Next cohort begins on 1st September 2025 | Submit your application today | Top 40 applications will be accepted for next cohort | Best 3 candidates in each Cohort gets 100% sponsored mentorship.
Deep Learning
Transform your career in just 10 weeks with Deep Learning
Limited Batch
Live Data
Real Client
On Job Experience
Who Can Benfit From The Program?
Business Intelligence Skills Using Power BI Can Benefit A Wide Range Of Individuals And Organizations Including:
Students: Aiming a career in Data Science
Working Professional: Wants to transform over to Data Science Domain.
Freelancer: Looking for opportunities to upskill and network in Analytics Domain
Human Resource Professionals: Who want to analyze employee data to identify trends and opportunities for improvement.
Finance Professionals: Who want to track and analyze financial data to make better business decisions.
Business Managers: For intelligent decision making in their operations.
Data Analysts: Who are upskilling their business analytical skills.
Enrepreneurs & C-Level Executives: Wanting to scale their business using BI strategies.
Sales Professionals: Who want to analyze sales data to identify trends and opportunities.
Marketing Professionals: Who want to track and analyze marketing data to improve campaign performance.
Introduction to Deep Learning
Overview of Deep Learning: Key concepts and historical development.
Importance: How deep learning differs from traditional machine learning.
Neural Networks Basics
Structure: Neurons, layers, and connections.
Activation Functions: Sigmoid, ReLU, and softmax functions.
Training Neural Networks
Forward and Backward Propagation: Understanding how networks learn.
Loss Functions and Optimization: Gradient descent, cross-entropy loss.
Deep Learning Architectures
Feedforward Networks: Structure and applications.
Convolutional Neural Networks (CNNs): For image processing tasks.
Recurrent Neural Networks (RNNs)
Sequential Data Handling: Time series, text, and speech data.
Variants: Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU).
Regularization Techniques in Deep Learning
Overfitting and Underfitting: Identification and mitigation.
Techniques: Dropout, batch normalization, and data augmentation.
Deep Learning Frameworks
Popular Libraries: TensorFlow, PyTorch, and Keras.
Building and Training Models: Basic implementation using these frameworks.
Advanced Deep Learning Architectures
Generative Adversarial Networks (GANs): Understanding generative models.
Transformers: For NLP tasks like language modeling and translation.
Applications of Deep Learning
Computer Vision: Object detection, facial recognition.
Natural Language Processing: Sentiment analysis, machine translation.
Challenges and Future Directions
Challenges: Data requirements, interpretability, and computational costs.
Future Trends: Explainability, AI ethics, and emerging architectures.
Program Certificate
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Program Certificate
Meet Our Experts
VINAY BORHADE
Founder | Chief Data Scientist
MOHIT JAIN
PM | Data Scientist
AKASH POL
AI Mentor
How It Works?
1.Application Process
Apply for Program via dedicated link to show your interest
