All about Deep learning

Today's world is driven by the emergence of Artificial Intelligence. AI has helped in various sectors i.e agriculture, medicine, research, education and so much more. Say for example if you are a movie fan and probably you use Netflix for movie streaming, you are likely to get movie recommendation of your choice. All this is done by Artificial Intelligence algorithms that try to behave like humans. Though AI has several fields, they should not confuse you, they are Machine Learning, Deep learning, and Data science. 

So what exactly is deep learning..

What is deep learning

Deep learning is an Artificial Intelligence field that uses complex algorithms inspired by the human brain. The human brain is a complex organ made up of millions of neurons. Now the brain makes decision based on the information provided. This same workability applies to Artificial Neural Networks(ANN). 

Imagine a scenario where you are provided with two know animals, say a dog and a cat, you'll be able to identify them quickly because you have been exposed to them or you have been learning about them. You will be able to distinguish them because of their physical appearance, sounds they make, body size and many factors. Now what if you are provided with two dogs, maybe one of them has black and white spots while the other is pure brown? Well, you'll still be able to say that both of them are dogs maybe because of their physical appearance, size and sounds made.

Deep learning models work the same way. A deep learning model is exposed to large set of data, now this set of data in programming is known as a dataset. Constant training of the model should be able to distinguish between a cat and a dog in this case. And should be able to distinguish between two dogs based on their physical appearance.

How deep learning works

Deep neural networks have many layers that process data allowing them to learn intricate patterns. In this case patterns are points that make a difference. Say a dog and a cat, their head shape is a little bit different, that may be the starting point in simple terms. 

You should know that this patterns may be hidden within the dataset provided. Deep neural network Excels in finding this patterns, like I said earlier about the cat and dog problem, the model might find the difference using the shape of the head, ears, eyes and so on. 

Deep learning models can..

1. Recognize images

2. Recognize speech

3. Translate languages

And so much more..

How does a neural network learn

A neural network learns by adjusting its parameters, often called weights based on the differences between its prediction and the expected targets in the training data. This method is known as back propagation.

During training, the network's forward-pass computes prediction then compares them to the actual labels, calculating a loss function that quantifies the error.

Next, gradients of the loss with respect to each parameter are computed through backpropagation, indicating how much each weight contributed to the error.

Finally, the network updates its weights in the opposite direction of the gradients using optimization algorithms like stochastic gradient descent (SGD), aiming to minimize the loss function and improve its predictions iteratively over many training steps.

One thing to NOTE: A neural network follows this order: PREDICT -> COMPARE -> LEARN

One of the methods I find most fascinating is the gradient descent.

Gradient descent

Gradient descent is an optimization algorithm used to minimize the loss function of a deep learning model. It works by iteratively moving in the direction of the steepest descent of the loss function with respect to the model parameters.

There are different variants of gradient descent, such as batch gradient descent, which computes the gradient using the entire training dataset, and stochastic gradient descent (SGD), which computes the gradient using only one data point at a time, making it faster but more noisy.

Mini-batch gradient descent is a compromise between the two, computing the gradient using a small subset of the training data. It combines the efficiency of SGD with the stability of batch gradient descent.

Gradient descent requires careful tuning of hyperparameters like learning rate, which determines the size of the steps taken during optimization, to ensure convergence to the optimal solution without overshooting or getting stuck in local minima.

Advantages of deep learning models

Highly adaptable: They can learn from diverse and large datasets, making them suitable for a wide range of tasks.

Feature learning: Deep learning models automatically learn relevant features from the data, reducing the need for manual feature engineering.

Hierarchical representation: They can capture hierarchical representations of data, learning abstract concepts at higher layers.

State-of-the-art performance: Deep learning models have achieved state-of-the-art performance in various domains, including computer vision, natural language processing, and speech recognition.

Scalability: Deep learning models can scale with data and computational resources, making them suitable for both small and large-scale applications.

Generalization: They often generalize well to unseen data when trained on diverse and representative datasets, reducing overfitting.

Python frameworks for deep learning

Python has proven to be the best language for AI algorithms due to its:- friendliness, fast, simple and has a large community. I have complied the following frameworks that you can install using pip for deep learning.

They include:-

Tensorflow

PyTorch

Keras