AI & Deep Learning with Tensor flow

AI & Deep Learning with Tensor flow online training course teaches you applied machine learning skills with TensorFlow so you can build and train powerful models.

  • 40000
  • 45000
  • Course Includes
  • Live Class Practical Oriented Training
  • 60 + Hrs Instructor LED Training
  • 45 + Hrs Practical Exercise
  • 25 + Hrs Project Work & Assignment
  • Timely Doubt Resolution
  • Dedicated Student Success Mentor
  • Certification & Job Assistance
  • Free Access to Workshop & Webinar
  • No Cost EMI Option


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What you will learn

  • Best practices for TensorFlow, a popular open-source machine learning framework to train a neural network for a computer...
  • Build natural language processing systems using TensorFlow.
  • Understand the Autoencoders and varitional Autoencoders. Learn to apply the Analytical mathematics to the data. Understa...
  • Learn about Autoencoders & discuss their Applications. Learn about the application of Convolutional Neural Networks. Dev...
  • Learn how to run a “Hello World” program in TensorFlow. Describe Deep Learning. Learn about TFlearn implementation
  • Learn the implementation procedure of Collaborative Filtering with RBM Understand what Restricted Boltzmann Machine is?...

Requirements

  • Basic mathematical Knowledge is required for this training.

Description

|| About AI & Deep Learning with Tensor flow Course

AI & Deep learning with Tensorflow Online Training course aims to impart training on the essentials of the Tensorflow and throws light on the aspects such as: main functions, operations and the execution pipeline. The candidates will gain complete understanding on the types of the Deep Architectures, such as Convolutional Networks and Recurrent Networks. The candidates will get to learn about the deep neural networks and its uses in complex raw data using TensorFlow.

 

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. AI & Deep Learning with Tensor flow teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems.

Course Content

Live Lecture

·       Deep Learning: A revolution in Artificial Intelligence

·       Limitations of Machine Learning

·       What is Deep Learning?

·       Advantage of Deep Learning over Machine learning

·       3 Reasons to go for Deep Learning

·       Real-Life use cases of Deep Learning

·       The Math behind Machine Learning: Linear Algebra

·       Scalars

·       Vectors

·       Matrices

·       Tensors

·       Hyperplanes

·       The Math Behind Machine Learning: Statistics

·       Probability

·       Conditional Probabilities

·       Posterior Probability

·       Distributions

·       Samples vs Population

·       Resampling Methods

·       Selection Bias

·       Likelihood

·       Review of Machine Learning

·       Regression

·       Classification

·       Clustering

·       Reinforcement Learning

·       Underfitting and Overfitting

·       Optimization

Live Lecture

·       How Deep Learning Works?

·       Activation Functions

·       Illustrate Perceptron

·       Training a Perceptron

·       Important Parameters of Perceptron

·       What is Tensorflow?

·       Tensorflow code-basics

·       Graph Visualization

·       Constants, Placeholders, Variables

·       Creating a Model

·       Step by Step - Use-Case Implementation

Live Lecture

·       Understand limitations of A Single Perceptron

·       Understand Neural Networks in Detail

·       Illustrate Multi-Layer Perceptron

·       Backpropagation – Learning Algorithm

·       Understand Backpropagation – Using Neural Network Example

·       MLP Digit-Classifier using TensorFlow

·       TensorBoard

Live Lecture

·       Why Deep Learning?

·       SONAR Dataset Classification

·       What is Deep Learning?

·       Feature Extraction

·       Working of a Deep Network

·       Training using Backpropagation

·       Variants of Gradient Descent

·       Types of Deep Networks

Live Lecture

·       Introduction to CNNs

·       CNNs Application

·       Architecture of a CNN

·       Convolution and Pooling layers in a CNN

·       Understanding and Visualizing a CNN

·       Transfer Learning and Fine-tuning Convolutional Neural Networks

Live Lecture

·       Intro to RNN Model

·       Application use cases of RNN

·       Modelling sequences

·       Training RNNs with Backpropagation

·       Long Short-Term memory (LSTM)

·       Recursive Neural Tensor Network Theory

·       Recurrent Neural Network Model

Live Lecture

·       Restricted Boltzmann Machine

·       Applications of RBM

·       Collaborative Filtering with RBM

·       Introduction to Autoencoders

·       Autoencoders applications

·       Understanding Autoencoders

Live Lecture

·       Define Keras

·       How to compose Models in Keras

·       Sequential Composition

·       Functional Composition

·       Predefined Neural Network Layers

·       What is Batch Normalization

·       Saving and Loading a model with Keras

·       Customizing the Training Process

·       Using TensorBoard with Keras

·       Use-Case Implementation with Keras

Live Lecture

·       Define TFlearn

·       Composing Models in TFlearn

·       Sequential Composition

·       Functional Composition

·       Predefined Neural Network Layers

·       What is Batch Normalization

·       Saving and Loading a model with TFlearn

·       Customizing the Training Process

·       Using TensorBoard with TFlearn

·       Use-Case Implementation with TFlearn

Fees

Offline Training @ Vadodara

  • Classroom Based Training
  • Practical Based Training
  • No Cost EMI Option
50000 45000

Online Training preferred

  • Live Virtual Classroom Training
  • 1:1 Doubt Resolution Sessions
  • Recorded Live Lectures*
  • Flexible Schedule
45000 40000

Corporate Training

  • Customized Learning
  • Onsite Based Corporate Training
  • Online Corporate Training
  • Certified Corporate Training

Certification

  • Upon the completion of the Classroom training, you will have an Offline exam that will help you prepare for the Professional certification exam and score top marks. The BIT Certification is awarded upon successfully completing an offline exam after reviewed by experts
  • Upon the completion of the training, you will have an online exam that will help you prepare for the Professional certification exam and score top marks. BIT Certification is awarded upon successfully completing an online exam after reviewed by experts.
  • This course is designed to solve different Deep Learning with Tensor flow Case studies on various Domains using Python. This certificate is very well recognized over 80 top MNCs from around the world and some of the Fortune 500 companies.