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AI DEEP LEARNING USING TENSORFLOW

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

4 Days / 32 Hrs

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Deep Learning using Python AI Deep Learning with Tensorflow
online course Deep Learning in TensorFlow Machine learning algorithms Neural Networks Python   Reviews 

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Deep Learning using Tensorflow

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Key Features: AI Deep Learning using Tensorflow

Price: USD 499

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Delivery Options: Attend remote-live or on-demand online classes.

AI Deep Learning with Tensorflow

  • Understand major technology trends driving Deep Learning
  • Build fully connected deep neural networks
  • Get proficient in Convolutional Neural Networks and its applications
  • Become adept in imposing Recurrent Neural Networks Model
  • Implement Neural Networks in TensorFlow
  • 40 hours of instructor-led training

DESCRIPTION

DESCRIPTION

AI Deep Learning with Tensorflow
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.
Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which consitutes the vast majority of data in the world.
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. This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Crafted and delivered by a team of industry experts, this comprehensive AI Deep Learning with Tensorflow training has all the components required to give you a head-start into the field of Deep Learnining of machines!
AI Deep Learning with Tensorflow course duration: 40-45 hours.

Deep Learning In TensorFlow with Python Course Objectives

Deep Learning in TensorFlow with Python Training is designed by industry experts to make you a Certified Deep Learning Engineer. The Deep Learning in TensorFlow course offers:

  • 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 & Deep Learning Training to learn industry standards and best practices
Skills that you will be learning with Mildaintrainings Deep Learning in TensorFlow Certification Training.

Deep Learning in TensorFlow with Python Training will help you to become a Deep Learning Engineer. It will hone your skills by offering you comprehensive knowledge on Deep Learning in TensorFlow. It will also acquaint you with the required hands-on experience for solving real-time industry-based projects.

  • Deep Learning and TensorFlow Concepts
  • Working with Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)
  • Proficiency in Long short-term memory (LSTM)
  • Implementing Keras, TFlearn, Autoencoders
  • Implementing Restricted Boltzmann Machine (RBM)
  • Knowledge of Neural Networks & Natural Language Processing (NLP)
  • Using Python with TensorFlow Libraries
  • Perform Text Analytics
  • Perform Text Processing
Prerequisites for this Deep Learning in TensorFlow with Python Course?
  • Basic programming knowledge in Python
  • Concepts about Machine Learning
  • Statistics and Machine learning algorithms
  • Python Essential
  • Some familiarity with C++

CURRICULUM

CURRICULUM

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
  • Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
  • Implementing a Linear Regression model
  • Implementing a Logistic Regression model
Understanding Neural Networks with 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
  • Building a single perceptron for classification on SONAR dataset
Deep dive into Neural Networks with 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
  • Building a multi-layered perceptron
Master Deep Networks
  • 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
Convolutional Neural Networks (CNN)
  • 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.
Recurrent Neural Networks (RNN)
  • 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.
Restricted Boltzmann Machine (RBM) and Autoencoders
  • Restricted Boltzmann Machine
  • Applications of RBM
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • Building a Autoencoder model
Keras API
  • 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
TFLearn API
  • 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
Wrapping Up
  • Research Evaluation
  • The Future of TensorFlow

FAQ | AI Deep Learning with Tensorflow

Why should I take AI Deep Learning with Tensorflow Training from Mildaintrainings?

You should go for AI Deep Learning with Tensorflow from Mildaintrainings as our trainers have 12 plus years of industry practical experience and we also provide Practical training with the live project so that you could understand each and everything better, it will help you in your job. At Mildaintraining we will provide you six months Technical support as well.

Do I get the AI Deep Learning with Tensorflow training certificate?

Yes, at Mildaintrainings we will provide you participation certificate after the completion of AI Deep Learning with Tensorflow course from Mildaintrainings.

When will the classes be held for AI Deep Learning with Tensorflow?

Classes will be held on weekends as well as weekdays as per schedule or your convenience.

What if I miss the AI Deep Learning with Tensorflow class?

If you miss the class in that case backup class can be adjusted in next live session.

What is AI Deep Learning with Tensorflow course duration?

This course duration will be of 40 – 45 hours or 5 days and it will be Instructor lead training at Mildaintrainings with Practical training with live project. The timing will be according to your convenience it can be on weekend and weekdays.

Reviews

Rekha Amminbhavi

Testing Engineer at CSC

Guidewire The program was really knowledgeable
and the modules were just perfectly made and managed
and were taught with ease.
I am so happy I choose mildain

Jyotish Phukon

Senior EDI Analyst at SIQES

IBM Sterling Integrator Good training. Explaining the things with practical examples. Well experienced and confident enough to answer every query. Trainers are working more as a friend rather than working like for money. Worth of paying for the course.

Machine Leaning With Python

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