Machine Learning Vs Deep Learning

To operate a machine like a human, the machine must learn to work, because the machine learning and deep learning techniques are used to help a machine solve a problem in real-time. They both have algorithms that work on these issues. In this article, I’m going to present you with a comprehensive study on Machine Learning Vs Deep Learning.

With the rapid growth of artificial intelligence, this industry needs speed and precision to achieve its goals. With machine learning and deep learning algorithms, the industry can meet its demands and these new techniques will provide the industry with a different way to solve problems.

Also, Read – 100+ Machine Learning Projects Solved and Explained.

What is Machine Learning?

Machine learning is a new way by which we can make machines learn to work, like making decisions, solving problems, solving problems in real-time. We see that machine learning works as a boost to artificial intelligence.

Machine learning is a way to apply artificial intelligence through machine learning algorithms to create an extraordinary machine for us. Machine learning has many algorithms and they are divided into three categories called supervised learning, unsupervised learning, and reinforcement learning.

Here are some of common machine learning algorithms:

1. Linear Regression
2. Logistic Regression
3. Decision Tree
4. Support Vector Machines
5. Naïve Bayes
6. K Nearest Neighbour
7. K-Means
8. Random Forest
9. Dimensionality Reduction
10. Gradient Boosting

What is Deep Learning?

Deep Learning is the whole new way to focus on the functioning of our brain and the human nervous system. Deep Learning closely observes the neural system of a human being. This helps to better understand the neural system and communication.

Through deep learning, we can learn to know how a normal human brain thinks and we can use it to map a new algorithm so that we can solve a problem via a machine like it was solved by a human brain.

Deep learning is persuaded by the biological process of the nervous system to think better and resolve better in a whole new way. It also focuses on how a brain recognizes, processes based on an image.

Deep Learning can also be seen as neural networks with multi-layered architectures and very important parameters on which it operates. Generally, deep learning is classified into four categories called unsupervised pre-trained network, convolutional neural network, recurrent neural network, and recursive neural network.

Some of the common methods by which Deep Learning is implemented are as follows:

1. Back Propagation
2. Batch Normalization
3. Dropout
4. Learning Rate Decay
5. Gradient Descent
6. Max Pooling
7. Long & Short Term Memory
8. Skip-gram
9. Transfer Learning
10. Continuous Bag of Words

Machine Learning Vs Deep Learning

Now let’s take a look at how machine learning and deep learning are different. In this section, I’ll walk you through Machine Learning Vs Deep Learning.

Machine Learning	Deep Learning
Machine learning is used to implement artificial intelligence. Machine learning works effectively when we have a moderate amount of sorted data that we train and get the desired results based on the problem we need to solve.	Deep Learning is also used to implement Artificial Intelligence techniques by neural networks. The basic principle is the neural network on which deep learning works. Deep learning takes longer to train the data but produces more improved and more precise output/results compared to machine learning.
Machine learning starts with matching a model while doing it, it splits a problem into subproblems because it is easy to get output from a short problem, and then at the end it cumulates the results to get the result final, in other words, we can say it uses the divide-n-conquer technique to analyze the data and derive results from it. How machine learning works doesn’t depend on the type of system or hardware we use, it works efficiently on every system.	Deep Learning works by creating clusters of similar data to get a result, then it accumulates the outputs of all the clusters and provides a better-improved result. Deep Learning needs advanced machines to operate efficiently and gives more precise results when we use a large amount of data to analyze results through deep learning.

Machine Learning Vs Deep Learning: Conclusion

Machine Learning and Deep Learning have been proven to amazingly solve problems so they have their future. And there are many more researchers around the world trying to explore these two learning techniques in their very essence.

These learning techniques will be used in the future to properly analyze the problem and get the result accordingly. Because these techniques will give a bright future to artificial intelligence as well as to neurosciences.

As we conclude, we get to know that both are equally important in the implementation of artificial intelligence. Deep Learning is therefore a subset of machine learning which is also a subset of artificial intelligence. This article on Machine Learning Vs Deep Learning gives us a clear view of Machine Learning and Deep Learning.

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