Deep Learning using Neural Networks Training in Gurgaon, Delhi

Deep Learning using Neural Networks

Neural Networks: Machine Learning Inspired by the Brain

Duration : 6 months Classes : 36 Days : Weekdays / Weekends

Overview
Curriculum
Pre-requisite
Review

Dive into the cutting-edge world of artificial intelligence with our immersive course on Deep Learning using Neural Networks. This program is tailored for aspiring AI engineers, data scientists, and tech professionals eager to master the foundations and applications of deep learning. Through a hands-on, project-driven approach, learners will explore the architecture and mechanics of neural networks, from simple feedforward models to complex convolutional, transformers and recurrent networks. Using popular frameworks like TensorFlow and PyTorch, participants will build, train, and optimize models that power real-world innovations in image recognition, natural language processing, and predictive analytics. Whether you're breaking into AI or advancing your expertise, this course equips you with the skills to design intelligent systems that learn and adapt.

This program is designed to transform you from a machine learning practitioner into a deep learning expert, capable of tackling the most complex, high-dimensional data problems-from computer vision and natural language processing (NLP) to advanced time series forecasting. Move beyond simple models and learn the critical skills of GPU-accelerated training, transfer learning, and advanced regularization techniques essential for building state-of-the-art, production-ready AI systems. This is your opportunity to build the specialized expertise demanded by top-tier tech companies.

This course blends theory with hands-on practice to help you build intelligent systems that learn and adapt. Whether you're looking to break into AI or sharpen your DL/ML toolkit, this program delivers the skills and confidence to thrive.

Target Audience:-
-Developers and engineers with basic Python and ML knowledge
-Data analysts and scientists transitioning into DNNs
-Students and professionals preparing for AI-focused careers
-Tech enthusiasts eager to explore predictive modeling and automation
-Statisticians and Mathematicians

Program Outcomes:-
-Comprehend the fundamental mathematics and architecture of DNNs
-Design and implement Convolutional Neural Networks (CNNs) from scratch
-Build and train Recurrent Neural Networks (RNNs)
-Apply advanced techniques such as Transfer Learning, Generative Adversarial Networks (GANs), and Transformer architectures
-Efficiently utilize GPU resources and implement practices like data augmentation
-Apply DNN techniques to real-world business problems across industries

Course Format:-
✔ The course shall be delivered through a combination of lectures, interactive discussions & case studies
✔ Participants are exposed to practical exercises and new-age projects, where they learn by doing
✔ Participants shall have access to online resources, including reading materials, videos & business simulations
✔ Students shall receive all the study material
✔ Guest speakers from the industry may be invited to share insights and experiences
✔ Regular assessments and quizzes will be conducted to reinforce learning
✔ This is a Classroom only training
✔ Corporates: We understand your specific needs and goals. Contact us for customizations to this training

Trainers:-
✔ Equipped with multidisciplinary backgrounds
✔ Experts from the field of Maths, Financial Markets, AIML, Data Science & Management
✔ Each with over 25+ years of International experience working in EU / US / Australia
✔ All our trainers are Highly Qualified and Certified, in their respective subject areas

- You have hands on exp with Machine Learning / Hadoop technologies with a genuine interest in Deep Learning

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NB: All our trainings are always tailored to adopt to the Individual's Pace and Learning Depth.

NB: As a stepping stone, providing foundational knowledge, Bridge Courses are conducted periodically, to help students transition between different levels by closing knowledge gaps. These classes can be attended ad hoc, and are 'complimentary' for our bonafide students.

Kindly fill the DownloadPDF Form for the Brouchre with latest curriculum and full Training details.
Or you may Book an Appointment to collect your Brouchre and complete your registration.

This syllabus provides a structured, module-by-module breakdown of this comprehensive training program focused on participants overall performance, retention, and engagement, covering foundational theory, implementation, best industry practices and advanced techniques in the subject.

Module 1: Foundations of Deep Learning
✔ Introduction to AI, ML, DL
✔ Biological inspiration and artificial neurons
✔ Perceptron and multilayer perceptron (MLP)
✔ Activation functions: ReLU, Sigmoid, Tanh
✔ Loss functions and optimization basics
✔ Building a Simple Dense Network

Module 2: Training Neural Networks
✔ Forward and backward propagation
✔ Gradient descent and variants (SGD, Adam, RMSprop)
✔ Overfitting and underfitting
✔ Regularization techniques
✔ Hyperparameter tuning

Module 3: Convolutional Neural Networks
✔ Introduction to CNNs
✔ CNN architecture
✔ Advanced CNN Concepts
✔ Feature Maps and filters
✔ Transfer learning and pre-trained models
✔ Data augmentation techniques

Module 4: Recurrent Neural Networks
✔ RNN architecture and limitations
✔ Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
✔ Sequence Tasks with RNNs
✔ Time-series forecasting
✔ Text generation and sentiment analysis

Module 5: Advanced Architectures and Techniques
✔ Attention Mechanism and Transformers
✔ Advanced Regularization and Generalization
✔ Autoencoders and representation learning
✔ Generative Adversarial Networks (GANs)
✔ Deep reinforcement learning basics
✔ Hardware Acceleration and Distributed Training

Module 6: Frameworks and Tools
✔ Introduction to TensorFlow and PyTorch
✔ Model building, training, and evaluation workflows
✔ GPU acceleration and performance optimization
✔ Experiment tracking and version control

Module 7: Deployment and Real-World Applications
✔ Model serialization and export formats
✔ Serving models via REST APIs
✔ Edge deployment and mobile inference
✔ Inference Optimization
✔ Model Monitoring and MLOps Concepts

Module 8: Capstone Project
✔ Choose a real-world dataset and problem
✔ Design, train, and evaluate a deep learning model
✔ Document and present findings

NB:The curriculum is regularly subjected to updates, reflecting the latest industry trends & current technological advancements.

At Vyom Data Sciences, we aspire to provide the latest curriculum and most recent technology, as a standard component of all our trainings. Experts, with 25+ years of experience from USA, Europe and Australia, bring the best industry practices while designing and executing these trainings. All our trainers are Highly Qualified and Certified in their respective subject areas.

Kindly fill the DownloadPDF Form for the Brouchre with latest curriculum and full Training details.
Or you may Book an Appointment to collect your Brouchre.

Bhawana

Fabulous NLP + ML course

I have eleven plus years of experience taking training courses. I do not usually complete surveys.
Your instructor was excellent, the best I've experienced on a software subject, and I couldn't imagine him doing a better job of seamlessly walking students through a breadth of information for such complex subject like AI and ML. he did a fabulous job pacing everything and addressing student questions. I am very impressed.

Harish

Excellent ML course!

The course was well structured and easy to understand. Good pace of learning.
The institute believes to provide knowledge as well as guidance in detail to each & every student.
I completed my ML course from the institute. Their international exp does help a lot !
Thanks for the training sir.