Deep Learning Course in Bangalore, India
Deep Learning With Tensorflow
Simplilearn's Deep Learning with TensorFlow Course in Bangalore serves the purpose of turning you into an expert by providing insights into Deep Learning and TensorFlow concepts. TensorFlow is an open-source software library that is related to machine learning uses and the deep neural network. This tensorflow training prepares you to become a deep learning scientist by training you in deep learning techniques like building artificial neural networks and traversing through layers of data abstraction to understand the power of data and implement algorithms.
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Deep Learning Course Overview
In this Deep Learning with Keras and TensorFlow course, you will become familiar with the language and fundamental concepts of artificial neural networks, PyTorch, autoencoders, and more. Upon completion, you will be able to build deep learning models, interpret results and build your own deep learning project.
Tensorflow Training Key Features
At Simplilearn, we value the trust of our patrons immensely. But, if you feel that a course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
- 34 hours of Blended Learning
- Real-life industry-based projects
- 24/7 support with dedicated project mentoring sessions
- Flexibility to choose classes
- Dedicated mentoring session from our Industry expert faculties
Skills Covered
- Keras and TensorFlow Framework
- PyTorch and its elements
- Image Classification
- Artificial Neural Networks
- Autoencoders
- Deep Neural Networks
- Conventional Neural Networks
- Recurrent Neural Networks
- ADAM Adagrad and Momentum
Benefits
Deep learning is one of the latest technologies in AI and machine learning, used in smartphone apps, power grids, helping us find solutions to climate change, and more. This course can lead to lucrative roles in IT, healthcare, FinTech, e-commerce, and other industries.
- Designation
- Annual Salary
- Hiring Companies
- Annual SalarySource: GlassdoorHiring CompaniesSource: Indeed
- Annual SalarySource: GlassdoorHiring CompaniesSource: Indeed
Deep Learning Course Curriculum
Eligibility
Demand for skilled deep learning engineers is booming across a wide range of industries, making this Deep Learning with Keras and TensorFlow training well-suited for professionals at the intermediate to advanced level. We recommend this deep learning course particularly for Software Engineers, Data Scientists, Data Analysts and Statisticians with an interest in deep learning.
Pre-requisites
Participants in this Deep Learning with Keras and TensorFlow course should have familiarity with programming fundamentals, fair understanding of basics of statistics and mathematics and good understanding of machine learning concepts.
Course Content
Section 1 - Deep Learning with Tensor Flow (Self Learning)
PreviewLesson 2 - Introduction to Tensorflow
31:55Preview2.1 Learning Objectives
07:002.2 Introduction to TensorFlow
07:002.3 TensorFlow's Hello World
03:282.4 Tensorflow Hello World
2.5 Linear Regression With Tensorflow
2.6 Logistic Regression With Tensorflow
2.7 Activation Functions
2.8 Intro to Deep Learning
02:392.9 Deep Neural Networks
11:48
Lesson 3 - Convolutional Networks
21:51Preview3.1 Learning Objectives
3.2 Intro to Convolutional Networks
04:373.3 CNN for Classifications
04:093.4 CNN Architecture
13:053.5 Understanding Convolutions
3.6 CNN with MNIST Dataset
Lesson 4 - Recurrent Neural Network
24:43Preview4.1 Learning Objectives
03:064.2 The Sequential Problem
03:064.3 The RNN Model
05:284.4 The LSTM Model
05:254.5 Applying RNNs to Language Modeling
07:384.6 LTSM Basics
4.7 MNIST Data Classification With RNN/LSTM
4.8 Applying RNN/LSTM to Language Modelling
4.9 Applying RNN/LSTM to Character Modelling
Lesson 5 - Restricted Boltzmann Machines (RBM)
14:145.1 Learning Objectives
04:295.2 Intro to RBMs
04:295.3 Training RBMs
05:165.4 RBM MNIST
5.5 Collaborative Filtering With RBM
Lesson 6 - Autoencoders
17:20Preview6.1 Learning Objectives
04:516.2 Intro to Autoencoders
04:516.3 Applying RNNs to Language Modelling
07:386.4 Autoencoders
6.5 DBN MNIST
Lesson 1 - Welcome!
02:061.1 Welcome!
02:061.2 Learning Objectives
Lesson 7 - Course Summary
02:177.1 Course Summary
02:17Unlocking IBM Certificate
Section 2 - Deep Learning with Keras and Tensor Flow (Live Classes)
PreviewLesson 1 - Course introduction
Introduction
Lesson 2 - AI and Deep learning introduction
What is AI and Deep learning
Brief History of AI
Recap: SL, UL and RL
Deep learning : successes last decade
Demo & discussion: Self driving car object detection
Applications of Deep learning
Challenges of Deep learning
Demo & discussion: Sentiment analysis using LSTM
Fullcycle of a deep learning project
Key Takeaways
Knowledge Check
Lesson 3 - Artificial Neural Network
Biological Neuron Vs Perceptron
Shallow neural network
Training a Perceptron
Demo code: Perceptron ( linear classification) (Assisted)
Backpropagation
Role of Activation functions & backpropagation
Demo code: Backpropagation (Assisted)
Demo code: Activation Function (Unassisted)
Optimization
Regularization
Dropout layer
Key Takeaways
Knowledge Check
Lesson-end Project (MNIST Image Classification)
Lesson 4 - Deep Neural Network & Tools
Deep Neural Network : why and applications
Designing a Deep neural network
How to choose your loss function?
Tools for Deep learning models
Keras and its Elements
Demo Code: Build a deep learning model using Keras (Assisted)
Tensorflow and Its ecosystem
Demo Code: Build a deep learning model using Tensorflow (Assisted)
TFlearn
Pytorch and its elements
Key Takeaways
Knowledge Check
Lesson-end Project: Build a deep learning model using Pytorch with Cifar10 dataset
Lesson 5 - Deep Neural Net optimization, tuning, interpretability
Optimization algorithms
SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
Batch normalization
Demo Code: Batch Normalization (Assisted)
Exploding and vanishing gradients
Hyperparameter tuning
Interpretability
Key Takeaways
Knowledge Check
Lesson-end Project: Hyperparameter Tunning With Keras Tuner
Lesson 6 - Convolutional Neural Network
Success and history
CNN Network design and architecture
Demo code: CNN Image Classification (Assisted)
Deep convolutional models
Key Takeaways
Knowledge Check
Lesson-end Project: Image Classification
Lesson 7 - Recurrent Neural Networks
Sequence data
Sense of time
RNN introduction
LSTM ( retail sales dataset kaggle)
Demo code: Stock Price Prediction with LSTM (Assisted)
Demo code: Multiclass Classification using LSTM (Unassisted)
Demo code: Sentiment Analysis using LSTM (Assisted)
GRUs
LSTM Vs GRUs
Key Takeaways
Knowledge Check
Lesson-end Project: Stock Price Forecasting
Lesson 8 - Autoencoders
Introduction to Autoencoders
Applications of Autoencoders
Autoencoder for anomaly detection
Demo code: Autoencoder model for MNIST data (Assisted)
Key Takeaways
Knowledge Check
Lesson-end Project: Anomaly detection with Keras
- Free Course
Deep Learning Fundamentals
PreviewLesson 1 - Learning Objectives
Learning Objectives
Lesson 2 - Introduction to Deep Learning
22:31PreviewLearning Objectives
1.1 Deep Learning: The Series Introduction
03:461.2 What is a Neural Network
06:281.3 Three Reasons to go Deep
03:561.4 Your choice of Deep Net
02:581.5 An Old Problem
05:23
Lesson 3 - Deep Learning Models
22:56PreviewLearning Objectives
2.1 Restricted Boltzmann Machines
04:502.2 Deep Belief Nets
04:312.3 Convolutional Nets
08:152.4 Recurrent Nets
05:20
Lesson 4 - Additional Deep Learning Models
19:13Learning Objectives
3.1 Autoencoders
03:513.2 Recursive Neural Tensor Nets
05:493.3 Use Cases
09:33
Lesson 5 - Deep Learning Platforms & Libraries
25:37PreviewLearning Objectives
4.1 What is a Deep Net Platform?
03:414.2 H2O.ai
03:424.3 Dato GraphLab
03:324.4 What is a Deep Learning Library?
01:584.5 Theano
03:214.6 Caffe
02:484.7 Tensorflow
06:35Unlocking IBM Certificate
Project
Deep Learning Exam & Certification
What do I need to do to unlock my Simplilearn certificate?
Candidates need to attend one complete batch of the Online Classroom training and submit any one of the projects for assessment. After the successful assessment, the candidates are awarded the Deep Learning with TensorFlow Certification.Who provides the certification?
Simplilearn provides the course completion certificate to the candidates who finish the Deep Learning online training course satisfactorily.
Is this course accredited?
No, this Deep Learning course is not officially accredited.
How many questions are there on the Deep Learning exam?
The Deep Learning simulation test includes a total of 144 questions.
How long does it take to complete this Deep Learning course?
The Deep Learning online certification course can be finished in 45-50 hours if the course path is followed smoothly.
How many attempts do I have to pass the Deep Learning exam?
The candidates are allowed a maximum of three attempts to clear the Deep Learning certification exam. The trainers at Simplilearn, however, assist the candidates to clear the exam successfully.
Deep Learning Course Reviews
Abhishek Tripathi
BangaloreGood online content for data science. I completed Data Science with R and Python. The instructors have good knowledge on the subject. Self-learning videos help a lot, too. Thanks, Simplilearn.
Angiras Modak
Associate System Engineer at IBM India Pvt. Ltd., KolkataSimplilearn is one of the best online training providers available. The trainer was really great in explaining the concepts to the minute detail and also gave multiple real-world examples. The course content was very informative. I understood the concept of CNN. Overall I really enjoyed the training a lot.
A.Anthony Davis
KingstonThe Simplilearn Data Scientist Master’s Program is an awesome course! You learn how to solve real-world problems, and the wide variety of projects give you hands-on experience to make you industry-ready. The lecturers are experts and share their knowledge energetically. Thank you for an excellent learning experience.
Why Simplilearn
Simplilearn’s Blended Learning model brings classroom learningexperience online with its world-class LMS. It combines instructor-led training, self-paced learning and personalized mentoring to provide an immersive learning experience
Tensorflow Training FAQs
What are the course objectives?
The presence of Deep Learning is increasing in the technology space with its implementation in various fields like climate change solutions, driving advancements in healthcare, smartphone applications, improving agricultural yields, creating efficiencies in the power grid etc.Simplilearn has designed the Deep Learning course with TensorFlow, which allows candidates to learn deep learning techniques and build deep learning models using TensorFlow.
TensorFlow is an open-source software library developed by Google used for deep neural network research and contains powerful tools to help you build and implement artificial neural networks. It helps candidates create deep learning models, manage neural networks and interpret the results.
Moreover, engineers with enhanced deep learning skills can earn an average salary of $120,000 per year, according to payscale.com.
What skills you learn in Deep Learning course?
By the end of this deep learning course in Bangalore, you will be able to accomplish the following:
- Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
- Differentiate between machine learning, deep learning, and artificial intelligence
- Troubleshoot and improve deep learning models
- Build deep learning models in TensorFlow and interpret the results
- Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
- Build your own deep learning project
- Understand the language and fundamental concepts of artificial neural networks
- Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
Who should take Tensorflow Course?
Professionals who have an intermediate to advanced level of experience can benefit from the Deep learning with TensorFlow training in Bangalore. With Artificial Intelligence finding its applications in various fields, there is a highly growing demand for proficient deep learning engineers. This course is best suited for professionals like:
- Statisticians with an interest in deep learning
- Data scientists
- Data analysts
- Software engineers
What are the prerequisites for learning Deep Learning with Tensorflow?
Participants in this Deep Learning online course should have:
- Familiarity with programming fundamentals
- Fair understanding of basics of statistics and mathematics
Who are the instructors and how are they selected?
Simplilearn prefers trainers with high alumni rating only. We make sure that the trainers selected by us have a teaching experience of 12+ years along with thorough domain knowledge. The selection process involves profile screening, technical assessment, and a training demonstration prior to getting a chance to become our mentors.
If I need to cancel my enrollment, can I get a refund?
After deducing the administration fee, the remaining course fee will be refunded on canceling the enrollment. Please go through our Refund Policy to find out more.
Our Bangalore Correspondence / Mailing address
# 53/1 C, Manoj Arcade, 24th Main, Harlkunte, 2nd Sector, HSR Layout, Bangalore - 560102, Karnataka, India.
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