• India Flag
    Call Us:

    +91 769-409-5404

  • USA Flag
    Call Us:

    +1 786-629-6893

About Course:

tensorflow

This Training helps you in learning with tensors, install TensorFlow, simple statistics and plotting, architecture and Integration of TensorFlow with different open-source frameworks. You will hands-on sessions on a computation using data flow graphs, Loading And Exploring The Data, Visualizing Traffic Sign Statistics, R Interface to TensorFlow, Feature Extraction and Modeling the Neural Network...etc


Duration : 20 hours

Fee: 338


Job Trends

Training Calender

Date Time Type Attend
No Schedule Available
Contact Us

+91 769-409-5404

Includes
  • 24 hours on-demand video
  • Articles
  • Coding Exercises
  • Full lifetime access
  • Certificate of Completion

Module of Training

LIVE ONLINE TRAINING


Live presentation of theory, demonstration of tool, features & tasks

We are connecting Online via Goto Meeting

Get practice environment for practical & hands-on Training curriculum has been designed by real-time industry professionals & real-time scenarios training pattern

CORPORATE TRAINING


Learn as per day-wise & customized schedule with discussions & lab exercises included

One to one or Batch-wise interactive demonstration of a tool, features and 100% practical classes

World-class learning material & case studies for the course

Completely customizable course content & schedule as per convenience

Certification guidance provided if necessary

Curriculum

TensorFlow Download  

About TensorFlow
TensorFlow is a powerful high-performance numerical computation open source software library collection. No Matter what platforms (CPUs, GPUs, TPUs) you have, its flexible architecture allows easy deployment of computation across a variety of platform including desktops, clusters of servers or mobile devices. It includes machine learning and deep learning with flexible numerical computation core. With BISP you gain 100% real-life examples of TensorFlow and learn how to solve real-life problems. Our trainer and technical support team ensure your queries are addressed in a timely manner.

Course description
This course focuses on deep learning with the best approach to building artificial intelligence algorithms. Starting from basics components of deep learning (what it means, how it works) to advance code develop development necessary to build various algorithms such as deep convolutional networks, variational autoencoders, generative adversarial networks, and recurrent neural networks. You will learn how to apply these algorithms for exploring creative applications and solving complex business scenarios. The training is all about training a computer to recognize objects in an image and use this knowledge to drive new and interesting behaviors, from understanding the similarities and differences in large datasets and using them to self-organize, to understanding how to infinitely generate entirely new content or match the aesthetics or contents of another image. Our practical approach applications along with guided homework assignments, you'll be expected to create datasets, develop and train neural networks, explore your own media collections, synthesize new content from generative algorithms, and understand deep learning's potential for creating entirely new aesthetics and new ways of interacting with large amounts of data.


TensorFlow Online Training curriculum


Introduction to Deep Learning

  • What is Deep Learning?
  • Limitations of Machine Learning
  • The core idea behind Deep Learning
  • Advantage of Deep Learning over Machine learning
  • Real-Life use cases of Deep Learning
  • Applications of Deep Learning
  • Getting Started with TensorFlow

What is TensorFlow?

  • TensorFlow code-basics
  • Hello World with TensorFlow
  • Linear Regression
  • Nonlinear Regression
  • Logistic Regression
  • Activation Functions

Basics of Defining Neural Networks

  • Graph Visualization
  • Constants, Placeholders, Variables
  • Creating a Model
  • Step by Step - Use-Case Implementation
  • The Biological Neuron
  • The Preceptor
  • Multi-Layer Feed-Forward Networks
  • Training Neural Networks
  • Back propagation Learning
  • Gradient Descent
  • Stochastic Gradient Descent
  • Quasi-Newton Optimization Methods
  • Generative vs Discriminative Models
  • Loss Functions
  • Loss Function Notation
  • Loss Functions for Regression
  • Loss Functions for Classification
  • Loss Functions for Reconstruction
  • Hyper parameters
  • Learning Rate
  • Regularization
  • Momentum
  • Sparsity

Convolution Neural Networks (CNN)

  • Main concepts of CNN's
  • CNN's in action
  • LeNet5
  • Implementing a LeNet-5 step by step
  • Dataset preparation
  • Fine-tuning implementation
  • Inception-v3
  • Emotion recognition with CNN's

Optimizing TensorFlow Auto-encoders

  • How does an auto-encoder work?
  • Implementing auto-encoders with TensorFlow
  • Improving auto-encoder robustness
  • Fraud analytics with auto-encoders

Recurrent Neural Networks

  • Working principles of RNNs
  • RNN and the gradient vanishing-exploding problem
  • Implementing an RNN for spam prediction
  • Developing a predictive model for time series data
  • An LSTM predictive model for sentiment analysis
  • Human activity recognition using LSTM model

Heterogeneous and Distributed Computing

  • GPGPU computing
  • The TensorFlow GPU setup
  • Distributed computing
  • The distributed TensorFlow setup

Advanced TensorFlow Programming

  • tf.estimator
  • TF Learn
  • Pretty Tensor
  • Keras

Recommendation Systems Using Factorization Machines

  • Recommendation systems
  • Movie recommendation using collaborative filtering
  • Factorization machines for recommendation systems
  • Improved factorization machines

Reinforcement Learning

  • The RL problem
  • Open AI Gym
  • The Q-Learning algorithm
  • Deep Q-learning

Delivery Methodology

  • We are using an experiential delivering methodology that blends theoretical concepts with hands-on practical learning to ensure a holistic understanding of the subject or course

Class Delivery

  • Live Interactive classes with expert

Who should Learn TensorFlow?

Python developers eager to learn the latest Deep Learning Techniques with TensorFlow

Prerequisite to learn TensorFlow

Mastery of intro-level algebra. You should be comfortable with variables and coefficients, linear equations, graphs of functions, and histograms. ...

Proficiency in programming basics, and some experience coding in Python. 

Delivery Methodology used to deliver the TensorFlow

We are using an experiential delivering methodology that blends theoretical concepts with hands-on practical learning to ensure a holistic understanding of the subject or course.

Class delivery

Live Interactive classes with expert

TensorFlow FAQ

Question: Can I attend the Demo session before enrollment?

Answer: Yes, you may attend the Demo class before enrollment for training Quality Evaluation. You can also Interact with a trainer as one to one session for a specific requirement or discussion

Question: Can you schedule the training based upon my availability?

Answer: Yes, we need to discuss it with a trainer, accordingly, we can schedule training at a convenient time.

Question: How I can pay for the course?

Answer: You can pay the fee or enroll yourself via payment gateway through the course page, make an online payment using various options.

Question: What if I missed any class?

Answer: BISP has a missing class policy. If you missed any session, we will be sharing a recorded session. However, you may retake whole training multiple times within 6 month period but the trainer is the same.

Question: Is there any live project training along with regular training?

Answer: Our training curriculum includes real-time scenarios and lives project working module & the trainer explains every topic with examples. If you have any issue or you are stuck in any scenario the trainer will explain end-to-end.

Question: What about certification preparation and guidance?

Answer: BISP technical faculty assist & guide you completely for certification and preparation. We ensure you will get certified easily after our training.

Question: Who is the trainer & about his experience?

Answer: All our trainers are working professionals and industry experts with at least 10-12 years of relevant teaching experience. Each of them has gone through a rigorous selection process which includes profile screening, technical evaluation, and a training demo We also ensure that only those trainers with a high alumni rating continue to train for us.

Question: Do you provide Job support?

Answer: Yes, we provide Job support services, but the cost structure is different and fixed. For more details: Just give us a call at: +91 769-409-5404 & +1 786-629-6893 You can also write to us: support@bisptrainings.com

Question: What if I have more queries and doubts?

Answer: Just give us call at : +91 769-409-5404 & +1 786-629-6893 You can also write to us: support@bisptrainings.com

Question: How Fee Refund Policy works?

Answer: Please refer the link for refund policy: https://www.bisptrainings.com/Refund-Policy

Case Study and Learning Pdf's

Certificate

Certificate123
Benefits of Certificate

Certification demonstrates your dedication, motivation and technical knowledge on a specific platform. Having a certification shows that you not only possess comprehensive knowledge of that technology but you also care enough about your own career to spend the time and money to get the certification.

We are welcoming our Students or professionals to participate in our professional online courses. We are offering great variety of online training programs and professional courses that you can always find as desired. After the completion of training program they will receive a certificate from BISP. As a Certified professional you can apply that knowledge in your future profession and enjoy with better salaries & career prospects.

Signup for free newsletter and
business tips

Any Questions?
Talk to our Course Co-ordinator

+91 769-409-5404
Want to see a live demo? We'll be in touch within 24 hours?