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Deep Learning And Reinforcement Learning With Tensorflow




Data Science and Tensor Flow Certification Course


Working Professionals and Freshers


Regular Offline and Online Live Training


Week Days and Week Ends

Duration :

2 Months

Data Science and Tensor Flow Objectives

•How to create a Data Science and Tensor Flow Project.
•Learn to manage application state with Data Science and Tensor Flow.
•Improve your front end design and development skills
•How to create your own Data Science and Tensor Flow components from scratch.
•Learn and Master Data Science and Tensor Flow with this time saving course
•Learn the Data Science and Tensor Flow fundamentals you’ll be using day-in and day-outAn easy way to learn one of the widely used Data Science and Tensor Flow
•How to handle different types of data inside a workflow using Data Science and Tensor Flow.
•Learn Data Science and Tensor Flow from basic to advanced with examples and interactive sessions at peak.

deep learning and reinforcement learning with tensorflow Training Highlights

•We are Known for High-Quality Training
•Training by Industry expert professionals
•Get Certified at the Best Training Institute.
•Regular Brush-up Sessions of the previous classes
•We Also provide Case studies for Online Training Courses
•Courseware that is curated to meet the global requirements
•Curriculum based on course outlines defined by in-demand skills in Python.
•This Instructor-led classroom course is designed with an aim to build theoretical knowledge supplemented by ample hands-on lab exercises

Who are eligible for Data Science and Tensor Flow

•Application Server, Problem Mgmt, SAP Technical/Functional, BO Developer, Automotive Developer, Protocols, Embedded C, AutoSar, Window Applications
•embedded platform software engineers, embedded multimedia developer, Middleware Developers, Android Middleware, device driver developers, c, c++, linux
•Java, Scrum Master, Agile, C#, It, Al, Big Data, Hadoop, .Net, Non It, It Recruitment, Ios, Android, React, Web Designing, Selenium, Testing, Qa, Cloud
•OBIEE, OBIA, ODI, PHP, QA, Oracle Apps DBA, SQL Sever DBA, Dot Net Developer, Automation Testing, Informatica Developer, UI Designer, Agile PLM
•Software Development, Senior Software Developer, Mean Stack, React.js, Mern Stack, Full Stack, Sql, Spark, Scala, Python, Ui Development


•Handson Deep Learning with TensorFlow
•The Course Overview
•TensorFlow for Building Deep Learning Models
•Basic Syntaxes Function Optimization Variables and Placeholders
•TensorBoard for Visualization
•Start by Loading the Imported Dataset
•Building the Layers of the Neural Network in TensorFlow
•Optimizing the Softmax Cross Entropy Function
•Using DNN Predicting Whether Breast Cancer Cells Are Benign or Not
•Importing the Two Datasets Using TensorFlow and Sklearn API
•Writing the TensorFlow Code to Add Convolutional and Pooling Layers
•Using tftrainAdamOptimizer API to Optimize CNN
•Implementing CNN to Create a Face Recognition System
•Understanding the RNN and the Need for LSTM
•Implementing RNN
•Monthly Riverflow Prediction of Turtle River in Ontario
•Implement LSTM Project to Predict Decimal Number of Given Binary Representation
•Encoder and Decoder for Efficient Data Representation
•TensorFlow Code Using Linear Autoencoder to Perform PCA on a D Dataset
•Using Stacked Autoencoders for Representation on MNIST Dataset
•Build a Deep Autoencoder to Reduce Latent Space of LFW Face Dataset
•Generator and Discriminator the Basics of GAN
•Downloading and Setting Up the Microsoft Research Asia Geolife Project Dataset
•Coding the Generator and Discriminator Using TensorFlow
•Training GANs to Create Synthetic GPS Based Trajectories
•Test Your Knowledge
•Handson Reinforcement Learning with TensorFlow
•Introduction to Reinforcement Learning
•Common RL Tasks and the Reinforcement Process
•Setting Up Environments Using Open AIs Gym Framework
•The Taxiv Environment
•Operating Taxiv Using a Dumb Agent
•Introducing Reinforcement QLearning
•Implementing QLearning
•QLearning Agent in Action
•The Cartpole Environment
•Introducing QNetworks
•TensorFlow Basics
•Implementing QNetwork
•QNetwork Agent in Action
•Introducing Deep QNetworks
•The DQN Training Algorithm
•Implementing DQN
•DQN in Action
•Dueling Double DQN
•Logging Saving and Visualizing
•Structuring the Code Base
•Debugging and Some Nice Practices in TensorFlow
•TensorFlow on Multiple Devices