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Tensorflow And Keras For Neural Networks And Deep Learning

Course

TENSORFLOW AND KERAS FOR NEURAL NETWORKS AND DEEP LEARNING

Category

Data Science and Tensor Flow Online Courses

Eligibility

Working Professionals and Freshers

Mode

Regular Offline and Online Live Training

Batches

Week Days and Week Ends

Duration :

30 to 45 days

Data Science and Tensor Flow What will you learn?

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tensorflow and keras for neural networks and deep learning Training Features

•Most comprehensive Industrry curriculum
•Resume & Interviews Preparation Support
•Flexible batch timings – Weekend & weekdays.
•Best Opportunity To Both Learn And Work From Home
•Assignments and test to ensure concept absorption.
•Repeating of lectures allowed (based on seat availability)
•Training time :  Week Day / Week End – Any Day Any Time – Students can come and study
• Our dedicated HR department will help you search jobs as per your module & skill set, thus, drastically reducing the job search time

Who are eligible for Data Science and Tensor Flow

•CNC Engineer, Software Developer, Testing Engineer, Implementation, Core Java, Struts, hibernate, Asp.net, c#, SQL Server, CNC Programming, backDevelopers, Architect, Business Analyst, Analytics, Core Java, Android, Android Sdk, Javascript, Front End, Angular Js, Html, Css, Software Engineering
•Java/J2EE, .Net C#, Networking, Oracle DBA, Embedded Developers, HTML5, Android Framework, Android Developers, MSTR Developer, Cognos, SAN, Windows Admin
•Object Oriented Programming, Cloud Computing, Java, Testing, Web Designing, Design, Front End, Javascript, It Infrastructure, Software Development, Support
•Software Development, Software Testing, Solution Design, software, Blueprism Developer, Rpa Developer

TENSORFLOW AND KERAS FOR NEURAL NETWORKS AND DEEP LEARNING Syllabus

Data and Scripts For the Course
•Python Data Science Environment
•For Mac Users
•Written Tensorflow Installation Instructions
•Install Keras on Windows 10
•Install Keras on Mac
•Written Keras Installation Instructions
•Python Packages for Data Science
•Create Numpy Arrays
•Numpy Operations
•Numpy for Statistical Operation
•Read in Data from CSV
•Read in Data from Excel
•Basic Data Cleaning
•A Brief Touchdown
•A Brief Touchdown: Computational Graphs
•Common Mathematical Operators in Tensorflow
•A Tensorflow Session
•Interactive Tensorflow Session
•Constants and Variables in Tensorflow
•Placeholders in Tensorflow
•What is Keras
•Some Preliminary Tensorflow and Keras Applications
•Theory of Linear Regression (OLS)
•OLS From First Principles
•Visualize the Results of OLS
•Multiple Regression With Tensorflow-
•Estimate With Tensorflow Estimators
•Multiple Regression With Tensorflow Estimators
•More on Linear Regressor Estimator
•GLM: Generalized Linear Model
•Linear Classifier For Binary Classification
•Accuracy Assessment For Binary Classification
•Linear Classification with Binary Classification With Mixed Predictors
•Softmax Classification With Tensorflow
•Some Basic Concepts
•What is Machine Learning?
•Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks)
•Unsupervised Learning With Tensorflow and Keras
•What is Unsupervised Learning?
•Autoencoders for Unsupervised Classification
•Autoencoders in Tensorflow (Binary Class Problem)
•Autoencoders in Tensorflow (Multiple Classes)
•Autoencoders in Keras (Sparsity Constraints)
•Autoencoders in Keras (Simple)
•Deep Autoencoder With Keras
•Neural Network for Tensorflow & Keras
•Multi Layer Perceptron (MLP) with Tensorflow
•Multi Layer Perceptron (MLP) With Keras
•Keras MLP For Binary Classification
•Keras MLP for Multiclass Classification
•Keras MLP for Regression
•Deep Learning For Tensorflow & Keras
•What is Artificial Intelligence?
•Deep Neural Network (DNN) Classifier With Tensorflow
•Deep Neural Network (DNN) Classifier With Mixed Predictors
•Deep Neural Network (DNN) Regression With Tensorflow
•Wide & Deep Learning (Tensorflow)
•DNN Classifier With Keras
•DNN Classifier With Keras-
•Convolution Neural Network (CNN) For Image Analysis
•Implement a CNN for Multi-Class Supervised Classification
•Activation Functions
•More on CNN
•Pre-Requisite For Working With Imagery Data
•CNN on Image Data-
•More on TFLearn
•CNN Workflow for Keras
•CNN With Keras
•CNN on Image Data with Keras-
•Autoencoders With Convolution Neural Networks (CNN)
•Autoencoders for With CNN- Tensorflow
•Autoencoders for With CNN- Keras
•Recurrent Neural Networks (RNN)
•Theory Behind RNNs
•LSTM For Time Series Data
•LSTM for Predicting Stock Prices
•Miscellaneous Section
•Use Colabs for Jupyter Data Science