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AI & Deep Learning with Tensor flow Training Course

AI & Deep learning with Tensorflow course Training aims to impart training on the essentials of the Tensorflow and throws light on the aspects such as: main functions, operations and the execution pip...

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Course Description

AI & Deep learning with Tensorflow course Training 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 ne...

AI & Deep learning with Tensorflow course Training 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.

What you’ll learn
  • Live Class Practical Oriented Training
  • Timely Doubt Resolution
  • Dedicated Student Success Mentor
  • Certification & Job Assistance
  • Free Access to Workshop & Webinar
  • No Cost EMI Option
  • Best practices for TensorFlow, a popular open-source machine learning framework to train a neural network for a computer
  • Understand the Autoencoders and varitional Autoencoders. Learn to apply the Analytical mathematics to the data.
  • Learn how to run a “Hello World” program in TensorFlow. Describe Deep Learning. Learn about TFlearn implementation
  • Build natural language processing systems using TensorFlow.
  • Learn about Autoencoders & discuss their Applications. Learn about the application of Convolutional Neural Networks.
  • Learn the implementation procedure of Collaborative Filtering with RBM Understand what Restricted Boltzmann Machine is?

Covering Topics

1
Introduction to Deep Learning
2
Understanding the Fundamentals of Neural Networks Using Tensorflow
3
Deep Dive into Neural Networks Tensorflow
4
Master Deep Networks
5
Convolution Neural Networks (CNN)
6
Recurrent Neural Networks (RNN)
7
Restricted Boltzmann Machine (RBM) & Autoencoders
8
Keras
9
TFlearn
10
Recurrent Neural Networks (RNN)

Curriculum

      Lecture-1 Introduction to Deep Learning

·       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
      Lecture-2 Understanding the Fundamentals of Neural Networks Using Tensorflow

·       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
      Lecture-3 Deep Dive into Neural Networks Tensorflow
·       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
      Lecture-4 Master Deep Networks

·       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
      Lecture-5 Convolution Neural Networks (CNN)
·       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
      Lecture-6 Recurrent Neural Networks (RNN)
 
·       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
      Lecture-7 Restricted Boltzmann Machine (RBM) & Autoencoders

·       Restricted Boltzmann Machine

·       Applications of RBM

·       Collaborative Filtering with RBM

·       Introduction to Autoencoders

·       Autoencoders applications

·       Understanding Autoencoders
      Lecture-8 Keras

·       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
      Lecture-9 TFlearn

·       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

Frequently Asked Questions

Basic mathematical Knowledge is required for this training.

The course offers a variety of online training options, including: • Live Virtual Classroom Training: Participate in real-time interactive sessions with instructors and peers. • 1:1 Doubt Resolution Sessions: Get personalized assistance and clarification on course-related queries. • Recorded Live Lectures*: Access recorded sessions for review or to catch up on missed classes. • Flexible Schedule: Enjoy the flexibility to learn at your own pace and according to your schedule.

Live Virtual Classroom Training allows you to attend instructor-led sessions in real-time through an online platform. You can interact with the instructor, ask questions, participate in discussions, and collaborate with fellow learners, simulating the experience of a traditional classroom setting from the comfort of your own space.

: If you miss a live session, you can access recorded lectures* to review the content covered during the session. This allows you to catch up on any missed material at your own pace and ensures that you don't fall behind in your learning journey.

The course offers a flexible schedule, allowing you to learn at times that suit you best. Whether you have other commitments or prefer to study during specific hours, the course structure accommodates your needs, enabling you to balance your learning with other responsibilities effectively. *Note: Availability of recorded live lectures may vary depending on the course and training provider.