Deep Learning with Pytorch
Create State of the Art Neural Networks for Deep Learning with Meta's PyTorch Deep Learning Library!
Start for Free
What you get:
- 2 hours of content
- 18 Interactive exercises
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
Deep Learning with Pytorch
Start for Free
What you get:
- 2 hours of content
- 18 Interactive exercises
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
$99.00
Lifetime access
Start for Free
What you get:
- 2 hours of content
- 18 Interactive exercises
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
What You Learn
- Model evaluation
- Building your first neural network in Pytorch
- Classification with Pytorch
- Regression with Pytorch
- Introduction to Kaggle
Top Choice of Leading Companies Worldwide
Industry leaders and professionals globally rely on this top-rated course to enhance their skills.
Course Description
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.
Curriculum
- 2. 🧠Learning Approach1 Lesson 3 MinDeep Learning as a Concept3 min
- 3. 🔥Learning PyTorch29 Lessons 88 MinPyTorch: World of Tensors Read now3 minTensor Dimensions and Notation Read now2 minHow Tensors Are Constructed Read now2 minPyTorch and NumPy work well together Read now3 minMasking Operation Read now2 minCommon PyTorch functions Read now1 minCoding Exercise 1: Tensor Transformations and Operations Read now1 minCoding Exercise 1: Solution Read now1 minDevice Management: CPU or GPU Read now8 minCoding Exercise 2: Unleashing GPU Power with PyTorch Read now1 minCoding Exercise 2: Solution Read now1 minAutomatic Differentiation: Learning Mechanism Read now7 minCoding Exercise 3: The Magic of Autograd in PyTorch Read now1 minCoding Exercise 3: Solution Read now1 minDataset: How to Feed Data to Model Read now8 minCoding Exercise 4: Building Efficient Data Pipelines with PyTorch Read now1 minCoding Exercise 4: Solution Read now1 minBuilding a Neural Network with PyTorch Read now11 minCoding Exercise 5: Building Your First Neural Network in PyTorch Read now1 minCoding Exercise 5: Solution Read now1 minLoss Function: How Good/ Bad is your performance Read now7 minCoding Exercise 6: Choosing and Using Loss Functions in PyTorch Read now1 minCoding Exercise 6: Solution Read now1 minTraining Loop: Putting it all together Read now12 minCoding Exercise 7: Implementing a Simple Training Loop Read now1 minCoding Exercise 7: Solution Read now1 minModel Evaluation: Final Performance Report Read now6 minCoding Exercise 8: Applying and Interpreting Evaluation Metrics Read now1 minCoding Exercise 8: Solution Read now1 min
- 4. 🛠️Hands-On Session3 Lessons 10 MinRegression Problem Read now3 minClassification Problem Read now3 minDebugging and Best Practices Read now4 min
- 5. 🏆Kaggle Competition4 Lessons 10 MinIntroduction to Kaggle — Your Playground for Data Science Read now2 minHands-On Kaggle Regression Based Competition (Step-by-Step) Read now4 minHands-On Kaggle Classification Based Competition (Step-by-Step) Read now3 min🎓 Concluding Lesson Read now1 min
Topics
Course Requirements
- Basic Python proficiency
- Familiarity with NumPy
- Basic knowledge of linear algebra
- Fundamentals of calculus
- Understanding of machine learning basics
Who Should Take This Course?
Level of difficulty: Advanced
- Data analysts, data scientists, and engineers
- Students and researchers
- AI and ML enthusiasts
Exams and Certification
A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.
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