Skip to main content

What is deep learning and how it is improving the machine learning


While Machine Learning is a subset of Artificial Intelligence, Deep Learning is a specialized subset of Machine Learning.Deep Learning layers algorithms to create a Neural Network, an artificial replication of the structure and functionality of the brain, enabling AI systems to continuously learn on the job and improve the quality and accuracy of results. This is what enables these systems to learn from unstructured data such as photos, videos, and audio files.

Deep Learning, for example, enables natural language understanding capabilities of AI systems, and allows them to work out the context and intent of what is being conveyed. Deep learning algorithms do not directly map input to output. Instead, they rely on several layers of processing units. Each layer passes its output to the next layer, which processes it and passes it to the next. The many layers is why it’s called deep learning. When creating deep learning algorithms,developers and engineers configure the number of layers and the type of functions that connect the outputs of each layer to the inputs of the next. Then they train the model by providing it with lots of annotated examples.

For instance, you give a deep learning algorithm thousands of images and labels that correspond to the content of each image. The algorithm will run the those examples through its layered neural network, and adjust the weights of the variables in each layer of the neural network to be able to detect the common patterns that define the images with similar labels. Deep Learning fixes one of the major problems present in older generations of learning algorithms.

While the efficiency and performance of machine learning algorithms plateau as the datasets grow, deep learning algorithms continue to improve as they are fed more data. Deep Learning has proven to be very efficient at various tasks, including image captioning,voice recognition and transcription, facial recognition, medical imaging,and language translation. Deep Learning is also one of the main components of driverless cars.

Comments

Post a Comment

Popular posts from this blog

ANN in depth

Artificial Neural Network Layers Artificial Neural network is typically organized in layers. Layers are being made up of many interconnected ‘nodes’ which contain an  ‘activation function’.  A neural network may contain the following 3 layers: a. Input layer The purpose of the input layer is to receive as input the values of the explanatory attributes for each observation. Usually, the number of input nodes in an input layer is equal to the number of explanatory variables. ‘input layer’ presents the patterns to the network, which communicates to one or more ‘hidden layers’. The nodes of the input layer are passive, meaning they do not change the data. They receive a single value on their input and duplicate the value to their many outputs. From the input layer, it duplicates each value and sent to all the hidden nodes. b. Hidden layer The  Hidden layers  apply given transformations to the input values inside the network. In this, incoming arcs that go from other hidden

Can AI be dangerous

CAN AI BE DANGEROUS? Most researchers agree that a superintelligent AI is unlikely to exhibit human emotions like love or hate, and that there is no reason to expect AI to become intentionally benevolent or malevolent.  Instead, when considering how AI might become a risk, experts think two scenarios most likely: The AI is programmed to do something devastating:   Autonomous weapons are artificial intelligence systems that are programmed to kill. In the hands of the wrong person, these weapons could easily cause mass casualties. Moreover, an AI arms race could inadvertently lead to an AI war that also results in mass casualties. To avoid being thwarted by the enemy, these weapons would be designed to be extremely difficult to simply “turn off,” so humans could plausibly lose control of such a situation. This risk is one that’s present even with narrow AI, but grows as levels of AI intelligence and autonomy increase. The AI is programmed to do something beneficial, but it d

What is ANN and CNN

An artificial neural network is a collection of smaller units called neurons, which are computing units modeled on the way the human brain processes information. Artificial neural networks borrow some ideas from the biological neural network of the brain, in order to approximate some of its processing results. These units or neurons take incoming data like the biological neural networks and learn to make decisions over time. Neural networks learn through a process called backpropagation. Backpropagation uses a set of training data that match known inputs to desired outputs. First, the inputs are plugged into the network and outputs are determined. Then, an error function determines how far the given output is from the desired output. Finally, adjustments are made in order to reduce errors. A collection of neurons is called a layer, and a layer takes in an input and provides an output. Any neural network will have one input layer and one output layer. It will also have one or more