Deep Learning Course Info
Introduction
Deep Learning is at the forefront of the AI revolution, transforming industries like healthcare, finance, and technology. From powering self-driving cars to enabling personalized medicine, Deep Learning is reshaping the future. Our 80-hour Deep Learning Course in Bangalore is meticulously designed to equip you with cutting-edge skills, hands-on experience, and industry-recognized certification. With 100% placement assistance and a proven track record of placing students in top MNCs like Google, Amazon, and Infosys, this course is your gateway to a thriving career in Artificial Intelligence and Machine Learning.
Key Features
Here’s why our Deep Learning Course in Bangalore stands out:
✅ 80 Hours of Intensive Training: Master Deep Learning concepts with a comprehensive curriculum designed by industry experts.
✅ Globally Recognized Certification: Earn a certification that adds value to your resume and boosts your career prospects.
✅ 100% Placement Assistance: Get placed in top MNCs like Google, Amazon, Infosys, and ITC with our dedicated placement support.
✅ Live Projects & Practicals: Gain hands-on experience by working on real-world projects in healthcare, finance, and e-commerce.
✅ Expert Trainers: Learn from industry professionals with years of experience in AI and Machine Learning.
✅ Flexible Batches: Choose from weekday or weekend batches to suit your schedule, whether you’re a student or a working professional.
Course Syllabus
Our Deep Learning Course in Bangalore is designed to provide a comprehensive understanding of Deep Learning concepts and their real-world applications. Here’s a breakdown of the curriculum:
1. Introduction to Deep Learning
Basics of Neural Networks
Applications of Deep Learning in industries like healthcare, finance, and e-commerce
Overview of AI and Machine Learning
2. Deep Learning with Python
Introduction to Python for Deep Learning
TensorFlow and Keras Basics
Building Your First Neural Network
3. Advanced Topics
Convolutional Neural Networks (CNNs) for image processing
Recurrent Neural Networks (RNNs) for sequential data
Generative Adversarial Networks (GANs)
4. Live Projects
Real-world projects in healthcare (e.g., disease prediction)
Finance (e.g., fraud detection)
E-commerce (e.g., recommendation systems)
Who Should Enroll
This course is ideal for:
Students: Aspiring AI/ML enthusiasts looking to build a strong foundation in Deep Learning and kickstart their careers.
Professionals: IT professionals, software developers, and data analysts aiming to upskill and transition into high-demand roles in AI and Machine Learning.
Career Switchers: Individuals from non-tech backgrounds seeking to enter the fields of Data Science, AI, and Deep Learning.
Tech Enthusiasts: Anyone passionate about cutting-edge technologies and eager to explore the world of Artificial Intelligence.
Benefits of the Course
Enrolling in our Deep Learning Course in Bangalore offers you a wealth of benefits to accelerate your career and skill development:
🚀 Career Opportunities: Land high-demand roles like Deep Learning Engineer, Data Scientist, AI Specialist, and Machine Learning Engineer in top MNCs.
🎓 Globally Recognized Certification: Earn a certification that validates your expertise and enhances your resume, making you stand out to employers.
💼 100% Placement Support: Get dedicated job assistance and placement opportunities in leading companies like Google, Amazon, Infosys, and ITC.
🛠️ Hands-On Experience: Work on live projects in industries like healthcare, finance, and e-commerce to build a strong portfolio and gain real-world experience.
👨🏫 Expert Guidance: Learn from industry professionals with years of experience in AI and Machine Learning.
📅 Flexible Learning: Choose from weekday or weekend batches to fit your schedule, whether you’re a student or a working professional.
🌟 Comprehensive Curriculum: Our course covers everything from the basics of Deep Learning to advanced topics like CNNs, RNNs, and GANs, ensuring you’re job-ready.
Call-to-Action (CTA)
Don’t miss out on the opportunity to transform your career with our Deep Learning Course in Bangalore!
This immersive program offers a deep dive into advanced deep learning techniques and their applications, equipping you with the knowledge and skills to become a proficient deep learning practitioner. Whether you’re a seasoned professional or a newcomer to the field, this course will empower you to harness the power of deep learning to solve complex real-world problems.
Deep Learning Course Content
Neural Networks Basics:
Perceptrons and Multi-Layer Perceptrons (MLPs).
Activation Functions: ReLU, Sigmoid, Tanh, Softmax.
Loss Functions: Mean Squared Error (MSE), Cross-Entropy Loss.
Gradient Descent and Backpropagation.
Optimization Techniques:
Stochastic Gradient Descent (SGD).
Momentum, RMSProp, Adam Optimizer.
Learning Rate Scheduling.
Convolutional Neural Networks (CNNs):
Convolutional Layers, Pooling Layers, Fully Connected Layers.
Popular Architectures: LeNet, AlexNet, VGG, ResNet, Inception.
Applications: Image Classification, Object Detection, Facial Recognition.
Recurrent Neural Networks (RNNs):
Vanilla RNNs, Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs).
Applications: Time Series Prediction, Natural Language Processing (NLP), Speech Recognition.
Generative Models:
Variational Autoencoders (VAEs).
Generative Adversarial Networks (GANs): DCGAN, CycleGAN, StyleGAN.
Applications: Image Synthesis, Data Augmentation, Art Generation.
Transformer Models:
Self-Attention Mechanism.
Transformer Architecture: Encoder-Decoder Structure.
- Applications: NLP (e.g., BERT, GPT), Image Processing (e.g., Vision Transformers).
Transfer Learning:
Fine-Tuning Pre-Trained Models (e.g., ResNet, BERT).
Applications: Domain Adaptation, Few-Shot Learning.
Reinforcement Learning with Deep Learning:
Deep Q-Networks (DQN).
Policy Gradient Methods.
Applications: Game Playing (e.g., AlphaGo), Robotics.
Unsupervised and Self-Supervised Learning:
Autoencoders.
Contrastive Learning (e.g., SimCLR, MoCo).
Applications: Clustering, Anomaly Detection.
Explainable AI (XAI):
Techniques: LIME, SHAP, Grad-CAM.
Applications: Interpreting Model Predictions, Debugging Models.
TensorFlow:
Building and Training Neural Networks.
TensorFlow Extended (TFX) for Production.
PyTorch:
Dynamic Computation Graphs.
TorchVision, TorchText, and TorchAudio Libraries.
Keras:
High-Level API for Rapid Prototyping.
Integration with TensorFlow.
Other Tools:
Hugging Face for NLP.
OpenCV for Computer Vision.
Weights & Biases for Experiment Tracking.
Computer Vision:
Image Classification, Object Detection, Semantic Segmentation.
Applications: Autonomous Vehicles, Medical Imaging.
Natural Language Processing (NLP):
Text Classification, Sentiment Analysis, Machine Translation.
Applications: Chatbots, Language Models (e.g., GPT, BERT).
Speech and Audio Processing:
Speech Recognition, Text-to-Speech, Audio Classification.
Applications: Virtual Assistants, Voice-Controlled Devices.
Healthcare:
Disease Prediction, Drug Discovery, Medical Imaging Analysis.
Applications: Cancer Detection, Personalized Medicine.
Finance:
Fraud Detection, Algorithmic Trading, Risk Assessment.
Applications: Credit Scoring, Portfolio Management.
Self-Supervised Learning:
Techniques for Learning from Unlabeled Data.
Applications: Pretraining Large Models.
Federated Learning:
Training Models on Decentralized Data.
Applications: Privacy-Preserving AI.
Neural Architecture Search (NAS):
Automating the Design of Neural Networks.
Applications: Efficient Model Design.
Quantum Machine Learning:
Combining Quantum Computing with Deep Learning.
Applications: Solving Complex Optimization Problems.
Data Preprocessing:
Handling Missing Data, Normalization, Augmentation.
Tools: Pandas, NumPy, Scikit-Learn.
Model Training and Evaluation:
Hyperparameter Tuning: Grid Search, Random Search, Bayesian Optimization.
Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC.
Deployment and Scaling:
Model Deployment with TensorFlow Serving, Flask, or FastAPI.
- Scaling Models with Distributed Training (e.g., Horovod).
Bias and Fairness:
Identifying and Mitigating Bias in Models.
Ensuring Fairness in AI Systems.
Privacy and Security:
Differential Privacy.
Adversarial Attacks and Defenses.
Environmental Impact:
Energy Consumption of Deep Learning Models.
Sustainable AI Practices.
