Tensorflow and Keras For Neural Networks and Deep Learning

Tensorflow and Keras For Neural Networks and Deep Learning, available at $74.99, has an average rating of 4.1, with 80 lectures, based on 469 reviews, and has 11067 subscribers.

You will learn about Harness The Power Of Anaconda/iPython For Practical Data Science Learn How To Install & Use Tensorflow Within Anaconda Implement Statistical & Machine Learning With Tensorflow Implement Neural Network Modelling With Tensorflow & Keras Implement Deep Learning Based Unsupervised Learning With Tensorflow and Keras Implement Deep Learning Based Supervised Learning With Tensorflow & Keras Implement Convolution Neural Networks With Tensorflow & Keras This course is ideal for individuals who are People Interested In Learning Python Based Tensorflow and Keras For Data Science Applications or People With Prior Exposure To Python Programming &/Or Data Science Concepts or People Interested In Implementing Neural Networks & Deep Learning Models With Tensorflow or People Interested In Implementing Neural Networks & Deep Learning Models With Keras It is particularly useful for People Interested In Learning Python Based Tensorflow and Keras For Data Science Applications or People With Prior Exposure To Python Programming &/Or Data Science Concepts or People Interested In Implementing Neural Networks & Deep Learning Models With Tensorflow or People Interested In Implementing Neural Networks & Deep Learning Models With Keras.

Enroll now: Tensorflow and Keras For Neural Networks and Deep Learning

Summary

Title: Tensorflow and Keras For Neural Networks and Deep Learning

Price: $74.99

Average Rating: 4.1

Number of Lectures: 80

Number of Published Lectures: 80

Number of Curriculum Items: 80

Number of Published Curriculum Objects: 80

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Harness The Power Of Anaconda/iPython For Practical Data Science
Learn How To Install & Use Tensorflow Within Anaconda
Implement Statistical & Machine Learning With Tensorflow
Implement Neural Network Modelling With Tensorflow & Keras
Implement Deep Learning Based Unsupervised Learning With Tensorflow and Keras
Implement Deep Learning Based Supervised Learning With Tensorflow & Keras
Implement Convolution Neural Networks With Tensorflow & Keras

Who Should Attend

People Interested In Learning Python Based Tensorflow and Keras For Data Science Applications
People With Prior Exposure To Python Programming &/Or Data Science Concepts
People Interested In Implementing Neural Networks & Deep Learning Models With Tensorflow
People Interested In Implementing Neural Networks & Deep Learning Models With Keras

Target Audiences

People Interested In Learning Python Based Tensorflow and Keras For Data Science Applications
People With Prior Exposure To Python Programming &/Or Data Science Concepts
People Interested In Implementing Neural Networks & Deep Learning Models With Tensorflow
People Interested In Implementing Neural Networks & Deep Learning Models With Keras

THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH TENSORFLOW & KERAS IN PYTHON!

It is a full 7-Hour Python Tensorflow & Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using two of the most important Deep Learning frameworks- Tensorflow and Keras. �

HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:

This course is your complete guide to practical machine & deep learning using the Tensorflow & Keras framework in Python..

This means, this course coversthe important aspects of Keras and Tensorflow (Google’s powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Tensorflow and Keras based data science. �

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of Tensorflow and Keras is revolutionizing Deep Learning…

By gaining proficiency in Keras and and Tensorflow, you can give your company a competitive edge and boost your career to the next level.

THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL KERAS & TENSORFLOW BASED DATA SCIENCE!

But first things first. My name is�Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.

Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning..

This gives students an incomplete knowledge of the subject. My course, on the other hand, will give you a robust grounding in all aspects of data science within the Tensorflow framework.

Unlike other Python courses, we dig deep into the statistical modeling features of Tensorflow & Keras and give you a one-of-a-kind grounding in these frameworks!

DISCOVER 8 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON BASED TENSORFLOW DATA SCIENCE:

• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about Tensorflow & Keras installation and a brief introduction to the other Python data science packages
• Brief introduction to the working of Pandas and Numpy
• The basics of the Tensorflow syntax and graphing environment
• The basics of the Keras syntax
• Machine Learning, Supervised Learning, Unsupervised Learning in the Tensorflow & Keras frameworks
• You’ll even discover how to create artificial neural networks and deep learning structures with Tensorflow & Keras

BUT, WAIT! THIS ISN’T JUST ANY OTHER DATA SCIENCE COURSE:

You’ll start by absorbing the most valuable Python Tensorflow and Keras basics and techniques.

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts.

My course will help youimplement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real -life.

After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python along with gaining fluency in Tensorflow and Keras. I will even introduce you to deep learning models such as Convolution Neural network (CNN) !!

The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today, start analyzing data for your own projects whatever your skill level, and impress your potential employers with actual examples of your data science abilities.

This course will take students without a prior Python and/or statistics background background from a basic level to performing some of the most common advanced data science techniques using the powerful Python based Jupyter notebooks

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results..

After each video you will learn a new concept or technique which you may apply to your own projects!

JOIN THE COURSE NOW!

Course Curriculum

Chapter 1: INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools

Lecture 1: Introduction to the Course

Lecture 2: Data and Scripts For the Course

Lecture 3: Python Data Science Environment

Lecture 4: For Mac Users

Lecture 5: Introduction to IPython

Lecture 6: Install Tensorflow

Lecture 7: Written Tensorflow Installation Instructions

Lecture 8: Install Keras on Windows 10

Lecture 9: Install Keras on Mac

Lecture 10: Written Keras Installation Instructions

Chapter 2: Introduction to Python Data Science Packages

Lecture 1: Python Packages for Data Science

Lecture 2: Introduction to Numpy

Lecture 3: Create Numpy Arrays

Lecture 4: Numpy Operations

Lecture 5: Numpy for Statistical Operation

Lecture 6: Introduction to Pandas

Lecture 7: Read in Data from CSV

Lecture 8: Read in Data from Excel

Lecture 9: Basic Data Cleaning

Chapter 3: Introduction to TensorFlow

Lecture 1: A Brief Touchdown

Lecture 2: A Brief Touchdown: Computational Graphs

Lecture 3: Common Mathematical Operators in Tensorflow

Lecture 4: A Tensorflow Session

Lecture 5: Interactive Tensorflow Session

Lecture 6: Constants and Variables in Tensorflow

Lecture 7: Placeholders in Tensorflow

Chapter 4: Introduction to Keras

Lecture 1: What is Keras

Chapter 5: Some Preliminary Tensorflow and Keras Applications

Lecture 1: Theory of Linear Regression (OLS)

Lecture 2: OLS From First Principles

Lecture 3: Visualize the Results of OLS

Lecture 4: Multiple Regression With Tensorflow-Part 1

Lecture 5: Estimate With Tensorflow Estimators

Lecture 6: Multiple Regression With Tensorflow Estimators

Lecture 7: More on Linear Regressor Estimator

Lecture 8: GLM: Generalized Linear Model

Lecture 9: Linear Classifier For Binary Classification

Lecture 10: Accuracy Assessment For Binary Classification

Lecture 11: Linear Classification with Binary Classification With Mixed Predictors

Lecture 12: Softmax Classification With Tensorflow

Chapter 6: Some Basic Concepts

Lecture 1: What is Machine Learning?

Lecture 2: Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks)

Chapter 7: Unsupervised Learning With Tensorflow and Keras

Lecture 1: What is Unsupervised Learning?

Lecture 2: Autoencoders for Unsupervised Classification

Lecture 3: Autoencoders in Tensorflow (Binary Class Problem)

Lecture 4: Autoencoders in Tensorflow (Multiple Classes)

Lecture 5: Autoencoders in Keras (Sparsity Constraints)

Lecture 6: Autoencoders in Keras (Simple)

Lecture 7: Deep Autoencoder With Keras

Chapter 8: Neural Network for Tensorflow & Keras

Lecture 1: Multi Layer Perceptron (MLP) with Tensorflow

Lecture 2: Multi Layer Perceptron (MLP) With Keras

Lecture 3: Keras MLP For Binary Classification

Lecture 4: Keras MLP for Multiclass Classification

Lecture 5: Keras MLP for Regression

Chapter 9: Deep Learning For Tensorflow & Keras

Lecture 1: What is Artificial Intelligence?

Lecture 2: Deep Neural Network (DNN) Classifier With Tensorflow

Lecture 3: Deep Neural Network (DNN) Classifier With Mixed Predictors

Lecture 4: Deep Neural Network (DNN) Regression With Tensorflow

Lecture 5: Wide & Deep Learning (Tensorflow)

Lecture 6: DNN Classifier With Keras

Lecture 7: DNN Classifier With Keras-Example 2

Chapter 10: Convolution Neural Network (CNN) For Image Analysis

Lecture 1: Introduction to CNN

Lecture 2: Implement a CNN for Multi-Class Supervised Classification

Lecture 3: Activation Functions

Lecture 4: More on CNN

Lecture 5: Pre-Requisite For Working With Imagery Data

Lecture 6: CNN on Image Data-Part 1

Lecture 7: CNN on Image Data-Part 2

Lecture 8: More on TFLearn

Lecture 9: CNN Workflow for Keras

Lecture 10: CNN With Keras

Lecture 11: CNN on Image Data with Keras-Part 1

Lecture 12: CNN on Image Data with Keras-Part 2

Chapter 11: Autoencoders With Convolution Neural Networks (CNN)

Lecture 1: Autoencoders for With CNN- Tensorflow

Lecture 2: Autoencoders for With CNN- Keras

Chapter 12: Recurrent Neural Networks (RNN)

Lecture 1: Theory Behind RNNs

Lecture 2: LSTM For Time Series Data

Lecture 3: LSTM for Predicting Stock Prices

Chapter 13: Miscellaneous Section

Lecture 1: Use Colabs for Jupyter Data Science

Lecture 2: Github

Lecture 3: What Is Data Science?

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 32 votes
2 stars: 21 votes
3 stars: 64 votes
4 stars: 94 votes
5 stars: 258 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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