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AI & Deep Learning with TensorFlow

713+ Learners

Stepleaf's Deep Learning in TensorFlow with Python Certification Training is curated by industry professionals as per the industry requirements & demands. You will master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras & TFLearn. The course has been specially curated by industry experts with real-time case studies.

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AI Deep Learning Course

Jul 25 Sat,Sun (7.5 Weeks) Weekend Batch Filling Fast 03:00 PM  05:00 PM
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Course Price at

$ 509.00

About Course

In this Deep Learning in TensorFlow with Python Training we will learn about what is AI, explore neural networks, understand deep learning frameworks, implement various machine learning algorithms using Deep Networks. We will also explore how different layers in neural networks does data abstraction and feature extraction using Deep Learning.

Stepleaf’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.

Course Objectives:

  • 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

Who should go for this training?

Deep Learning is one of the most accelerating and promising fields, among all the technologies available in the IT market today. To become an expert in this technology, you need a structured training with the latest skills as per current industry requirements and best practices.

Besides strong theoretical understanding, you will be working on various real-life data projects using different neural network architectures as a part of solution strategy.

Additionally, you will receive guidance from a Deep Learning expert who is currently working in the industry on real-life projects.

Pre-requisites

  • Basic programming knowledge in Python
  • Concepts about Machine Learning
Key Skills

Python, machinelearning, deeplearning, Artificial Intelligence, TensorFlow, Convolutional Neural Network, CNN, Recurrent Neural Network, RNN, Long short-term memory, LSTM, Keras, TFlearn, Autoencoders, Restricted Boltz-mann Machine, RBM, Neural Networks, Natural Language Processing, NLP, Text Analytics, Text Processing

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Course Contents

Download Syllabus

AI & Deep Learning with TensorFlow Content

Learning Objectives: 

In this module, you’ll get an introduction to Deep Learning and understand how Deep Learning solves problems which Machine Learning cannot. Understand fundamentals of Machine Learning and relevant topics of Linear Algebra and Statistics.

Topics: 

  • 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

Hands-On

  • Implementing a Linear Regression model for predicting house prices from Boston dataset
  • Implementing a Logistic Regression model for classifying Customers based on a Automobile purchase dataset
Learning Objectives:
In this module, you’ll get an introduction to Neural Networks and understand it’s working i.e. how it is trained, what are the various parameters considered for its training and the activation functions that are applied.
Topics:
  • 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

Hands-On

  • Building a single perceptron for classification on SONAR dataset

Learning Objectives:
In this module, you’ll understand backpropagation algorithm which is used for training Deep Networks. You will know how Deep Learning uses neural network and backpropagation to solve the problems that Machine Learning cannot.
Topics:
  • 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

Hands-On

  • Building a multi-layered perceptron for classification of Hand-written digits

Learning Objectives:
In this module, you’ll get started with the TensorFlow framework. You will understand how it works, its various data types & functionalities. In addition, you will create an image classification model.
Topics:
  • 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

Hands-On

  • Building a multi-layered perceptron for classification on SONAR dataset
Learning Objectives:
In this module, you’ll understand convolutional neural networks and its applications. You will learn the working of CNN, and create a CNN model to solve a problem.
Topics:
  • Introduction to CNNs
  • CNNs Application
  • Architecture of a CNN
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing a CNN

Hands-On

  • Building a convolutional neural network for image classification. The model should predict the difference between 10 categories of images.

Learning Objectives:
In this module, you’ll understand Recurrent Neural Networks and its applications. You will understand the working of RNN, how LSTM are used in RNN, what is Recursive Neural Tensor Network Theory, and finally you will learn to create a RNN model.
Topics:
  • 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

Hands-On

  • Building a recurrent neural network for SPAM prediction.

Learning Objectives:
In this module, you’ll understand RBM & Autoencoders along with their applications. You will understand the working of RBM & Autoencoders, illustrate Collaborative Filtering using RBM and understand what are Deep Belief Networks.
Topics:
  • Restricted Boltzmann Machine
  • Applications of RBM
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders

Hands-On

  • Building a Autoencoder model for classification of handwritten images extracted from the MNIST Dataset

Learning Objectives:
In this module, you’ll understand how to use Keras API for implementing Neural Networks. The goal is to understand various functions and features that Keras provides to make the task of neural network implementation easy.
Topics:
  • 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

Hands-On

  • Build a model using Keras to do sentiment analysis on twitter data reactions on GOP debate in Ohio

Learning Objectives:
In this module, you’ll understand how to use TFLearn API for implementing Neural Networks. The goal is to understand various functions and features that TFLearn provides to make the task of neural network implementation easy.
Topics:
  • 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

Hands-On

  • Build a recurrent neural network using TFLearn to do image classification on hand-written digits

Learning Objectives:
In this module, you should learn how to approach and implement a project end to end. The instructor will share his industry experience and related insights that will help you kickstart your career in this domain. In addition, we will be having a QA and doubt clearing session for you.
Topics: 
  • How to approach a project?
  • Hands-On project implementation
  • What Industry expects?
  • Industry insights for the Machine Learning domain
  • QA and Doubt Clearing Session

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Projects

How will I execute the practicals?

You will do your Assignments/Case Studies using Jupyter Notebook that is already installed on your Cloud Lab environment whose access details will be available on your LMS. You will be accessing your Cloud Lab environment from a browser. For any doubt, the 24*7 support team will promptly assist you.

What is CloudLab?

CloudLab is a cloud-based Jupyter Notebook which is pre-installed with TensorFlow and Python packages on cloud-lab environment. It is offered by StepLeaf as a part of Deep Learning with TensorFlow course where you can execute all the in-class demos and work on real-life projects in a fluent manner.

You’ll be able to access the CloudLab via your browser which requires minimal hardware configuration. In case, you get stuck in any step, our support ninja team is ready to assist 24x7.

What are the projects included in this Deep Learning in TensorFlow with Python Certification Training?

StepLeaf's TensorFlow Certification Training includes the following case studies:

  • Create an image classifier using CNN, and classify images in one of the predefined 100 classes
  • Create a script generator using LSTM, and generate scripts for any popular novel that might interest you
  • Choose a dataset of your own, explore the different challenges faced on the dataset domain and try to solve one of them with any neural network architecture covered in this course

Certification

StepLeaf’s Deep Learning Engineer Certificate Holders work at 1000s of MNC Companies All Over the World

FAQ

All the instructors at StepLeaf are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by StepLeaf for providing an awesome learning experience to the participants.

We have 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.
You will never miss a lecture at StepLeaf! 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.
Yes, the access to the course material will be available for lifetime once you have enrolled into the course.
According to payscale.com, the median salary for Deep Learning and TensorFlow Certified Engineer tops $120,000 per year.
Deep learning is being applied on most of the AI related areas for better performance. Lots and lots companies are moving into Deep Learning to improve their model accuracy and therefore, making their product more efficient. There is an ample opportunity to apply Deep Learning & TensorFlow in the field of medicine, precision agriculture, etc.
Deep Learning with TensorFlow Certification Training by StepLeaf has been carefully designed in such a way that both beginners and experts can go through it without facing any difficulties. The instructor will be available for you throughout the training period to ensure that you are able grasp all the concepts perfectly. Besides this, the 24x7 support ninjas are available to help you with all your issues and doubts.
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