Machine learning can feel confusing at first because you have a ton of options and all this training material. I just want something that’ll get me going fast. If I start with a working example, I can build from there and learn quicker.
Of course, we could go through all the tutorials, examples, and documentation. That’s fine to get a general understanding, but I learn best when I get my hands dirty. Although you’re watching this video, it’s not the same as hands-on learning.
But we’ll walk through a sample code at least, that’s fairly simple. And not to toot my own horn, but the model could be easier. there are patterns you need to know when exploring AI models. A great use for AI nowadays is processing information like text, audio, photos, or videos and producing some type of output or doing a task.
Generative AI really excites me because it’s adaptable, though it can be slow and pricey. If we want to achieve precise goals, we don’t need to worry about prompts; those instructions you give an AI that you fine-tune to gauge their responses. Even without these prompts, if you’re training your own model on a specific task, it’ll still do well and it won’t take much energy or time to train.
We’ll look at some typical code in most common machine learning models. Our aim is to write a quick Python script that will feed data into an AI model, train it, and then use that model to solve unseen tasks. Let’s dive into the code line by line; I’ll explain as much as I can.
I recommend starting with Keras because it’s one of the simplest AI model frameworks. It meets all your performance needs yet it’s still simple to use; out of Tensorflow, PyTorch, Scikit-learn and Keras, it’s the easiest. Keras sits neatly at the top, drawing power from lower-level machine learning models and their frameworks.
We are going to use Numpy, a library written in C that pairs with Python to handle arrays and numbers efficiently. AI models all are about matrices. These layers are the ones we’ll use in our Sequential model, starting with Dense.
A Dense layer is just a typical matrix, with defined dimensions, and these are the weights or parameters we want to hold on to. We’ll train that matrix using Stochastic gradient descent (SGD). During training, SGD shuffles the data instead of going through it one by one. It’s like a shuffle mode for your data.
We also deal with hyperparameters. Hyperparameters define a model’s characteristics. One such parameter is the vocabulary or tokens, we use to process input data where every sentence is tokenized and then embedded to generalize meanings based on the position of each character.
This means the computer can see patterns and offer us answers. Considering our input data, a cluster of sentences, we’ve designed an alphabet that comprises lowercase and uppercase English letters, numerals 0-9, and a few symbols. This way, we can reduce the dimensionality of the input data while retaining relevant information without having to bother about capital and lowercase letters.
Additionally, the algorithm includes a few hyperparameters; input dimension, output dimension, learning rate, and epsilon. These matrices are generally fixed; hence, we create a shape that multiplies with the input matrix. The output dimensions are a single floating point number optimized towards whether the input sentence had a negative or positive tone.
Next, the learning rate determines the magnitude of backpropagation, where the delta, how much we’ve strayed from the correct prediction, is multiplied by the learning rate to adjust the matrix’s floating-point numbers. Epsilon is a way of ensuring that no input value is absolute zero since zero would deactivate neurons during matrix multiplication. It provides a cushion to ensure there’s some activation or throughput.
For a machine learning model, data preparation is crucial. Clean, well-organized input data is what the model uses to train itself, so we must ensure the accuracy and reliability of all the data we provide. In this case, we start with sentences and instructions, and we need to prepare the data for introduction into an AI model.
We translate every sentence into a series of floating-point numbers based on our token map, so each letter is replaced by its corresponding number. To provide a better perception for the computer, this process goes a step further by generating a self-correlation vector from the tokenized sentence, although this is optional. Once assigned, the values remain static and are only altered by the formula mentioned above.
Now that we’ve set up the AI model, we can prep it for operation. The input data (sentences) and targets (answers) are next in line to be fed into the model. We import all the sentences into the model.
Each of these serves as training data. The targets, the task the AI is assigned, are listed alongside each sentence.
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