and 6:40 can perform a variety of language tasks such as translation, 6:44 summarization, and question answering. 6:48 In fact, the majority of this micro course was written with ChatGPT, 6:51 a variation of GPT-3. 6:56 ChatGPT is trained on an immense dataset of conversational text, 6:58 which allows the model to understand and generate human like text that is 7:02 appropriate for a wide range of conversational contexts. 7:07 It can be used in many natural language processing tasks, 7:10 such as creating learning resources like the one you're watching now. 7:14 Large Language Models are used in a variety of products, 7:19 including language translation services. 7:23 A great example of a language translation service is Google Translate, 7:26 a free online language translation service developed by Google that can 7:31 translate text, speech, images, and web pages in over 100 languages. 7:36 LLMs are also used to power the conversational abilities of chatbots and 7:41 virtual assistants. 7:46 They can understand and respond to a wide range of inputs, 7:48 from simple questions to complex conversations. 7:52 Siri and Alexa are a few well known examples. 7:55 Content generation for social media and other forms of digital media. 7:59 LLMs can generate human like text, which can be used to generate articles, 8:04 product descriptions and instructional videos. 8:08 Automated writing and content creation tools, 8:13 ChatGPT is a recent high profile example of a product in this category. 8:16 Sentiment analysis, LLMs can be used to understand the emotions and 8:21 opinions expressed in text and social media content. 8:26 Text summarization and analysis tools, GitHub Copilot is one example. 8:29 Copilot is a code completion and code assistance tool developed by GitHub 8:34 designed to help developers write code more efficiently and accurately by 8:39 providing suggestions and predictions for code snippets as they type. 8:44 GitHub Copilot uses machine learning models to understand the context 8:48 of the code and predict what the developer is trying to write. 8:53 Demand for professionals in these areas has grown rapidly in recent years, 8:58 as more and more companies adopt these technologies. 9:03 So, what does the future hold for Large Language Models? 9:07 The world is likely to see continued advancements and 9:12 increased adoption in a wide range of industries as the technology behind 9:15 language models improves, and becomes more sophisticated. 9:19 The outlook for career opportunities in Large Language Models is positive. 9:23 With more and more companies using language models for 9:29 tasks like language translation and chatbots, there are many opportunities for 9:32 individuals with the right skills and experience to work on these applications, 9:36 and to work within the broader field of data science. 9:41 LLMs are only a subset within the vast field known as data science. 9:45 Data science is a rapidly growing field, 9:51 encompassing a wide range of techniques, tools, and applications. 9:53 With technologies advancing every day, the demand for 9:59 data scientists will continue to advance as well. 10:02 In fact, faster than the average for most other occupations. 10:05 As new discoveries are made in the field, so too will new professions emerge. 10:10 So, if you're asking yourself, how can I get started in the field of LLMs? 10:16 Here are a few next steps to consider. 10:22 Study the fundamentals of machine learning and natural language processing, 10:26 and experiment with pretrained LLMs like GPT-3, BERT, or T5. 10:30 This will help you understand how they work and 10:36 how to use them in real world applications. 10:38 Join a community or forum where people discuss and 10:42 share their experiences with LLMs. 10:45 Develop your own LLMs. 10:49 You can use open source libraries such as TensorFlow or 10:51 PyTorch to build your own models and gain hands on experience. 10:54 It's worth noting that the field of data science is rapidly evolving. 10:59 The skills required to work with LLMs are constantly changing. 11:04 So, staying up to date with the latest developments is essential for 11:08 professionals in this field. 11:11 Finally, consider taking a course or earning a degree in a related field such 11:14 as computer science, data science, or artificial intelligence. 11:18 To continue your learning journey with Treehouse, 11:24 check out these Treehouse courses. 11:26 In the course Machine learning basics, 11:30 dive deeper into machine learning frameworks. 11:33 You'll learn to use a Python library called scikit-learn, 11:37 which includes well designed tools for performing common machine learning tasks. 11:40 As well as Anaconda, 11:44 a Python-based platform focused on data science and machine learning. 11:46 In introduction to algorithms, take your first steps toward understanding 11:54 the world of algorithms, time complexity, and data structures. 11:58 In this course, our teaching team will examine algorithmic thinking, and 12:03 you will learn how to implement algorithms in code. 12:07 And data analysis basics provides a comprehensive overview 12:12 of charting, visualizing, and analyzing data. 12:17 We hope you found this micro course helpful in introducing you to 12:24 Large Language Models. 12:28 As we've seen, machine learning and AI are positioned as the future of tech. 12:30 We at Treehouse hope this step we've taken into AI generated 12:35 content has been a positive experience for you. 12:39 We want to know what you thought of this video, 12:43 please email your honest feedback to firstname.lastname@example.org. 12:47 Thanks for watching. 12:52
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