The ‘Deep Learning: Convolutional Neural Networks in Python Course’ on Udemy is taught by Lazy Programmer Inc., one of the best Udemy instructors. The course talks about one of the most powerful deep-learning architectures – Convolutional Neural Networks (CNNs). The course covers the basics of convolution and its use in deep learning and NLP.
The course is suitable for candidates who know basic math (derivatives, matrix arithmetic, probability), Python, Numpy, and Matplotlib. The course is usually available for INR 2,799 on Udemy but students can click on the link and get the ‘Deep Learning: Convolutional Neural Networks in Python Course’ for INR 449.
Who all can opt for this course?
- Anyone with an interest in Deep Learning, Computer Vision, or NLP, including academics, professionals, and others.
- Data scientists and software engineers who wish to advance in their careers.
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 13.5 hours |
| Rating | 4.7/5 |
| Student Enrollment | 34,682 students |
| Instructor | Lazy Programmer Inc. https://www.linkedin.com/in/lazyprogrammerinc. |
| Topics Covered | Basics of machine learning and neurons, neural networks for classification and regression, CNN using Tensorflow 2, image classification in Tensorflow 2, etc. |
| Course Level | Advanced (Candidates must know basic Math, Python, Numpy, and Matplotlib) |
| Total Student Reviews | 5,140 |
Learning Outcomes
- Learn and figure out why convolution is useful for deep learning
- Understand the architecture of a convolutional neural network (CNN)
- Recognize and describe the convolutional neural network’s design (CNN)
- Use TensorFlow 2’s CNN function
- Use CNNs for difficult image recognition jobs
- Use CNNs to Text Classification via Natural Language Processing (NLP) (eg. Spam Detection, Sentiment Analysis)
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Welcome (34 minutes) | Introduction and Outline |
| Get Your Hands Dirty, Practical Coding Experience, Data Links | ||
| How to use Github & Extra Coding Tips (Optional) | ||
| Where to get the code, notebooks, and data | ||
| How to Succeed in this Course | ||
| 2. | Google Colab (44 minutes) | Intro to Google Colab, how to use a GPU or TPU for free |
| Uploading your own data to Google Colab | ||
| Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn? | ||
| 3. | Machine Learning and Neurons (02 hours 05 minutes) | Review Section Introduction |
| What is Machine Learning? | ||
| Code Preparation (Classification Theory) | ||
| Classification Notebook | ||
| Code Preparation (Regression Theory) | ||
| Regression Notebook | ||
| The Neuron | ||
| How does a model “learn”? | ||
| Making Predictions | ||
| Saving and Loading a Model | ||
| Suggestion Box | ||
| 4. | Feedforward Artificial Neural Networks (01 hour 36 minutes) | Artificial Neural Networks Section Introduction |
| Forward Propagation | ||
| The Geometrical Picture | ||
| Activation Functions | ||
| Multiclass Classification | ||
| How to Represent Images | ||
| Code Preparation (ANN) | ||
| ANN for Image Classification | ||
| ANN for Regression | ||
| 5. | Convolutional Neural Networks (02 hours 20 minutes) | What is Convolution? (part 1) |
| What is Convolution? (part 2) | ||
| What is Convolution? (part 3) | ||
| Why use 0-indexing? | ||
| Convolution on Color Images | ||
| CNN Architecture | ||
| CNN Code Preparation | ||
| CNN for Fashion MNIST | ||
| CNN for CIFAR-10 | ||
| Data Augmentation | ||
| Batch Normalization | ||
| Improving CIFAR-10 Results | ||
| 6. | Natural Language Processing (NLP) (46 minutes) | Embeddings |
| Code Preparation (NLP) | ||
| Text Preprocessing | ||
| CNNs for Text | ||
| Text Classification with CNNs | ||
| 7. | Convolution In-Depth (22 minutes) | Real-Life Examples of Convolution |
| Beginner’s Guide to Convolution | ||
| Alternative Views on Convolution | ||
| 8. | Convolutional Neural Network Description (27 minutes) | Convolution on 3-D Images |
| Tracking Shapes in a CNN | ||
| 9. | Practical Tips (11 minutes) | Advanced CNNs and how to Design your Own |
| 10. | In-Depth: Loss Functions (23 minutes) | Mean Squared Error |
| Binary Cross Entropy | ||
| Categorical Cross Entropy | ||
| 11. | In-Depth: Gradient Descent (54 minutes) | Gradient Descent |
| Stochastic Gradient Descent | ||
| Momentum | ||
| Variable and Adaptive Learning Rates | ||
| Adam (pt 1) | ||
| Adam (pt 2) | ||
| 12. | Setting Up Your Environment (FAQ by Student Request) (37 minutes) | Anaconda Environment Setup |
| How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow | ||
| 13. | Extra Help With Python Coding for Beginners (FAQ by Student Request) (01 hour 09 minutes) | Beginner’s Coding Tips |
| How to Code by Yourself (part 1) | ||
| How to Code by Yourself (part 2) | ||
| How to Uncompress a .tar.gz file | ||
| Proof that using Jupyter Notebook is the same as not using it | ||
| Python 2 vs Python 3 | ||
| Is Theano Dead? | ||
| 14. | Effective Learning Strategies for Machine Learning (FAQ by Student Request) (59 minutes) | How to Succeed in this Course (Long Version) |
| Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? | ||
| Machine Learning and AI Prerequisite Roadmap (pt 1) | ||
| Machine Learning and AI Prerequisite Roadmap (pt 2) | ||
| 15. | Appendix / FAQ Finale (08 minutes) | What is the Appendix? |
| BONUS |
Resources Required
- Candidate should have a basic understanding of probability, matrix arithmetic, and derivatives
- Matplotlib, Numpy, and Python
Featured Review
Rajeev Nath (5/5): After completing the course, I am impressed with the way he teaches. He explains everything with an example and in an easy way.
Pros
- Maretoshi H (5/5): Awesome lecture for convolution process! Visualizing how each layer is learning was really awesome!
- Runsheng Xu (5/5): Because of your excellent courses for both theory and coding, I get chances to join a lot of projects in our school lab.
- Yong Du (5/5): This course was great! It covers topics like convolution in a way that anyone can understand.
- Viaan Dubey (5/5): This course rocks, its the best way for anyone to learn about convolutional neural networks.
Cons
- BoshengAn (1/5): I have to spend hours to search for other tutorials to learn by myself.
About the Author
The instructor of this course is Lazy Programmer Inc. He works as an Artificial Intelligence and Machine Learning Engineer. With a 4.6 instructor rating and 1,41,144 reviews on Udemy, he offers 32 courses and has taught 5,12,747 students so far.
- Although he is recognized as a data scientist, big data engineer, and full stack software engineer, he currently spends the majority of his time as an artificial intelligence and machine learning engineer with an emphasis on deep learning.
- He earned his master’s degree in computer engineering with a focus on machine learning and pattern recognition more than ten years ago.
- His second master’s degree was in statistics with a focus on financial engineering.
- He is a data scientist and big data engineer with experience in online advertising and digital media (optimizing click and conversion rates and building data processing pipelines).
- He routinely uses big data technologies like Hadoop, Pig, Hive, MapReduce, and Spark.
- He has developed deep learning models for text modelling, image and signal processing, user behaviour prediction, and click-through rate estimation.
- He has also taught students at universities like Columbia University, NYU, Hunter College, etc.
Comparison Table
| Parameters | Deep Learning: Convolutional Neural Networks in Python | Deep Learning Prerequisites: Logistic Regression in Python | Natural Language Processing with Deep Learning in Python |
|---|---|---|---|
| Offers | INR 449 ( | INR 455 ( | INR 455 ( |
| Duration | 13.5 hours | 7 hours | 12 hours |
| Rating | 4.7/5 | 4.5 /5 | 4.5 /5 |
| Student Enrollments | 34,682 | 28,682 | 43,666 |
| Instructors | Lazy Programmer Inc. | Lazy Programmer Inc. | Lazy Programmer Inc. |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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