A Python Developers Guide to AI in 2024

Key Takeaways

in this video I'll give you a complete guide to AI specifically for python developers you can treat this as a road map of topics and modules you may want to learn or simply a quick refresher that will keep you up to speed with everything going on in the AI world with that said let's dive in let's begin with machine learning algorithms now these are computational methods that allow computers to learn from and make decisions or predictions Based on data they do this by identifying patterns and relationships in large data sets during a process known as training now during training an algorithm will adjust its parameters in order to minimize errors once training is finished then this algorithm can make predictions usually with a high accuracy if it's been trained correctly on new data that it's never seen before this means that your program has actually learned something and without explicit programming it's able to take in some new data and make hopefully an accurate prediction about it there's all kinds of different machine learning algorithms but these are really at the core of AR icial intelligence and you should learn them first before moving on to anything more complicated now I have a list of machine learning algorithms that I recommend that you learn so let me read them out number one is linear regression followed by decision trees random forests K means K nearest neighbors and support Vector machines there are a ton of other ones that you can look at but these are some of the more popular ones and they'll give you a good foundation of machine learning algorithms and how they actually work now in order to build these out yourself or make them on your own you you can use various different python modules for example you can use S kit learn you can use numpy pandas map plot lib and Seaborn now these are all used for different tasks within actually implementing these types of algorithms but they're good to know and really where you should start if you want to be a python AI developer now obviously as a developer we can build out AI features on our own but often times it's a lot more helpful and much faster to work with pre-existing tools now that's where the sponsor of this video squid AI comes in Squid AI is a flexible unopinionated backend as a service that lets you connect and secure any database or API in minutes whether you're working with no SQL SQL graphql or HTTP squid makes it super easy to integrate with your data sources through their developer friendly sdks now one of the coolest features is how Squid's AI allows you to take action on both unstructured and structured data making it easy to build interactive fully featured AI Solutions imagine just being able to focus on your app's functionality and allowing squid to take care of all the backend complexities for you squid also offers powerful security functions seamless integration with typescript libraries and the ability to embed AI agents directly in your application this means you can have intelligent agents that interact with their environment gather data and perform tasks autonomously and the best part of this is that you can get started completely for free by clicking the link in the description or watching my full tutorial where I walk through building an entire AI application with squid in just one hour moving on we have neural networks neural networks are a specific type of machine learning algorithm whose architecture is inspired by the function of the human brain neural networks are comprised of layers of nodes or neurons which are interconnected and can transmit information between them now each of these connections between the neurons has something known as a weight this weight is adjusted during the training process to minimize errors and give a better accur for the network or the machine learning model now unlike traditional or more simple machine learning algorithms which typically rely on manually crafted features neural networks can automatically extract and learn more complex features of raw data this allows them to do more complex tasks and to come up with representations or find patterns and relationships that simple machine learning algorithms just can't do this also means that neural networks are a little bit more of a mystery after you've trained a neural network you're not quite exactly sure what it's using to come up with the correct answer but you know that it's adjusted the weights and different parameters within the network so that it's fine tune to the specific data that you've trained it on now this is a complex topic and not something we need to get into a ton but there are a few features you may want to consider learning about neural networks obviously the architecture so things like an input layer your hidden layers your output layer you want to learn about things like activation functions weights and biases and how data is propagated through the network then then there are two main terms you'll be interested in in terms of how a network is actually trained the first is back propagation this refers to actually going from the end result and going back through the network and adjusting all of the weights and biases to learn from mistakes or to move closer towards the correct answer or the correct features within the neural network the next is gradient descent gradient descent is something that refers to finding the local minimum or maximum in kind of three-dimensional or multi-dimensional space and this is something that's used to kind of guide the model in the right direction again I'm not an expert in this field and I don't want to confuse you in just a few minutes here but these are really cool things to learn about and those are some of the main topics you may want to focus on now in order to build and work with neural networks you can look at modules like P torch tensorflow and caras and you can also check out things like neat neuro evolution of augmented topologies which is a little bit more advanced but uses a neural network as its backbone moving on we have computer vision now computer vision refers to doing image and video analysis and typically things like object detection and tracking facial recognition image segmentation so picking out specific parts of images and can do all kinds of other amazing things for example I was just watching the Olympics and that's a great example where they have a ton of computer vision and they're doing things like tracking all of the places different athletes hit the ball or where they serve the ball on like a pingpong table really interesting applications and typically this can use basic machine learning algorithms or more advanced things things like convolutional neural networks now what I mean by that is we'll take an actual image and we'll analyze that image using machine learning algorithms those could be basic machine learning algorithms like some of the ones that we talked about before that are more specified for images or things like convolutional neural networks which are specific types of neural networks in terms of their architecture that analyze images and videos specifically again there's a lot of topics to get into within computer vision but if you want a few modules to check out that can help you do this the first that I definitely recommend is open CV this has a lot of built-in uh kind of models and features in it that allow you to really easily do computer vision tasks and then we have things like scikit image and pillow or pill p l check those out computer vision to me is one of the coolest Fields with in Ai and with python you can get started pretty quickly without having to be an expert now after computer vision we have large language models or llms which in my opinion are one of the most misunderstood forms of AI in terms of what they actually do and how you can use them large language models are designed to be able to understand and generate human text in a broad sense they're trained on tons of different textual data from things like books movies articles the internet doesn't matter they're trained on so much different text and this allows them to broadly be pretty good at a lot of different tasks they can answer questions they can generate essays for you they can write code they can do a lot of things decently well in so many different context because of the amount of information that they've been trained on that said most llms with the exception of things like chat GPT that have other features built into them don't have access to realtime information and sometimes they can hallucinate and give you responses that don't actually make sense even though it sounds like they do because their English is quite good this means if you want an llm to be really good for your specific task you should find tune it fine-tuning is the process of passing this specific data related to exactly what you want this llm to do for example medical diagnosis you could pass it a ton of past diagnosises and it can learn quickly and use that in combination with its natural language processing ability to really answer something quite well and to achieve higher performance now when we talk about llms there's two main types you want to be aware of the first is GPT or generative pre-trained Transformer now this process is text unidirectionally meaning going from left to right and is generally very good at generating new content now the next is Bert b t and I'm just going to read this so I don't get the name wrong this is bidirectional encoder representations from Transformers now this understand text and processes it from left to right and from right to left which allows it to be really good at comprehension and answering questions about information or textual data it's not quite as good as actually generating new content or text so generally speaking if you want to generate something new that doesn't already exist use GPT and if you want to understand something more deeply and have better comprehension you can use a Bert model now if you want to work with various llms in Python then I recommend looking at hugging face hugging face is a place that has pre-built Transformers for you that you can f- tune and bring into your python programs now moving on we get into the natural Next Step which is rag or retrieval augmented generation now this is a technique that you can use with llms to get better responses and use them for more context specific applications as I said before llms typically don't have access to realtime information or even if they do they might not have access the information you need them to have let's say you're a restaurant you have some reservations you have different menus you have a bunch of information that's constantly changing and maybe stored in some kind of database well you may want an llm internally or some kind of chatbot that you can really quickly ask questions too so you don't need to go and query all of that information on your own well if you just have a base LM it doesn't have access to all of that information so instead we use a technique called rag now rag involves your llm querying a specific type of database grabbing information that's relevant to what it needs to answer and then giving you a context specific response what I mean by that is you may ask the llm hey what's the menu tonight and what it will do is go to something known as a vector search database at least this is typically what you'll use it can very quickly find information relevant to the menu pull that Direct IR L into the llm read through it understand it and then use it to generate a response that makes sense so what you're doing is you're taking the data that you want this model to have access to storing it in a really fast database to look up stuff from again typically called a vector search or vector store database the llm will then use that database by providing some kind of prompt it will get relevant results back it will read and understand that and then generate something that's context specific this is actually not that difficult to implement I've made all kinds of videos on it on my channel and this is a way that you can really enhance llms and use that base level natural language processing and understanding ability to really enhance your own applications now if you want to build rag apps in Python you can do this using things like Lang chain olama llama index uh those are kind of the main ones you'll probably want to mess with moving on we're talking about AI agents now ai agents don't just have the ability to read information from something like a database but they have the ability to interact with their environment and use maybe various different tools that you give them access to an AI agent could be something as simple as having the ability to send an email or it could be something that has access to a full Suite of different tools and actually selects by itself what tool it should use based on the task you ask it for example you could make your own AI virtual assistant and in this case what it will do is have access to your calendar it can read that information it could create new events it could send emails on your behalf it could delete things it could clean up your desktop for you it can do whatever you want it to do if you give it access to those specific tools AI agents interact with the environment they have access to a set of tools and they can do things like determine the tasks they need to do or multiple tasks that they need to do collect data and then obviously as I said many times interact with the environment which is the key Point here to build out these type of AI agents you can use the same Frameworks I talked about before like Lang chain and llama index and they are super cool and something you should definitely mess with with that said guys that's going to wrap up this video wanted to give you a quick comprehensive guide to all of the AI stuff as a python developer you should know if you found this helpful make sure you leave a like subscribe and I will see you in the next one [Music]