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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!

🚀 Enroll Now to Start Your Deep Learning Journey!

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.

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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.

 

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