Deep Learning Course with Tensorflow Certification Training

Deep Learning with TensorFlow Training

In this Deep Learning in TensorFlow with Python Training, we will learn about what AI is, explore neural networks, understand deep learning frameworks and implement various machine learning algorithms using Deep Networks. We will also explore how different layers in neural networks do data abstraction and feature extraction using Deep Learning. AI Deep Learning with Tensorflow Training, data in the world is unlabeled and unstructured so you need to learn how to implement Machine learning algorithms Neural Networks Python. Make use of Google’s library to apply deep Learning using Python in this Deep Learning in TensorFlow online course, Mildaintrainings provides hands-on training. Enroll & Get Certified now!

✔ Course Duration : 32 hrs

✔ Training Options : Live Online / Self-Paced / Classroom

✔ Certification Pass : Guaranteed

Deep Learning with TensorFlow Training

In this Deep Learning in TensorFlow with Python Training, we will learn about what AI is, explore neural networks, understand deep learning frameworks and implement various machine learning algorithms using Deep Networks.TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Mildaintrainings’s Deep Learning in TensorFlow training is designed to make you a Data Scientist by providing you rich hands-on training on Deep Learning in TensorFlow with Python.
This course is a stepping stone in your Data Science journey using which you will get the opportunity to work on various Deep Learning projects. In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.

What you will Learn

In-depth knowledge of Deep Neural Networks
Comprehensive knowledge of various Neural Network architectures such as Convolutional Neural Network, Recurrent Neural Network, Autoencoders
Implementation of Collaborative Filtering with RBM
The exposure to real-life industry-based projects which will be executed using TensorFlow library
Rigorous involvement of an SME throughout the AI and Deep Learning Training to learn industry standards and best practices

PREREQUISITES

Basic programming knowledge in Python
Concepts about Machine Learning
Statistics and Machine learning algorithms
Python Essential

CURRICULUM

Learning Objectives:

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
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
Implementing a Linear Regression model
Implementing a Logistic Regression model

Learning Objectives:

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
Building a single perceptron for classification on SONAR dataset

Learning Objectives:

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
Building a multi-layered perceptron

Learning Objectives:

Why Deep Networks
Why Deep Networks give better accuracy?
Use-Case Implementation on SONAR dataset
Understand How Deep Network Works?
How Backpropagation Works?
Illustrate Forward pass, Backward pass
Different variants of Gradient Descent
Types of Deep Networks
Building a multi-layered perceptron

Learning Objectives:

Introduction to CNNs
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN
Building a convolutional neural network for image classification.

Learning Objectives:

Introduction 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
Building a recurrent neural network for SPAM prediction.

Learning Objectives:

Restricted Boltzmann Machine
Applications of RBM
Collaborative Filtering with RBM
Introduction to Autoencoders
Autoencoders applications
Understanding Autoencoders
Building a Autoencoder model

Learning Objectives:

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
Build a model using Keras to do sentiment analysis

Learning Objectives:

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
Using TensorBoard with TFLearn
Use-Case Implementation with TFLearn
Build a recurrent neural network using TFLearn

Learning Objectives:

Research Evaluation
The Future of TensorFlow

FAQs

You can enroll for this classroom training online. Payments can be made using any of the following options and receipt of the same will be issued to the candidate automatically via email.
1. Online ,By deposit the mildain bank account
2. Pay by cash team training center location

Highly qualified and certified instructors with 20+ years of experience deliver more than 200+ classroom training.

Contact us using the form on the right of any page on the mildaintrainings website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

You will never miss a lecture at Mildaintrainigs! You can choose either of the two options: View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch.

We have a limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.

Yes, you can cancel your enrollment if necessary prior to 3rd session i.e first two sessions will be for your evaluation. We will refund the full amount without deducting any fee for more details check our Refund Policy

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

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