Difference between Machine Learning and Deep Learning
It is well acknowledged today that artificial intelligence has a significant value in today’s world. It is also expected to evolve sharply further in future. Artificial Intelligence means making the machines decide and act like Human Intelligence. Artificial Intelligence is a broad concept and one of its important component is Machine Learning. In recent times the term “Deep Learning” is also being used popularly and sometimes interchangeably with Machine Learning. The new trends in AI training are deep learning courses and they are being considered effective solution to learning and developing AI skills and expertise.
So lets explore further what exactly is “Deep Learning” and how is it different from Machine Learning.
What is Machine Learning?
Machine Learning as the name suggests is the way to make machines learn. Basically machines are trained to perform a function based on the data provided. Furthermore they are also trained to keep getting better progressively with numerous iteration. After all Practice makes one perfect and that’s true for Machines as well.
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How does Deep Learning Models Work?
Deep Learning models are based on the functioning of Human Brains. They consist of artificial neural network (ANN) which are fashioned on the biological neural network of the human brain. ANN is a complex structure of layered alogrithms which analyzes data in step wise logic structure and patterns similar to how a human brain analyzes patterns and makes conclusion. However the scope of “Human Error” in analysis becomes minimal in case of Deep Learning and hence its performance can even surpass human logic and capability boundaries.
For Example: if you wish to classify pictures of dogs and cats – Machine learning algorithm can do that quickly and accurately once you have provided some initial features related to Dogs and Cats to the machine , while DL can automatically discover the features that can classify Dogs from Cats and classify all pictures based on its own derived classification. You will then just need to only name the two category of animals as Dogs and Cats (or whatever you fancy).
So deep learning models do perform well and without guidance and hence can be very valuable.However they need high-end algorithms and significantly high volume of data to provide accurate results.
So both Deep Learning and Machine Learning techniques have their own advantages and if you know them well, you can be utilized in best possible ways for different requirements.
For example: for small and simple data analysis you may not need Deep Learning Techniques. Machine Learning algorithms will suffice for most simple business applications.
However for AI enthusiasts who wish to develop solutions and innovations based on Artificial Intelligence to handle routine tasks, training in deep Learning can be very beneficial.
Deep Learning can automatically identify differentiating features and patterns!
After all for slicing vegetables – you do not need to use a sword. A knife will do well and indeed be more effective.
Following are the popular topics covered in Deep Learning Training
To pursue deep learning and artificial intelligence training, one needs to first understand and be familiar with the machine learning techniques before going for deep learning courses. There are some reliable institutes you can contact which offer AI courses and certification to help you archive a better and successful profession or career
- Introduction to Neural Networks
- Perception and Activation Functions
- Cost Functions
- Gradient Descent Back propagation
- Boltzmann Machine Intuition
- Tensor Flows & Theano Implementation
- Convolution Neural Networks
- Recurrent Neural Networks
- Artificial Intelligence: Reinforcement Learning
- Vibrational Autoencoders
- GANS – short for – Generative Adversarial Networks
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