Saquib Rahman K.
After 365:
Data Scientist | EXL
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Online Course
free
Deep Learning with Pytorch
with
Gaurav Sarkar
Create State of the Art Neural Networks for Deep Learning with Meta's PyTorch Deep Learning Library!
1,135 students already enrolled
Skill level:
Advanced
Duration:
2 hours
-
Lessons (2 hours)
- Lessons (2 hours)
CPE credits:
2.5
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited
certificate
What you learn
- Build your first neural network using PyTorch.
- Perform regression tasks with PyTorch models.
- Implement classification models in PyTorch.
- Evaluate model performance effectively.
- Learn how to use Kaggle for projects.
Topics & tools
Deep LearningPytorchNeural NetworksPythonKaggleMachine and Deep Learning
Your instructor
AI Solution Engineer at Intel | Hugging Face Fellow | Kaggle Expert
Track record
Bringing real-world expertise from leading global companies
Academic background
Master's degree, Computer Science
Course OVERVIEW
Description
CPE Credits: 2.5
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
Ready to master deep learning with PyTorch? This course takes you from foundational concepts to hands-on neural network deployment, designed to provide you with practical skills and real-world experience.
Dive into the world of tensors, understand dimensions, notation, and tensor construction. Seamlessly integrate PyTorch with NumPy and leverage common PyTorch functions. Master GPU acceleration to dramatically enhance computational performance. Explore automatic differentiation and PyTorch’s powerful autograd system. Build efficient data pipelines and construct your first neural networks. Implement essential components like loss functions, training loops, and evaluation metrics with hands-on coding exercises.
Tackle real-world regression and classification problems. Learn debugging techniques and best practices for developing reliable models.
Get introduced to Kaggle, the global playground for data science. Step-by-step guidance to participate effectively in regression and classification competitions.
Solidify your skills and prepare to confidently apply deep learning to practical challenges.
Prerequisites
- Python (version 3.8 or later), PyTorch library, and a code editor or IDE (e.g., Jupyter Notebook, VS Code, or Google Colab)
- Intermediate Python and machine learning knowledge is required.
- Familiarity with NumPy and neural network fundamentals is recommended.
Advanced preparation
Curriculum
41 lessons
18 exercises
1 exam
- 2. 🧠Learning Approach3 min3 minDeep Learning as a Concept FreeExercise Free
- 3. 🔥Learning PyTorch88 min88 minPyTorch: World of Tensors FreeTensor Dimensions and Notation FreeExercise FreeHow Tensors Are Constructed FreePyTorch and NumPy work well together FreeExercise FreeMasking Operation FreeCommon PyTorch functions FreeExercise FreeCoding Exercise 1: Tensor Transformations and Operations FreeCoding Exercise 1: Solution FreeExercise FreeDevice Management: CPU or GPU FreeCoding Exercise 2: Unleashing GPU Power with PyTorch FreeCoding Exercise 2: Solution FreeExercise FreeAutomatic Differentiation: Learning Mechanism FreeCoding Exercise 3: The Magic of Autograd in PyTorch FreeCoding Exercise 3: Solution FreeExercise FreeDataset: How to Feed Data to Model FreeCoding Exercise 4: Building Efficient Data Pipelines with PyTorch FreeCoding Exercise 4: Solution FreeExercise FreeBuilding a Neural Network with PyTorch FreeCoding Exercise 5: Building Your First Neural Network in PyTorch FreeCoding Exercise 5: Solution FreeExercise FreeLoss Function: How Good/ Bad is your performance FreeCoding Exercise 6: Choosing and Using Loss Functions in PyTorch FreeCoding Exercise 6: Solution FreeExercise FreeTraining Loop: Putting it all together FreeCoding Exercise 7: Implementing a Simple Training Loop FreeCoding Exercise 7: Solution FreeExercise FreeModel Evaluation: Final Performance Report FreeCoding Exercise 8: Applying and Interpreting Evaluation Metrics FreeCoding Exercise 8: Solution FreeExercise Free
- 4. 🛠️Hands-On Session10 min10 minRegression Problem FreeClassification Problem FreeDebugging and Best Practices Free
- 5. 🏆Kaggle Competition10 min10 minIntroduction to Kaggle — Your Playground for Data Science FreeHands-On Kaggle Regression Based Competition (Step-by-Step) FreeHands-On Kaggle Classification Based Competition (Step-by-Step) Free🎓 Concluding Lesson Free
- 6. Course exam60 min60 minCourse exam Free
Free lessons
1.1 Meet your instructor
1 min
1.2 Why PyTorch for Deep Learning
2 min
1.3 Learning Objectives
2 min
1.4 PyTorch vs. TensorFlow
4 min
2.1 Deep Learning as a Concept
3 min
3.1 PyTorch: World of Tensors
3 min
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96%
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365 Data Science.
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ACCREDITED certificates
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