| Course Name | Duration | Classes | Total Fee | Mode of Training | Class Timing |
|---|---|---|---|---|---|
|
Deep Learning
Course |
3 Months |
60 |
60,000 PKR |
Online & Face-to-Face |
To be decided mutually with students |
Deep Learning Training Certification Course
In addition to validating your technical skills, Deep Learning Certification can help you advance your expertise. Once you are Deep Learning Certified, you’ll be eligible for perks that help you show off your achievements and keep learning. Register for exams and claim benefits at Technocation training.
Technocation provides an excellent faculty and qualified developers as there is a remarkable prospect in this field. One can make his/her Career with the help of both Deep Learning Training and establish an identity and get guidance in Rawalpindi.
Therefore, we aim to shape inspiring students with in-depth training to meet the requirements of the IT industry and build substantial grounds in Deep Learning Training by exhibiting students with various projects. Technocation also bestows the Best Deep Learning Training Course in Rawalpindi, Islamabad.
We guide people from every background to change their lives via our career-oriented short-term courses in Rawalpindi. Our evening and online course primarily focus on school, college, university students, and full/part-time employees.
Advantages of Deep Learning
- High-Demand Skills
- Better Job Opportunities
- Career Growth
- Enhances Problem-Solving and Analytical Skills
- Opportunities for Continuous Learning
- Empowerment and Confidence
- Business Growth and Innovation
Deep Learning Training Certification Course Outline
A Deep Learning course helps you to learn and develop smart AI models and automation tools.
Module 1: Introduction to Deep Learning
- What is Deep Learning?
- Difference Between Machine Learning & Deep Learning
- Applications of Deep Learning (NLP, Computer Vision, Robotics)
- Overview of Deep Learning Frameworks (TensorFlow, PyTorch, Keras)
Module 2: Fundamentals of Neural Networks
- Understanding Perceptrons & Artificial Neurons
- Activation Functions (Sigmoid, ReLU, Tanh, Softmax)
- Feedforward Neural Networks (FNN)
- Backpropagation & Gradient Descent
Module 3:Building Deep Neural Networks
- Designing & Training Deep Learning Models
- Hyperparameter Tuning (Learning Rate, Batch Size, Optimizers)
- Regularization Techniques (Dropout, L1/L2 Regularization)
- Model Evaluation Metrics (Loss Functions, Accuracy, Precision, Recall)
Module 4: Convolutional Neural Networks (CNNs)
- Introduction to Image Processing & Convolution
- CNN Architecture (Convolution, Pooling, Fully Connected Layers)
- Transfer Learning & Pretrained Models (ResNet, VGG, Inception)
- Image Classification & Object Detection
Module 5: Recurrent Neural Networks (RNNs) & Time-Series Analysis
- Understanding Sequence Data & Temporal Dependencies
- RNN Architecture & Backpropagation Through Time (BPTT)
- Long Short-Term Memory (LSTM) & Gated Recurrent Units (GRUs)
- Applications in Stock Market Prediction & Speech Recognition
Module 6: Natural Language Processing (NLP) with Deep Learning
- Introduction to Word Embeddings (Word2Vec, GloVe)
- Transformers & Attention Mechanism
- Implementing NLP Tasks (Text Classification, Sentiment Analysis, Chatbots)
- Using Pretrained Models (BERT, GPT, T5)
Module 7: Generative Models & Advanced Architectures
- Autoencoders & Variational Autoencoders (VAEs)
- Generative Adversarial Networks (GANs) & Deepfakes
- Deep Reinforcement Learning & Applications
- Ethical Considerations in Generative AI
Module 8: Optimization, Deployment & Scalability
- Model Optimization Techniques (Quantization, Pruning, Knowledge Distillation)
- Deploying Deep Learning Models (Flask, FastAPI, TensorFlow Serving)
- Running Models on Edge Devices (Mobile, IoT, Raspberry Pi)
- Scaling Deep Learning with Distributed Computing & Cloud Platforms
Final Module :Capstone Project & Certification
- Hands-on Deep Learning Project (Healthcare, Finance, Autonomous Vehicles, etc.)
- Model Interpretation & Explainability (SHAP, LIME)
- Building a Deep Learning Portfolio
- Course Completion Certification
Experience and Inspiring Trainers:
Our trainers bring their years of industry experience during the course. They are expert and passionate about delivering inspiring training as they know training inside out. They will advise you on all the options to make sure you get the best possible result.
Real-time Practice and Projects:
Our deep learning course is comprehensive and practical. Moreover we work on industry related projects.
Prerequisites for Deep Learning Course:
- Mathematics Basics: Linear Algebra, Calculus, Probability, and Statistics are useful.
- Programming Skills: Python is highly recommended (basic syntax, loops, functions).
- Machine Learning Basics: Understanding supervised & unsupervised learning helps.
- Basic Knowledge of Neural Networks: Knowing how neurons work can be beneficial.
- Basic Understanding of AI Concepts: Knowing AI applications and use cases is a plus.
- No prior experience in Deep Learning is required, but having these basics will help in grasping concepts faster.
Certificate:
Finally completing this training you will receive a course completion certificate along with internship in Deep Learning Training so you can get recognition for your new skills.
Course Material:
Softy Copy notes are briefly included in this course
Support and Careers Advice:
In the end our trainers are always ready to help you for any problems or question regarding Deep Learning. We prepare students for facing Interview questions on Deep Learning and help them to build their online resume. Our more than 90% students are placed in good MNCs.
“Our Student Success is Our Mission”.
