Deep Learning in TensorFlow Training in Porur Chennai
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.
DThe Deep Learning is a subarea of the Machine Learning that makes use of Deep Neural Networks (with many layers) and specific novel algorithms for the pre-processing of data and regularisation of the model. Deep learning affected business applications as never happened in Machine Learning before.
About Our Trainer
- Karthik is an experienced statistician and data miner with more than 10+ years of experience using R, Python and SAS and a passion for building analytical solutions. He is a M.S. in Quantitative Economics and Applied Mathematics graduate who has analytics experience working with companies like Capital One, Walmart, ICICI Lombard etc.
Karthik is a lead Data Scientist at Citi Bank. As a Certified Predictive Modeler, Statistical Business Analyst, and Certified Advanced Programmer, Karthik is passionate about sharing his knowledge on how data science can support data-driven business decisions.
Qualification: M.S. in Statistics
Membership American Statistical Association
Want a free career Advice or any career related queries? Reach him by
+91- 8939915572
Curriculum
Deep Learning in TensorFlow Training Course Content
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.
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.
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.
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
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.
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.
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 Deep Learning projects.
During this course you will be trained by our expert instructors on:
- 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 Boltz-mann Machine (RBM)
- Knowledge of Neural Networks & Natural Language Processing (NLP)
- Using Python with TensorFlow Libraries
- Perform Text Analytics
- Perform Text Processing
The TensorFlow with Python Training is for all the professionals who are passionate about Deep Learning and want to go ahead and make their career as a Deep Learning Engineer. It is best suited for individuals who are:
However, Deep learning is not just focused on one industry or skill set, it can be used by anyone to enhance their portfolio.
- Developers aspiring to be a 'Data Scientist'
- Analytics Managers who are leading a team of analysts
- Business Analysts who want to understand Deep Learning (ML) Techniques
- Information Architects who want to gain expertise in Predictive Analytics
- Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused on one industry or skill set, it can be used by anyone to enhance their portfolio.
Required Pre-requisites
- Basic programming knowledge in Python
- Concepts about Machine Learning
- Statistics and Machine learning algorithms
- Python Essentials
greens offers you complimentary self-paced courses:
Course 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 for predicting house prices from Boston dataset
- Implementing a Logistic Regression model for classifying Customers based on a Automobile purchase dataset
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.
Hands-On
- 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:
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.
Hands-On
-
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 for classification of Hand-written digits
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.
Hands-On
-
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 for classification on SONAR dataset
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.
Hands-On
-
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. The model should predict the difference between 10 categories of images.
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.
Hands-On
-
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.
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.
Hands-On
-
Learning Objectives:
- Restricted Boltzmann Machine
- Applications of RBM
- Collaborative Filtering with RBM
- Introduction to Autoencoders
- Autoencoders applications
- Understanding Autoencoders
- Building a Autoencoder model for classification of handwritten images extracted from the MNIST Dataset
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.
Hands-On
-
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 on twitter data reactions on GOP debate in Ohio
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.
Hands-On
-
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
- Customizing the Training Process
- Using TensorBoard with TFLearn
- Use-Case Implementation with TFLearn
- Build a recurrent neural network using TFLearn to do image classification on hand-written digits
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.
Hands-On
-
Learning Objectives:
- How to approach a project?
- Hands-On project implementation
- What Industry expects?
- Industry insights for the Machine Learning domain
- QA and Doubt Clearing Session
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.
