Radical Technologies
AI
★★★★★
(2,095 ratings)  50,000+ Student

AI WITH ADVANCED DEEP LEARNING

Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions. Deep learning AI is able to learn without human supervision, drawing from data that is both unstructured and unlabeled. Also known as deep neural learning or deep neural network.

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Radical Technologies
50,000+ English 60 hours Weekdays / Weekends Classroom / Online / Corporate
Online / Classroom

AI WITH ADVANCED DEEP LEARNING

IT Training Programme

Duration 60 hours
Batch Type Weekdays / Weekends
Mode of Training Classroom / Online / Corporate
Locations Pune, Bangalore, Kochi
Language English
Certification Globally Recognized
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What you'll learn

Understand core concepts and architecture from the ground up
Get hands-on with the tools used by working professionals
Build real-world projects you can add to your portfolio
Learn industry best practices and coding standards
Practice with real datasets and real-world scenarios
Prepare for certification and technical interviews
Work on collaborative, team-based exercises
Apply performance tuning and optimization techniques
Understand how the technology fits into a larger ecosystem
Complete assignments reviewed by mentors

Programme Overview

3 sections covering the complete curriculum — a single, progressive learning arc.

60 hours
Training Duration
3
Core Modules
29
Total Lessons
4.4
Average Rating
50K+
Students Trained
01

Foundations & Core Concepts

Get hands-on with the fundamentals and architecture — the building blocks for everything that follows.

Fundamentals Architecture Setup
02

Hands-On Practical Training

Work through real exercises and assignments designed to mirror what you will do on the job.

Practicals Assignments Labs
03

Real-World Projects

Apply what you have learned to end-to-end projects that go straight into your portfolio.

Projects Portfolio Case Studies
04

Advanced Techniques

Go beyond the basics with advanced concepts, integrations and production-grade practices.

Advanced Integration Best Practices
05

Ecosystem Integration

Understand how this technology connects with the broader tools and platforms used in the industry.

Ecosystem Tools Platforms
06

Performance & Interview Prep

Master optimization techniques and prepare for the technical interview questions employers actually ask.

Optimization Interview Prep Certification

Who is this programme for?

Whether you're already writing code, working with data, or supporting applications today — this programme is built to take you into a AI role.

Software Developers

Engineers who want to add this skill set to their toolkit

Analysts & Consultants

Professionals moving into a more technical, hands-on role

IT Professionals

System admins and support engineers upskilling into a new domain

Fresh Graduates

CS/IT graduates aiming for a job-ready technical role

Course Curriculum

3 sections  •  29 lessons  •  60 hours

01 Introduction

Nuts & Bolts

Introduction to Github & Kaggle
Introduction to Machine Learning Concepts
Mathematics of Artificial Neural Network.
Single neuron prediction model.

TensorFlow 2.x

Introduction to Google’s TensorFlow Framework for Deep Learning
Data types in TF, key data transformation Methods.
Implement TensorFlow data pipeline using Tfrecords and tf.data methods

Construct Deep Learning Network 

Construct a Deep Learning Model to predict an Image.
Details of Sequential vs functional API of TF Keras implementation.
Hyper Parameter tunning of model.
02 Image Processing

Image Classification

Convolution Neural Network
Advanced CNN Networks – AlexNet, Residual Networks (ResNet)
Implement ResNET model in Google Colab.

Transfer Learning

Introduction to TensorFlow Hub
Open Source labelling tools for custom data annotation.
Fine tune pre trained ResNet & Inception V4 models.

Object Detection & Image Segmentation

TF’s Object Detection API
FasterRCNN algorithm.
MaskRCNN for image segmentation
03 Text Processing

Text Classification

Introduction to Word Embeddings
Recurrent Neural Networks (RNN)
LSTM / Bi LSTM & GRU Networks

Text Extraction

Named Entity Extraction using spaCy library
Construct a custom NER model using BiLSTM netowrk
Hyper Parameter Tunning of BiLSTM and spaCy’s model

BERT – attention-based models

Introduction to BERT architecture
Introduction to Hugging face’s BERT methods
Fine Tune a Question & Answering Model on Custom data set
 

Tools & Technologies

Every tool listed here is installed, configured and used in a hands-on lab session.

Core Tools

Hands-On Labs

Practical Environment

Industry-Standard Tools

Real-World Setup

Guided Exercises

Skill Building

Sample Datasets

Practice Material

Practice & Projects

Mini Projects

Applied Practice

Assignments

Mentor Reviewed

Doubt Sessions

Live Support

Career Readiness

Resume Building

Career Support

Mock Interviews

Interview Prep

Certification Prep

Global Recognition

Deployment & Delivery

Production Practices

Real-World Ready

Best Practices

Industry Standards

29+
Hands-On Lessons
3
Core Modules
60 hours
Training Duration
100%
Practical Training

You don't just learn AI WITH ADVANCED DEEP LEARNING. You ship it.

Three major projects, each mirroring how production teams actually work — from guided foundations to a portfolio-ready capstone.

PROJECT // 01

Guided Foundation Project

Requirement Analysis

Guided Implementation

Mentor Review

Iteration

Foundation Beginner

Apply the fundamentals in a structured, mentor-reviewed project

Take the core concepts from the first half of the curriculum and apply them to a realistic scenario, with guidance and feedback from your mentor at every step.

Structured project brief
Step-by-step implementation
Mentor feedback and review
Documented outcome
Stack Core Concepts Best Practices
PROJECT // 02

Applied Practice Project

Scenario Design

Independent Build

Testing & Validation

Peer Review

Applied Intermediate

Build a more independent project mirroring real production scenarios

Work through a project that combines multiple concepts from the curriculum, closer to how work is actually structured on the job — less hand-holding, more ownership.

End-to-end implementation
Testing and validation
Documentation
Peer/mentor review
Stack Applied Skills Testing
PROJECT // 03

Capstone Project

Planning

End-to-End Build

Review & Refinement

Presentation

Capstone Advanced

Take a project from requirements to a polished, portfolio-ready deliverable

Your final project — plan, build, test and present a complete solution using everything covered in the curriculum, reviewed by mentors before you graduate.

Complete working solution
Presentation-ready documentation
Mentor sign-off
Portfolio-ready deliverable
Stack Full Curriculum Portfolio

All 3 projects go directly into your portfolio & resume — reviewed by mentors before you graduate.

See Sample Project Reports

Upcoming Batches

No upcoming batches scheduled right now. Enquire to get notified.

Why Radical Technologies

Live Online Training
  • Highly practical oriented training
  • Installation support on your system
  • 24/7 Email and Phone support
  • 100% Placement Assistance
  • Global Certification Preparation
  • Trainer-Student Interactive Portal
  • Assignments and Projects by Mentors
Live Classroom Training
  • Weekend / Weekdays / Morning / Evening batches
  • 80:20 Practical and Theory ratio
  • Real-life Case Studies
  • Easy make-up for missed sessions
  • PSI | Kryterion | Redhat Test Centers
  • Lifetime Video Classroom Access (coming soon)
  • Resume Prep and Mock Interviews
Self-Paced Training
  • Learn 300+ courses at your own time
  • 50,000+ Satisfied Learners
  • Course Completion Certificate
  • Practical Labs available
  • Mentor Support available
  • Doubt Clearing Session available
  • 10% Discounted Global Certification

Like the Curriculum? Let's Get Started

Join 50,000+ students already enrolled at Radical Technologies

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Global Certification

Radical Technologies is the leading IT certification institute in Pune, offering globally recognized certifications across various domains. With expert trainers and comprehensive materials, we ensure students gain in-depth knowledge and hands-on experience to excel in their careers. Our certification programs are tailored to meet industry standards — from cloud technologies to data science — empowering individuals to stay ahead in the ever-evolving tech landscape.

Certificate of Completion

Career Services

At Radical Technologies, we are committed to your success beyond the classroom. Our 100% Job Assistance program ensures that you are not only equipped with industry-relevant skills but also guided through the job placement process. With personalised resume building, interview preparation, and access to our extensive network of hiring partners, we help you take the next step confidently into your IT career.

Career Support

Course Completed? Need next steps?
Need Interview Supports?
Need Job Assistance?
Came from any other Institute?

Join our Brush-up Session & get support until you find a job!

Get Started

Radical Learning Eco-System

Exam Simulator

Cloud SandBox

Hands-on Cloud Lab

Developer Coding Ground

Student Reviews

4.4★
Average learner rating
50K+
Students trained
30+
Hiring companies alumni work at
100%
Placement assistance
4.4
★★★★★

Course Rating

★★★★★
62%
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21%
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10%
★★☆☆☆
4%
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3%
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Frequently Asked Questions

15 questions about the AI WITH ADVANCED DEEP LEARNING course.

01 What is Advanced Deep Learning AI?
Advanced Deep Learning AI refers to the use of complex neural network architectures and algorithms to solve sophisticated problems in AI. It focuses on deep learning techniques that involve large-scale neural networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), among others, to analyze vast datasets and make high-accuracy predictions in fields like computer vision, natural language processing, and autonomous systems.
02 What are the key prerequisites for learning Advanced Deep Learning AI?
To learn Advanced Deep Learning AI, it’s essential to have a strong foundation in linear algebra, calculus, probability, and statistics. You should also be proficient in programming languages, particularly Python, and have experience with machine learning algorithms and frameworks such as TensorFlow or PyTorch. Basic knowledge of deep learning concepts like neural networks, backpropagation, and gradient descent is also important.
03 What are the most common applications of Advanced Deep Learning AI?
Advanced Deep Learning AI has a wide range of applications, including:
Healthcare: Medical imaging, disease diagnosis, and drug discovery.
Autonomous Vehicles: Object detection, path planning, and vehicle control.
Natural Language Processing (NLP): Chatbots, sentiment analysis, and machine translation.
Finance: Fraud detection, risk management, and algorithmic trading.
Computer Vision: Image recognition, facial recognition, and video analysis.
04 What is the difference between deep learning and machine learning?
Deep learning is a subset of machine learning that uses neural networks with multiple layers (deep architectures) to model complex patterns in data. Machine learning, on the other hand, includes a broader range of algorithms like decision trees, support vector machines, and linear regression, which do not necessarily use neural networks or require deep architectures. Deep learning is particularly effective when dealing with large datasets and unstructured data like images, audio, and text.
05 Why is GPU usage important in deep learning?
GPUs (Graphics Processing Units) are critical for deep learning because they provide the parallel processing power needed to train large neural networks efficiently. Neural networks require significant computational resources for matrix operations, and GPUs can handle these operations much faster than traditional CPUs, dramatically reducing the time needed to train models.
06 How does transfer learning benefit deep learning models?
Transfer learning allows a pre-trained model, originally trained on a large dataset, to be fine-tuned for a new, smaller task. This is beneficial because it reduces the need for massive datasets and computing power when developing new models. It is particularly useful in applications where labeled data is scarce, allowing models to generalize better across different tasks.
07 What are some popular frameworks used in Advanced Deep Learning AI?
Several popular frameworks are commonly used in advanced deep learning:
TensorFlow: A widely used open-source framework developed by Google for building and deploying deep learning models.
PyTorch: Developed by Facebook, PyTorch is popular for research and experimentation due to its flexibility and dynamic computational graph.
Keras: A high-level API that runs on top of TensorFlow, simplifying the model-building process.
MXNet: Known for its scalability and efficiency, MXNet is often used in industry-level applications.
08 What are the challenges in training very deep neural networks?
Training deep neural networks can be challenging due to several issues:
Vanishing/Exploding Gradient Problem: During backpropagation, gradients can become too small (vanishing) or too large (exploding), making it difficult for the network to converge.
Overfitting: Deep networks are prone to memorizing the training data, leading to poor generalization on unseen data.
Computational Cost: Deep networks require significant computational resources and time for training, especially on large datasets
09 What is a Convolutional Neural Network (CNN), and where is it used?
A Convolutional Neural Network (CNN) is a type of deep learning model designed specifically for processing structured grid-like data, such as images. CNNs use convolutional layers to automatically learn features from raw image pixels, making them highly effective for tasks like image classification, object detection, and facial recognition.
10 What is the role of activation functions in deep learning?
Activation functions introduce non-linearity into a neural network, allowing it to learn complex patterns in data. Without activation functions, the network would behave as a linear model, limiting its ability to model real-world data. Common activation functions include ReLU (Rectified Linear Unit), Sigmoid, and Tanh, each with its specific properties and use cases.
11 What are Generative Adversarial Networks (GANs), and how are they used?
Generative Adversarial Networks (GANs) consist of two neural networks—the generator and the discriminator—competing against each other. The generator tries to create realistic data, while the discriminator attempts to distinguish between real and generated data. GANs are used in various applications, such as generating realistic images, video synthesis, and data augmentation.
12 How does reinforcement learning differ from deep learning?
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. While deep learning focuses on supervised or unsupervised learning from existing data, reinforcement learning involves learning through exploration and experience. Deep reinforcement learning combines both approaches by using deep neural networks to learn optimal policies for decision-making tasks.
13 What is the significance of model interpretability in deep learning?
Model interpretability refers to the ability to understand and explain the behavior of a model, including how it makes predictions. In deep learning, models are often seen as “black boxes” due to their complexity. However, interpretability is important, especially in high-stakes domains like healthcare and finance, where understanding the decision-making process is crucial for trust, regulatory compliance, and improving model performance.
14 What is hyperparameter tuning, and why is it important in deep learning?
Hyperparameter tuning is the process of adjusting the settings of a deep learning model (e.g., learning rate, batch size, and number of layers) to optimize its performance. It is essential because the choice of hyperparameters directly affects the model’s ability to learn from data. Effective hyperparameter tuning can significantly improve the accuracy, speed, and generalization capabilities of a model.
15 How does advanced deep learning contribute to AI ethics and fairness?
Advanced deep learning can address AI ethics and fairness by incorporating techniques that reduce bias, increase transparency, and ensure the responsible use of AI systems. For example, fairness-aware machine learning algorithms can mitigate bias in training data, while explainable AI (XAI) models provide insights into how decisions are made, promoting trust and accountability in AI applications.

AI WITH ADVANCED DEEP LEARNING Interview Questions

10 questions commonly asked in AI WITH ADVANCED DEEP LEARNING interviews.

  1. 01 Can you discuss the role of hyperparameter tuning in deep learning? What are the common strategies used to optimize hyperparameters, and how do you implement them?
  2. 02 How does transfer learning improve the performance of deep learning models, and what are the steps involved in fine-tuning a pre-trained model?
  3. 03 What are the key techniques for preventing overfitting in deep learning models, and how do they impact the model's generalization capabilities?
  4. 04 Can you explain the differences between Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), and in which scenarios would you use each?
  5. 05 How does backpropagation work in deep neural networks, and what are the challenges in implementing it for very deep architectures?
  6. 06 How would you approach designing a deep learning model for an unstructured dataset? What preprocessing steps are crucial to ensure the model's performance?
  7. 07 In deep learning, how do you decide which activation function to use in a neural network, and what are the pros and cons of using ReLU versus other activation functions?
  8. 08 What are Generative Adversarial Networks (GANs), and how are they trained? Can you provide examples of real-world applications where GANs are particularly effective?
  9. 09 Describe the architecture of a Transformer model. How does it differ from traditional RNNs and LSTMs in handling sequential data like text?
  10. 10 What is the vanishing gradient problem, and how do modern deep learning architectures address this issue in training deep networks?

Ai With Advanced Deep Learning Course Certification With Training In Pune

Radical Technologies is the premier institute in Bangalore, renowned for offering top-tier Deep Learning Courses, Training, and Certification programs. Our cutting-edge curriculum, designed by industry experts, ensures that learners gain a strong foundation in Deep AI Learning and are equipped to tackle real-world challenges. We offer both classroom-based Deep Learning Classes and flexible Deep Learning Online Courses, making it accessible to a wide range of students and professionals.

Our Advanced Deep Learning Courses are recognized as the Best Deep Learning Courses in Bangalore, providing comprehensive training from basic concepts to advanced techniques. Whether you’re looking to Master Deep Learning or earn a Deep Learning AI Certificate, we provide the Best Course for Deep Learning to suit your needs.

At Radical Technologies, we pride ourselves on delivering the most in-depth Deep Learning Training in Bangalore, covering everything from AI to neural networks, and providing practical, hands-on experience. Our institute also offers an industry-recognized Deep Learning Certification Course, giving you the credentials needed to excel in the rapidly evolving AI field. With a focus on both theoretical knowledge and practical application, our Deep Learning Certificate Program stands out as the Best Deep Learning Certification available today.

For professionals and organizations, we also offer Deep Learning Corporate Training to upskill teams in cutting-edge AI techniques. Our Deep Learning Full Course is designed to offer a deep dive into advanced topics, helping students and professionals alike gain mastery over AI technologies.

Join Radical Technologies in Bangalore and take the next step toward a future in AI with our highly sought-after Advanced Deep Learning Course and Deep Learning AI Courses.

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Accenture
Amazon
Avisys Services
Birlasoft
Capgemini
Catchpoint
Cognizant
Darwish Cybertech
DataVision
GiBots
Google
Groots Software
HCL Technologies
IBM
Info Gain
Infosys
ITCube Solutions
KPIT
L&T Infotech
Microsoft
Mphasis
mPhatek
Oracle
Quantbit Technologies
Saina Cloud
TCS
Tech Mahindra
Wipro
YASH Technologies
Zensar Technologies

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