- Get a strong understanding of Artificial Neural Networks (ANN) and Deep Learning
- Learn utilization of Keras and Tensorflow libraries
- Understand the enterprise eventualities the place Artificial Neural Networks (ANN) is relevant
- Building a Artificial Neural Networks (ANN) in Python and R
- Use Artificial Neural Networks (ANN) to make predictions
What is roofed in this course?
This course teaches you all of the steps of making a Neural community based mostly mannequin i.e. a Deep Learning mannequin, to resolve enterprise issues.
Below are the course contents of this course on ANN:
Part 1 – Python and R fundamentals
This half will get you began with Python.
This half will show you how to arrange the python and Jupyter surroundings in your system and it will train you the right way to carry out some fundamental operations in Python. We will perceive the significance of various libraries resembling Numpy, Pandas & Seaborn.
Part 2 – Theoretical Concepts
This half gives you a strong understanding of ideas concerned in Neural Networks.
In this part you’ll be taught in regards to the single cells or Perceptrons and how Perceptrons are stacked to create a community structure. Once structure is ready, we perceive the Gradient descent algorithm to seek out the minima of a perform and learn the way that is used to optimize our community mannequin.
Part 3 – Creating Regression and Classification ANN mannequin in Python and R
In this half you’ll learn to create ANN fashions in Python.
We will begin this part by creating an ANN mannequin utilizing Sequential API to resolve a classification downside. We learn to outline community structure, configure the mannequin and practice the mannequin. Then we consider the efficiency of our educated mannequin and use it to foretell on new information. We additionally clear up a regression downside in which we attempt to predict home costs in a location. We can even cowl the right way to create advanced ANN architectures utilizing useful API. Lastly we learn to save and restore fashions.
We additionally perceive the significance of libraries resembling Keras and TensorFlow in this half.
Part 4 – Data Preprocessing
In this half you’ll be taught what actions you want to take to arrange Data for the evaluation, these steps are crucial for making a significant.
In this part, we are going to begin with the fundamental concept of resolution tree then we cowl information pre-processing matters like lacking worth imputation, variable transformation and Test-Train break up.
By the tip of this course, your confidence in making a Neural Network mannequin in Python will soar. You’ll have a radical understanding of the right way to use ANN to create predictive fashions and clear up enterprise issues.
Go forward and click on the enroll button, and I’ll see you in lesson 1!
Below are some standard FAQs of scholars who wish to begin their Deep studying journey-
Why use Python for Deep Learning?
Understanding Python is among the beneficial expertise wanted for a profession in Deep Learning.
Though it hasn’t all the time been, Python is the programming language of selection for information science. Here’s a quick historical past:
In 2016, it overtook R on Kaggle, the premier platform for information science competitions.
In 2017, it overtook R on KDNuggets’s annual ballot of information scientists’ most used instruments.
In 2018, 66% of information scientists reported utilizing Python day by day, making it the primary instrument for analytics professionals.
Deep Learning specialists anticipate this development to proceed with growing growth in the Python ecosystem. And whereas your journey to be taught Python programming could also be simply starting, it’s good to know that employment alternatives are ample (and rising) as effectively.
What is the distinction between Data Mining, Machine Learning, and Deep Learning?
Put merely, machine studying and information mining use the identical algorithms and strategies as information mining, besides the sorts of predictions fluctuate. While information mining discovers beforehand unknown patterns and data, machine studying reproduces recognized patterns and data—and additional mechanically applies that data to information, decision-making, and actions.
Deep studying, then again, makes use of superior computing energy and particular sorts of neural networks and applies them to massive quantities of information to be taught, perceive, and determine sophisticated patterns. Automatic language translation and medical diagnoses are examples of deep studying.