**What is Machine Learning and Deep Learning?** Machine Learning is the sub-topic of Artificial Intelligence. Machine Learning is the tool that makes the system to understand, learn and improve from previous examples, without any special...
What is Machine Learning and Deep Learning? Machine Learning is the sub-topic of Artificial Intelligence. Machine Learning is the tool that makes the system to understand, learn and improve from previous examples, without any special programs.
The main aim of Machine Learning is to make computers learn, automatically. So far, machine learning is the best tool to study, understand and recognize the format in data.
Deep Learning is the sub-topic of Machine Learning. It is a software that copy, the neural network in a brain. It is named as Deep Learning, as it uses deep neural networks. Any system uses various layers, for learning through data. The number of layers represents, the depth of the model.
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Differences between Machine Learning and Deep Learning? Feature Engineering:
Feature Engineering is the process, in which the subject knowledge is kept into the creation of feature extraction. This decreases the difficulty of the data and creates formats, that are more visible for learning the working of algorithms. This process is taking more time and requires expertise. In Machine Learning, Many features that represent the data are to be recognized, by an Expert and the coding is done manually according, to the data type and domain. In Deep Learning, there is no need to recognize, the best feature that represents data. Deep Learning algorithms learn greater features, from data. So this reduces the work of developing a new feature extractor, for each problem.
Performance of both the algorithm changes, depending on the size of the data. Machine Learning performs well, on small and medium-size data. Deep Learning, performs well on Big size data. Because the DL algorithm requires, large size data for understanding it perfectly.
Interpretability is the biggest reason, for which everyone thinks many times before using deep learning algorithms. In Deep Learning, it's very difficult to understand the algorithms. For instance, if you use a deep learning algorithm to score essays automatically. It performs very well in giving scores, almost like a human. But, there is a problem. It is very difficult to understand, why it has given a particular score. So, it's very hard to understand the results. In Machine Learning, some algorithms are easy to understand and some are difficult. Machine learning algorithms like decision trees, make us understand why it selects, what it selects. So, it is easy to understand the reason behind it.
Machine Learning depends on Low-end machines. Deep Learning depends on High-end machines, where GPUs are required. Deep learning algorithms perform, a huge amount of matrix multiplication functions.
The approach in Problem Solving:
While solving a problem, In Machine Learning, we divide the problem into different parts and solve them separately, and merge them to get the final result. For instance, in an object identification task. If you use a Machine learning algorithm, it divides the task into two parts, object detection, and object recognition. In object identification, with the help of a grabcut algorithm slides through the image, and then identify all possible objects. In object recognition, with the help of recognition algorithms like SVM with HOG, we recognize the relevant objects. Deep Learning solves the problem, by using end to end approach. For the object identification task, it would give the image and location with the object name.
Learn for more Machine Learning interview questions Execution Time: Deep learning takes more time to train, sometimes up to weeks as there are many parameters, in the Deep Learning algorithm. Training so many parameters in Deep learning, takes more time. Machine Learning takes less time usually, a few minutes to hours. Almost two weeks of time is taken for training completely from basics, by State of art deep learning ResNet. Whereas by machine learning, training time is very less. When it comes to Testing time, a Deep learning algorithm takes less time. whereas, in some of the machine learning algorithms like k-nearest neighbors, test time raises with the increase in data size. Based on the requirement, we have to select which algorithm to use. This article gives the major differences, between Machine Learning and Deep Learning. Follow my articles to get more updates on Machine Learning and Deep Learning.
**What is Machine Learning?** Machine Learning is a subset of artificial intelligence that focuses primarily on machine learning based on your experience and making predictions based on your experience. It allows computers or machines to make...
In this TensorFlow 2.0 tutorial, you’ll understanding of how you can get started building machine learning models in Python with TensorFlow 2.0 as well as the other exciting available features!
Deep Learning vs. Machine Learning: You'll learn how the two concepts compare and how they fit into the broader category of Artificial Intelligence. During this demo we will also describes how deep learning can be applied to real-world scenarios such as fraud detection, voice and facial recognition, sentiment analytics, and time series forecasting.