Requirements
- Comfortable with Python (functions, loops, NumPy/Pandas basics)
- Basic Machine Learning concepts (train/test split, overfitting, metrics)
- Math basics: linear algebra (vectors/matrices) + probability (mean/variance)
- Laptop: i5/Ryzen 5+, 8–16GB RAM (GPU optional; we guide cloud/GPU setup)
Features
- Live Project-Based Training
- Expert-Led Sessions
- Flexible Learning Options
- Interactive Learning
- Comprehensive Study Material
- Globally Recognized Certification
- One-on-One Mentorship
Target audiences
- BTech/BSc/MCA students aiming for AI/ML roles
- Data analysts/scientists moving into deep learning
- Software engineers building AI features (vision/NLP/recommendations)
- Working professionals upskilling for ML/AI projects
- Researchers and enthusiasts who want practical model-building skills
If you already know the basics of Machine Learning and want to build real AI models, this Deep Learning Training at Ascents Learning is the next practical step. We keep it workshop-style: you code, test, fix, and ship models—just like you would in a real project.
In this Deep Learning Training, you’ll learn how neural networks actually learn (forward pass, backprop, loss curves), then move into the models companies use every day: CNNs for images, sequence models for text, and modern Transformers. You’ll work on projects like image classification, text sentiment analysis, and time-series forecasting, so your portfolio shows more than “Hello World” notebooks.
We use industry tools (Python, Jupyter, PyTorch/TensorFlow, GPU workflows) and focus on clean training pipelines—data prep, evaluation, tuning, and deployment basics. By the end of this Deep Learning Training, you should be able to read a model architecture, train it properly, debug overfitting, and explain results clearly in interviews.
Want to map this to job roles like Deep Learning Engineer, ML Engineer, or AI Developer? Ascents Learning also supports resume/LinkedIn and mock interview prep.
Call +91-921-780-6888 or visit www.ascentslearning.com
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Curriculum
- 15 Sections
- 0 Lessons
- 10 Weeks
- Module 1: Deep Learning Foundations0
- Module 2: Python Stack for Deep Learning0
- Module 3: Math Refresher (Practical)0
- Module 4: Neural Network Building Blocks0
- Module 5: Forward Pass + Backprop (Core Intuition)0
- Module 6: Training Pipeline Basics0
- Module 7: Optimizers + Learning Rate Tuning0
- Module 8: Regularization + Generalization0
- Module 9: Data Handling + Preprocessing0
- Module 10: Introduction to CNNs (Computer Vision)0
- Module 11: Advanced CNN Techniques + Transfer Learning0
- Module 12: Sequence Learning (RNN, LSTM, GRU)0
- Module 13: Transformers + Modern NLP Basics0
- Module 14: Model Evaluation + Debugging Skills0
- Module 15: Deployment Basics + Capstone Project0