Deep Learning With Keras And Tensorflow In Python And R
Simply the best place to start learning Deep Learning with Keras and Tensorflow in Python and R using Udemy
Understand Deep Learning and build Neural Networks using Keras and TensorFlow 2.0 in Python and R
What you’ll learn
- Get a solid understanding of Artificial Neural Networks (ANN) and Deep Learning
- Learn usage of Keras and Tensorflow libraries
- Understand the business scenarios where Artificial Neural Networks (ANN) is applicable
- Building a Artificial Neural Networks (ANN) in Python and R
- Use Artificial Neural Networks (ANN) to make predictions
- Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same. Students will need to install R, Python and Anaconda software but we have a separate lecture to help you install the same
You’re looking for a complete Course on Deep Learning using Keras and Tensorflow that teaches you everything you need to create a Neural Network model in Python and R, right?
You’ve found the right Neural Networks course!
After completing this course you will be able to:
- Identify the business problem which can be solved using Neural network Models.
- Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.
- Create Neural network models in Python and R using Keras and Tensorflow libraries and analyze their results.
- Confidently practice, discuss and understand Deep Learning concepts
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.
If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.
Why should you choose this course?
This course covers all the steps that one should take to create a predictive model using Neural Networks.
Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Practice test, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.
What is covered in this course?
This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.
Below are the course contents of this course on ANN:
- Part 1 – Python and R basics This part gets you started with Python.This part will help you set up the python and Jupyter environment on your system and it’ll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.
- Part 2 – Theoretical Concepts This part will give you a solid understanding of concepts involved in Neural Networks.In this section you will learn about the single cells or Perceptions and how Perceptions are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.
- Part 3 – Creating Regression and Classification ANN model in Python and R In this part you will learn how to create ANN models in Python.We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.We also understand the importance of libraries such as Keras and TensorFlow in this part.
- Part 4 – Data Preprocessing In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split.
Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, its nice to know that employment opportunities are abundant (and growing) as well.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge and further automatically applies that information to data, decision-making, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
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