How do you teach NLP to beginners?

In my experience teaching over 150,000 learners AI, you have to find the sweet spot between practical and theoretical. Make sure you cover tokens probabilities and language models. But also dive deep into project based learning.

Personally, I would start with showing examples of NLP that people can relate to. A lot of people probably aren't aware of how many of the services they're using daily are relying on NLP. Easy examples would be auto-correct, Google search and - most recently - ChatGPT. Once you caught their interest, you can go deeper into the details step-by-step, explaining what techniques are used in the examples you gave and how they work. Periodically review the examples whenever you're discussing a new topic and explain how things are fitting together to create a bigger picture.

Begin with why NLP is important and how it works in real world. List the use cases how this technology is applied in different industries.Due to its complexity, explain why NLP is difficult.

It's critical to understand the context of: * Who are you teaching?; and * Why do they want to learn? For example, if you're educating the c-suite you need to present examples and anecdotes that align to their business objectives and KPIs. By understanding their pain points, you can direct the conversation to case studies that demonstrate the "art of the possible" and its relevance to their team. In contrast, if presenting to data scientists it's still useful to provide case studies and examples for business context. Eventually you need to drill into the technical details, including the 'gotchas' when applying the techniques in practice.

In my experience, it can be done with: Conceptual Grounding: Begin with the basics of language processing and ML concepts to establish a strong foundation. Interactive Demos: Use relatable examples to illustrate NLP tasks like sentiment analysis, text classification, and chatbots. Step-by-Step Implementation: Guide them through hands-on coding exercises, starting with simple tokenization and gradually progressing to more complex tasks. Model Understanding: Explain core algorithms like word embeddings and introduce pre-trained models like BERT, showcasing their impact on NLP advancements. Real-World Project: Practice with real world projects.

The foundational understanding of NLP is akin to learning the grammar of a language. Focus on making these basics tactile and visual. Using interactive examples and exercises not only helps in comprehension but instills confidence in the learner. It's like teaching cooking by actually getting the students to chop and sauté rather than merely showing them pictures of the process. The hands-on approach ensures that the foundational concepts become part of the learners' natural thinking process.

Start with the basics, such as the data types and sources used in NLP, and the common methods and tools for analyzing and manipulating natural language data. Use interactive examples and exercises to help beginners practice and understand these concepts.

To teach NLP to beginners, start with the basics of natural language, such as parts of speech and sentence structure. Then, introduce the basics of machine learning, such as supervised and unsupervised learning. Finally, teach the basics of Python and NLP libraries that are widely used in NLP.

The two main libraries to cover as a part of NLP, imo, are LangChain and LlamaIndex. These libraries add the additional capabilities that make LLMs particularly intesting.

Python and NLP libraries are the tools of the trade. Teaching these is like giving an artist different brushes and colors. Starting with the very basics of Python ensures that even those without a programming background can keep up. Providing code snippets and working through them together builds not just understanding but also the practical skills required for real-world application. Think of this as teaching someone to fish, rather than just giving them a fish.

I agree that early on, the focus should be on concepts, applications and building blocks and less on the libraries used. However, I would start to include Python (or any programming language that is used) as early as possible. Not only does it help to understand theory at a deeper level by adding an alternative representation (text/formulaic vs. code), it's also enabling interactive experimentation. Applying concepts you've learned is not only fun but also an effective way to learn new things.

Machine learning allows computers to learn patterns from data in order to make predictions or decisions without explicit programming. The goal is to develop algorithms that can receive input data and leverage statistical analysis to output values of interest. At its core, machine learning identifies relationships within data to build models that can generalize to new unseen data. Broadly, machine learning approaches can be grouped into three main paradigms: supervised learning, unsupervised learning, and reinforcement learning.

Giving Personalize Language accurately explains the essential aspects of machine learning and deep learning in the context of NLP, covering basic concepts, terminology, frameworks, and practical applications.

Machine learning is a branch of artificial intelligence which develops algorithms and models capable of learning patterns and making predictions or decisions without programming. Deep learning is a subset of machine learning that uses artificial neural networks, inspired by the human brain's structure, to learn hierarchical representations of complex data. Key concepts include supervised, unsupervised, and reinforcement learning, as well as classification, regression, and clustering. Optimization, loss functions, and gradient descent are important for finding the best model parameters. Backpropagation calculates the gradient of the loss function to train deep neural networks. Activation functions determine neuron outputs based on inputs.

One of the latest and most important emerging trends in NLP is the concept of Augmented LLMs. This involves building support systems around LLMs, which leverages their characteristically strong language ability, and combines it with domain or task specific knowledge. This knowledge may be stored in a range of data storage media, including classic relational databases, time-series and object oriented databases, vector databases, and knowledge graphs. Typically, this involves using the LLM to query the chosen data medium, ie. by translating prompts to SQL queries, retrieving relevant information, and then adding it to the primary prompt. This allows LLMs to be leveraged for specialized tasks without expensive training.

Both the focus and attention of experts and the general public are that we can see the beginnings of a prota-intelligence through the behviours and capabilties on LLMs. The concept of AI Agents, that is LLM's acting as RAG / CoT orchestrators across multiple function and API calls, with vectorbase memorisation and summerisation, to facillitate complex outcomes is so very close to AGI that if you squint a little, it looks just like it. Of course this is debaitable, but it is not far off I would put forward as there is so much focus on solving the challenges in this particular space.

In my experience, nothing beats building a project and learn from it. One can learn all the theories in the world but by building projects, one will better grasp the concepts and limitations of the techniques. I would encourage to use some cloud based IDE and a few simple steps to get the beginner started on creating a chatbot that uses the NLP techniques.

Discover NLP's trends & applications: e.g., social media, e-commerce, healthcare, GPT-3. Embrace tech like transformers, BERT; learn via online courses, books, blogs. My experience: I saw NLP propel customer service through chatbots, aiding diagnosis in healthcare. Exciting journey!

Encouraging beginners to experiment and learn on their own fosters deeper understanding and practical skills. For instance, guiding them to build a simple sentiment analysis model on their own, even if it’s rudimentary, helps them grasp the concepts of tokenization, vectorization, and model training more effectively than passive learning. This hands-on approach not only builds confidence but also cultivates problem-solving abilities, allowing them to learn from mistakes and successes alike, which is crucial in mastering NLP.

Some degree of prompt engineering skills will most likely be a required common skill, much like basic google search syntax is/was. The best and easiest way for beginners to learn would be to play with Tree of Though and Chain of Thought prompting, to undertand the ability of these tools.

AI is an amazing field that offers endless possibilities for creativity, innovation and problem-solving. The best way to learn is doing , experimenting and having fun with it. Developers can start using IDE tools (Github copilot, Code Whisperer, chatgpt) for code suggestions, code reviews, performance tuning, generating sql. This would make them indirectly tune their prompts to get the best out of the AI tools for development.

I would end the teaching with a comparison. Some problems that we solve without NLP and and how we solve it with NLP and why it matters. This section would give clear examples why NLP matters. What problems NLP can solve specifically. After that I would take the same problem and now again make it before and after LLMs. So in overall we will have three phases : Before NLP, After NLP ( without Llms) NLP after LLMs

From my experience leveraging AI in business, it is key that programmers and data scientists understand the importance of involving the business owners and subject matter experts to truly understand the data, the problem, and what a solution needs to look like in order for it to add value in practice. Technology alone will not get you there!

I would also look at evaluation of NLP tasks, especially if they are not of classification sort. Introducing common evaluation scorings or tips on how to carry out human feedback evaluation is increasingly important with the rise of LLMs, to ensure NLP tasks by LLMs are of value and accurate.

Starting from linear algebra, matrix factorization and singular value decomposition. Grasping the idea to encode letters, words or sentences as vectors or another algebra object. Moving through the concept of bag of words through LSA and LSI. Then going after LDA. Passing through Word2vec and after that, delve into deep learning architectures: RNN, LSTM, Autoencoders, attention mechanism like Bahdanau’s and others to finally dive into transformers and LLM. After that you can fusion all you NLP baggage with image recognition to know about Multi Modal Architectures. It’s a long path but worth to walk. NLP is one of the most important game changers in business, industry and research. Recommendation, translations, text summaries, lot to play!

Harness NLP's power: While working at a tech startup, I used NLP to analyze customer feedback. Spotting trends and sentiment helped improve our product. NLP's potential is vast - from chatbots enhancing customer service to sentiment analysis refining products. Remember, understanding the context is key!