Software Training Institute in Chennai with 100% Placements – SLA Institute

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Deep Learning Online Course

(1538)
Live Online & Classroom Training
EMI
0% Interest

Our Deep Learning Online Training in Chennai will make students learn some of the most in-demand concepts in Deep Learning such as – Neural Network, Building Deep Learning Environment, TenserFlow, Activation Functions, Popular CNN Model Architectures, Word Representation Using word2vec etc. This curriculum will surely make students experts in the concept of  Deep Learning in a shorter span of time. Our Deep Learning Online Course with 100% placement support is curated with the help of leading experts from the IT industry, which makes our Deep Learning Online Course up-to-date in accordance with the latest trends.

Our SLA Institute is guaranteed to place you in high-paying Deep Learning Engineer and other Deep Learning related jobs with help of our experienced placement officers. SLA Institute’s Course Syllabus for Deep Learning covers all topics that are guaranteed to give you a complete understanding of the Deep Learning Online Course in Chennai.

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Upcoming Batches

Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
November 2024
Week days
(Mon-Fri)
Online/Offline

2 Hours Real Time Interactive Technical Training 

1 Hour Aptitude 

1 Hour Communication & Soft Skills

(Suitable for Fresh Jobseekers / Non IT to IT transition)

Course Fee
November 2024
Week ends
(Sat-Sun)
Online/Offline

4 Hours Real Time Interactive Technical Training

(Suitable for working IT Professionals)

Course Fee

Save up to 20% in your Course Fee on our Job Seeker Course Series

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Quick Enquiry

Placement

100% Assistance

Learning

Job-Centered Approach

Timings

Convenient Hrs

Mode

Online & Classroom

Certification

Industry-Accredited

This Course Includes

  • FREE Demo Class
  • 0% EMI Loan Facilities
  • FREE Softskill & Placement Training
  • Tie up with more than 500+ MNCs & Medium Level Companies
  • 100% FREE Placement Assistance
  • Course Completion Certificate
  • Training with Real Time Projects
  • Industry-Based Coaching By MNC IT Professionals
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Expected Criteria for Assured Placement

The following criteria help the placement team guide the candidates to get placed immediately after the course completion through SLA Institute.

  • 80% of coursework completion helps us arrange interviews in required companies.
  • 2 or 3 projects to be done for the selected course to ace the technical round effectively.
  • Ensure attending the placement training right from the first day of the selected course.
  • Practice well with resume building, soft skill, aptitude skill, and profile strengthening.
  • Utilize the internship training program at SLA for the complete technical skills.
  • Collect the course completion certificate and update the copy to the placement team.
  • Ensure your performance indicator meets the expectation of top companies.
  • Always be ready with the updated resume that includes project details done at SLA.
  • Enjoy unlimited interview arrangements along with internal mock interviews.
Have Queries? Ask our Experts

+91 89256 88858

SLA's Distinctive Placement Approach

1

Tech Courses

2

Expert Mentors

3

Assignments & Projects

4

Grooming sessions

5

Mock Interviews

6

Placements

Objectives of Deep Learning Online Course in Chennai

The primary objective of our Deep Learning Online Course in Chennai is to make enrolled candidates experts in Deep Learning. This Deep Learning Online Course will make students grow into successful and most in-demand Deep Learning Engineers, and more. SLA Institute’s Deep Learning Online Course Curriculum is loaded with some of the most useful and rare concepts that will surely give students a complete understanding of Deep Learning. So, some of those concepts are discussed below:

  • To make students well-versed with fundamental concepts in Deep Learning like – what is neural network..?, How do neural networks work?, Gradient descent, Stochastic Gradient descent, DL environment setup locally, Installing Tensorflow, Installing Keras etc.
  • To make students learn more about Deep Learning Course by learning topics like – TenserFlow Basics – Variables, Constant, Computation graph; Activation Functions – Sigmoid function, Hyperbolic Tangent function, ReLu -Rectified Linear units etc.
  • To make students learn all the advanced concepts in Deep Learning like -Exploring the MNIST dataset, Defining the hyperparameters, Model definition; LeNet architecture, AlexNet architecture, VGGNet architecture etc.

Scopes in the future for Deep Learning Online Course in Chennai

The following are the scopes available in the future for the Deep Learning Online Course:

  • Cutting-Edge Neural Network Designs: Education on advanced neural network models such as transformers, GANs (Generative Adversarial Networks), and attention mechanisms.
  • Integration with AI and Machine Learning: Combining deep learning with other AI techniques to develop hybrid models.
  • Cloud-Based Deep Learning Solutions: Utilizing cloud services like AWS, Google Cloud, and Azure for scalable deep learning training and deployment.
  • Real-Time Processing and Edge AI: Creating deep learning models for real-time applications and deployment on edge devices, including smartphones, IoT gadgets, and embedded systems.

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Project Practices on Deep Learning Training

Project 1Image Classification with CNNs

Develop a Convolutional Neural Network (CNN) to categorize images from datasets such as CIFAR-10 or MNIST. Use data augmentation techniques to boost model accuracy.

Project 2Text Classification with NLP

Build a model to classify text documents into predefined categories (e.g., distinguishing between spam and non-spam emails) using Recurrent Neural Networks (RNNs) or Transformers.

Project 3Object Detection and Localization

Create a model to identify and locate objects within images using architectures like YOLO (You Only Look Once) or Faster R-CNN.

Project 4Image Generation with GANs

Implement a Generative Adversarial Network (GAN) to generate new images based on a training set. Experiment with different GAN variations like DCGAN or StyleGAN.

Prerequisites for learning Deep Learning Online Course in Chennai

SLA Institute does not demand any prerequisites for any course at all. SLA Institute has courses that cover everything from the fundamentals to advanced topics so whether the candidate is a beginner or an expert they will all be accommodated and taught equally in SLA Institute. However having a fundamental understanding of these concepts below will help you understand Deep Learning better, However it is completely optional:

  • Basic Programming Skills: Proficiency in at least one programming language commonly used in deep learning, such as Python.
  • Understanding of Linear Algebra: Familiarity with vectors, matrices, and operations such as dot products and matrix multiplication.
  • Knowledge of Calculus: Basic understanding of differentiation and integration, with a focus on partial derivatives.
  • Familiarity with Probability and Statistics: Understanding of basic concepts like distributions, statistical measures, and probability theory.

Our Deep Learning Online Course in Chennai is apt for:

  • Students eager to excel in Deep Learning
  • Professionals considering transitioning to Deep Learning careers
  • IT professionals wanting to enhance their Deep Learning skills
  • Deep Learning Engineers who are looking forward to expanding their expertise.
  • Individuals searching opportunities in the Deep Learning field.

Job Profile for Deep Learning Online Course in Chennai

After finishing the Deep Learning Online Course in Chennai, students will be placed in various organizations through SLA Institute. This section will explore the various range of job profiles in which students can possibly be possible be placed as in the Deep Learning sector; 

  • Deep Learning Engineer: Deep Learning Online Course will train students into successful Deep Learning Engineers who will develop and refine deep learning models and algorithms for applications in areas like computer vision, natural language processing, and speech recognition.
  • Machine Learning Engineer: Deep Learning Online Course will turn students into skilled Machine Learning Engineer who will deploy machine learning models, including deep learning models, into live environments. Focus on feature engineering, model improvement, and scalability.
  • Data Scientist: Deep Learning Online Course will make students into Data Scientist who will analyze complex datasets to extract meaningful insights. Apply deep learning methods to enhance predictive analytics and data interpretation.
  • AI Research Scientist: The SLA Institute will provide students with enough resources that it will train students into successful AI Research Scientist who will conduct research to push the boundaries of deep learning and AI. Develop new algorithms and models and publish research findings in academic journals.
  • Computer Vision Engineer: The SLA Institute will turn students into skilled Computer Vision Engineers who will create and implement computer vision algorithms using deep learning for tasks such as image classification, object detection, and image segmentation.
  • Natural Language Processing (NLP) Engineer: The SLA Institute will make students into Natural Language Processing (NLP) Engineers who will build NLP systems for applications like text classification, sentiment analysis, and language translation using deep learning models.
  • AI Product Manager: Manage the development and rollout of AI products, working with teams to integrate deep learning solutions and ensure they meet market needs.
  • Robotics Engineer: Utilize deep learning in robotics for tasks such as autonomous navigation, object handling, and human-robot interaction.
  • Speech and Audio Processing Engineer: Develop deep learning models for tasks such as speech recognition, speech synthesis, and audio processing.
  • Data Engineer: Design and manage data pipelines and infrastructure to support the deployment of deep learning models and handle large datasets.
  • Business Intelligence (BI) Developer: Incorporate deep learning models into BI tools to enhance data analytics and reporting.
  • AI Solutions Architect: Design and implement AI solutions, including deep learning models, tailored to meet specific business needs and technical requirements.

Want to learn with a personalized course curriculum?

The Placement Process at SLA Institute

  • To Foster the employability skills among the students
  • Making the students future-ready
  • Career counseling as and when needed
  • Provide equal chances to all students
  • Providing placement help even after completing the course

Deep Learning Course FAQ

What role do activation functions play in deep learning, and how should one select them?

Activation functions introduce non-linearity into neural networks, enabling them to model complex patterns. Common functions include:

  • ReLU: Popular in hidden layers due to its simplicity and ability to avoid vanishing gradients.
  • Sigmoid: Used in output layers for binary classification, though less common in hidden layers due to vanishing gradients.
  • Tanh: Similar to sigmoid but with outputs ranging from -1 to 1, suitable for tasks needing negative values.
  • Softmax: Applied in the output layer for multi-class classification to produce probability distributions.
  • Selection: ReLU and its variants are generally preferred for hidden layers, while sigmoid and softmax are used in output layers based on the task.
What is transfer learning, and how can it be utilized in deep learning projects?
  • Transfer learning uses a pre-trained model for a new, related task, speeding up training and improving performance with limited data.
  • Utilization: Start with a pre-trained model (e.g., VGG, ResNet) and fine-tune it on your dataset, typically by freezing lower layers and retraining higher layers or the classifier.
How does the choice of optimizer influence deep learning model performance?

Optimizers adjust network weights to minimize the loss function, affecting convergence speed and effectiveness. Key optimizers include:

  • SGD: Simple but can be slow and sensitive to learning rate.
  • Adam: Combines advantages of SGD variants for faster convergence and better performance.
  • RMSprop: Adjusts learning rates based on recent gradients, useful for non-stationary tasks.
  • Impact: Advanced optimizers like Adam often provide better performance and faster convergence but should be chosen based on specific task needs.
What should be considered when deploying deep learning models in production?

Key considerations include:

  • Scalability: Ensuring the model handles large-scale data and requests effectively.
  • Latency: Minimizing response time with optimization or hardware accelerators.
  • Model Monitoring: Tracking performance and detecting issues like concept drift.
  • Version Control: Managing model updates and rollbacks.
  • Resource Management: Optimizing computational resources for cost efficiency.
  • Tools: Utilize platforms like TensorFlow Serving or NVIDIA Triton for efficient deployment and scaling.
Where is the corporate office of the SLA Institute located?

The corporate office of the SLA Institute is located at K.K.Nagar.

Is EMI an option at the SLA Institute?

Yes, the SLA Institute does indeed offer EMI options in payments with 0% interest.

Is it easy to learn the Deep Learning Course in the Online mode?

Learning the Deep learning Course in Online mode can be easy if students give their complete dedication in learning the course by cooperating with the trainers and submitting the project before the deadline. 

How long is the Deep Learning Online Course?

The Deep Learning Online Course is 1.5 months long. 

On Average Students Rated The Deep Learning Course 4.80/5.0
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