“Deep Learning with PyTorch” is categorized as one of the best courses for experienced programmers. Students will be able to build their neural network using Deep Learning techniques in PyTorch. The courses are usually available at INR 1,999 on Udemy but you can click now to get 77% off and get Deep Learning with PyTorch Course for INR 455.
This course focuses on training the programmer’s RNN model from scratch for text generation. Students can learn to Perform reinforcement learning to solve OpenAI’s Cartpole task.
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 455 ( |
| Duration | 4.5 hours |
| Rating | 4.0/5 |
| Student Enrollment | 292 students |
| Instructor | Packt Publishing |
| Topics Covered | Loss Functions in PyTorch, Convolutional Neural Networks, Loading and Using MNIST Dataset, Recurrent Neural Networks |
| Course Level (Resources Required) | Advanced |
| Total Student Reviews | 54 |
| Merits |
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| Shortcomings |
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Learning Outcomes
- Understand PyTorch and Deep Learning concepts
- Build your neural network using Deep Learning techniques in PyTorch.
- Perform basic operations on your dataset using tensors and variables
- Build artificial neural networks in Python with GPU acceleration
- See how CNN works in PyTorch with a simple computer vision example
- Train your RNN model from scratch for text generation
- Use Auto Encoders in PyTorch to remove noise from images
- Perform reinforcement learning to solve OpenAI’s Cartpole task
- Extend your knowledge of Deep Learning by using PyTorch to solve your own machine learning problems
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Getting Started with PyTorch (1 hour 7 mins) | Introduction to PyTorch |
| Installing PyTorch on Linux and Windows | ||
| Installing CUDA | ||
| Introduction to Tensors and Variables | ||
| Working with PyTorch and NumPy | ||
| Working with PyTorch and GPU | ||
| Handling Datasets in PyTorch | ||
| Deep Learning Using PyTorch | ||
| 2. | Training Your First Neural Network (31 mins) | Loss Functions in PyTorch |
| Optimizers in PyTorch | ||
| Training the Neural Network | ||
| Saving and Loading a Trained Neural Network | ||
| Training the Neural Network on a GPU | ||
| 3. | Computer Vision – CNN for Digita Recognition (1 hour 27 mins) | Convolutional Neural Networks |
| The Convolution Operation | ||
| Concepts – Strides, Padding, and Pooling | ||
| Loading and Using MNIST Dataset | ||
| Building the Model | ||
| Training and Testing | ||
| 4. | Sequence Models – RNN for Text Generation (47 mins) | Word Embedding |
| Recurrent Neural Networks | ||
| Building a Text Generation Model in PyTorch | ||
| Training and Testing | ||
| 5. | Autoencoder- Denoising Images (28 mins) | How Autoencoders Work |
| Types of Autoencoders | ||
| Building Denoising Autoencoder Using PyTorch | ||
| Training and Testing | ||
| 6. | Reinforcement Learning – Balance Cartpole Using DQN (48 mins) | Reinforcement Learning Concepts |
| DQN, Experience Replay | ||
| The OpenAI Gym Environment | ||
| Building the Cartpole Agent Using DQN | ||
| Training and Testing |
Resources Required
- Python programming knowledge and basic math skills (matrix/vector manipulation, simple probability calculations) are required.
Comparison Table
| Parameters | Deep Learning with PyTorch | Advanced AI: Deep Reinforcement Learning in Python | Artificial Intelligence: Reinforcement Learning in Python |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 4.5 hours | 10.5 hours | 14.5 hours |
| Rating | 4.0/5 | 4.7/5 | 4.8/5 |
| Student Enrollments | 292 | 35,000 | 42,000 |
| Instructors | Packt Publishing | Lazy Programmer Team | Lazy Programmer Team |
| Level | Advanced | Advanced | Advanced |
| Topics Covered | Loss Functions in PyTorch, Convolutional Neural Networks, Loading and Using MNIST Dataset, Recurrent Neural Networks | Elements of a Reinforcement Learning Problem, Random Search, RBF Networks with CartPole, Policy Gradient in TensorFlow for CartPole | From Bandits to Full Reinforcement Learning, MDP Section Introduction, The Bellman Equation, Dynamic Programming Section Introduction |
| Coding Exercises | Yes | Yes | Yes |
| Projects | No | No | Yes |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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Student Reviews
Check out the student reviews for the Deep Learning with PyTorch course,
- Isaac Andrew Song B. (5.0/5) “Quite good so far, but I am not happy with two things: 1. Not enough on text-based classifiers. 2. Not enough GPU acceleration speedups in performance, the communication overheads were huge for the GPU code mentioned in the talks”
- Liana (5.0/5) “I understand how CNN works now. I would strongly recommend anybody who is interested in CNN to take this course first. Follow the explanations folks, this course is money very well spent!”
- Yaron R. (5.0/5) “This is a very good course, it passes over most of the deep learning different kind of neural networks. This pass is very detailed and comprehensive, but to my opinion, you have to be a little bit familiar with the subject in order to understand. I recommend this course.”
- Ganapathy R. (5.0/5) “Excellent”
- Mitul S. (4.0/5) “A Good course for anyone who wishes to quickly get familiar with PyTorch for Deep Learning applications. Concise and well-organized course. Thanks a lot, Anand Saha for such an excellent course.”
- Pavel K. (4.0/5) “A great introductory course. However, the PyTorch version is not up to date.”
- Phil A. (4.0/5) “Dude, do not glimpse over setup. If you set something up, tell us. I got distracted while trying to get the Iris data into my collabs- You did not show how you got it and your code did not work. I went through a similar thing with a torch. and utils. and data. I got these to work but it took away attention from learning.”
- John P. (3.0/5) “Courses on technology quickly get outdated and this was certainly the case for this course I was finding that the code for all of the exercises didn’t run (except for Section 6 which ran just fine). Either the videos need to be updated or the course should have significant addenda to help people who are running the latest versions of Anaconda, PyTorch, CUDA, etc.”
- Atharva P. (3.0/5) “The instructor should give clear explanations of what is he teaching. Data preprocessing is not done not don but rather skipped. The instructor should explain each line of code, as is required by beginners. Mistake enrolling here.”
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Deep Learning with PyTorch FAQs
Ques. What is the fee for the course?
Ans. The course is originally priced at INR 1,999 but currently, it is available for INR 455.
Ques. What will I learn in the course?
Ans. This course focuses on training the programmer’s RNN model from scratch for text generation. Students can learn to Perform reinforcement learning to solve OpenAI’s Cartpole task.
Ques. What is the duration of the course?
Ans. 4.5 Hours
Ques. Is there a certification from Udemy?
Ans. Yes, you will get a certificate of completion from Udemy
Ques. What is the rating?
Ans. 4.0/5
Ques. Do I have lifetime access to this course?
Ans. Yes, you can access this course for a lifetime on mobile or TV.
Ques. Can I access the course on mobile devices, laptops, and TV?
Ans. Yes, you can access the course on mobile devices, laptops, and TV.
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