Machine Learning(ML) and Deep Learning(ML)

In this blog, we will also cover overview and differences between Deep Learning and Machine Learning on various points.

Introduction to Machine Learning

“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E ” by Tom Mitchell

Machine Learning
Machine Learning

ML is a subset of Artificial Intelligence, focusing on the development of machines and algorithms that can access the data and use it to learn for themselves.

Types of Machine learning

Machine learning algorithms are often categorized as Supervised, Unsupervised, and Reinforcement ML algorithms.

Supervised ML algorithm searches for patterns within the value labels that were assigned to data points. With the base of these level data set it make predictions.

Unsupervised ML No labels are associated with data points, it organized data into a group of clusters.

Reinforcement learning Algorithm that Agent interacts with its environment by producing actions and discovers errors or rewards. After some time, the algorithm changes its strategy to learn better according past experience.

Machine Learning

Limitation of the Machine Learning

Machine Learning is useful only when Data Volume is not Large that is where we have a large number of inputs and outputs ML algorithms are not efficient. Example of handwriting Digit reconciliation.

 Second limitation of ML is in Field of feature extraction.

Therefore, these ML algorithms are very difficult to apply to complex problems like hand writing recognition, Natural language processing (NLP), etc.

Machine Learning
ML and DL

Deep Learning

Deep learning is one of the only way by which we can solve the problem of high dimensionality data and   overcome the challenges of feature extraction.

Deep learning is a subset of ML in that has Machine capable of learning unsupervised from data that is unstructured or unlabeled. It inspired by biological Neurons also known as Deep Neural Network.

Deep Neural Network or Deep Learning

Deep learning inspired by functionality of biological neurons an artificial neuron (also called perceptron) was developed.

Deep learning algorithms are constructed with connected layers. All Deep neural network will consist of three main layers:

The Input Layer

The Hidden Layer

The Output Layer

Differences between Deep Learning vs. Machine Learning

Deep Learning techniques tend to solve the problem end to end, whereas Machine learning techniques need the problem statements to break down to different parts to be solved first and then their results to be combine at final stage.

Deep is good and accurate when data volume is large.

Machine Learning is an approach to achieve AI.

Deep Learning could be a technique for implementing ML.

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