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
So lets explore further what exactly is ” Deep Learning” and how is it different from Machine Learning.
Difference between Machine Learning and Deep 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.
How does Deep Learning Models Work?
Deep Learning models are based on the functioning of Human Brains. They consist of
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
So deep learning models do perform well and without guidance and hence can be very valuable
So both Deep Learning and Machine Learning techniques have their own advantages and if you k
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.
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