Deep learning definition: Challenges, pros and cons

Deep Learning (DL) is a type of Machine Learning that trains computers to learn how to make decisions independently, using large amounts of data and complex algorithms. Think about how children learn from repeated experiences.

Eventually, they learn on their own what to do in certain instances. Similarly, by feeding the data to computers, they can also understand.

Deep Learning operates using a structure called a neural network, inspired by the human brain. Of course, other factors are also at play, which you can see below:

1. Neural networks - these consist of layers of interconnected nodes or "neurons," including an input layer, several hidden layers‌ and an output layer.
2. Weights and biases - connections between neurons have weights, and each neuron has a bias. These parameters, which are adjusted during training, are crucial for the network's learning process.
3. Activation functions - neurons apply these functions to transform input into output, introducing non-linearities that enable the network to model complex patterns.
4. Forward propagation - input data (like an image or sound) passes through the network, with each neuron processing the input and passing the result onward.
5. Loss function - this measures the difference between the network’s predictions and the actual targets, quantifying the network's performance.
6. Backpropagation - the network adjusts its weights and biases to reduce the loss, using the gradient of the loss function.
7. Optimisation algorithms - these update the weights and biases to make the Deep Learning model more efficient.
8. Training and testing - the network learns from a large dataset and is tested on new data to evaluate its performance.

With any learning model, some issues can arise, which include:

1. Overfitting - this happens when the model learns the training data a bit too well, including its errors. So, when it gets new data, it can make mistakes.
2. Data needs - Deep Learning models need lots of high-quality data, which can be hard to source at the speed and scale required.
3. Complexity - designing and training Deep Learning models requires cutting-edge expertise.
4. Black box - it's often hard to understand why a deep learning model makes a particular decision, which can reduce public trust in the technology.

Deep learning has transformed how we use technology, enabling computers to recognise speech, identify images‌ and even understand natural language.

It’s behind some of the impressive smart systems we use every day, like speech recognition, image identification, and even natural language understanding.

Think about those language translation tools on Google or the personalised recommendations you get on Netflix or Amazon – that’s DL at work. Even our very own Natasha, uses DL to understand your requests with speech or image recognition.

A new revolutionary type of DL is self-driving cars. Using deep learning, they’re enabling cars to understand their surroundings and eventually drive themselves.

Although they’re both AI, they differ in certain areas like complexity, design and even the data they process.

In simple terms, Machine Learning is like teaching a computer basic skills using simpler methods and less data, making it easier to understand and quicker to use. Deep Learning, on the other hand, is like teaching a computer advanced skills that mimic the human brain, which is great for complex tasks but requires a lot more data, power, and time.

Deep learning is a powerful tool that’s making computers smarter and more capable every day. Whether it's recognising images, understanding speech, or even driving cars, DL is pushing the boundaries of what technology can do. By getting to know its basics and learning how to use it effectively, you can harness its power to create smarter, more efficient systems.