Use CNN for Image Recognition, Computer vision using TensorFlow & VGGFace2! For Data Science, Machine Learning, and AI
What you'll learn
- The importance of Convolutional Neural Networks (CNNs) in Data Science.
- The reasons to shift from hand engineering (classical computer vision) to CNNs.
- The essential concepts from the absolute beginning with comprehensive unfolding with examples in Python.
- Practical explanation and live coding with Python.
- An overview of concepts of Deep Learning theory.
- Evolutions of CNNs from LeNet (1990s) to MobileNets (2020s).
- Deep details of CNNs with examples of training CNNs from scratch.
- TensorFlow (Deep learning framework by Google).
- The use and applications of state-of-the-art CNNs (with implementations in state-of-the-art framework TensorFlow) that are much more recent and advanced in terms of accuracy and efficiency.
- The use and applications of state-of-the-art pre-trained CNNs (with implementations in state-of-the-art framework TensorFlow) for transfer learning on your own dataset.
- Building your own applications for human Face Verification and Neural Style Transfer.
- No prior knowledge is needed. You start from the basics and slowly build your knowledge in the subject.
- A willingness to learn and practice.
Comprehensive Course Description:
Convolutional Neural Networks (CNNs) are considered as game-changers in the field of computer vision, particularly after AlexNet in 2012. And the good news is CNNs are not restricted to images only. They are everywhere now, ranging from audio processing to more advanced reinforcement learning (i.e., Resnets in AlphaZero). So, the understanding of CNNs becomes almost inevitable in all the fields of Data Science. Even most of the Recurrent Neural Networks rely on CNNs these days. So, keeping all these concerns in parallel, with this course, you can take your career to the next level with an expert grip on the concepts and implementations of CNNs in Data Science.
The course ‘Mastering Convolutional Neural Networks, Theory and Practice in Python, TensorFlow 2.0’ is crafted to reflect the in-demand skills in the marketplace that will help you in mastering the concepts and methodology with regards to Python. The course is:
- Easy to understand.
- Practical with live coding.
- Rich with state-of-the-art and recently discovered CNN models by the champions in this field.
This course has been designed for beginners. However, we will go far deep gradually.
Also, this course is a quick compilation of all the basics, and it encourages you to press forward and experience more than what you have learned. By the end of every module, you will work on the assigned Homework/tasks/activities, which will evaluate / (further build) your learning based on the previous concepts and methods. Several of these activities will be coding-based to get you up and running with implementations.
Data Science is certainly a rewarding career that not only allows you to solve some of the most interesting problems, but also offers you a handsome salary package. With a core understanding of CNNs, you can back up your business and ensure emerging career growth.
Unlike other courses, this comprehensive course is relatively inexpensive – in fact, you can learn the concepts and methodologies of CNNs with Data Science at a fraction of the cost. Our tutorials are divided into 75+ short HD videos along with detailed code notebooks.
So, get started with the course and embrace yourself with the knowledge that waits for you.
Teaching is our passion:
We work hard to create online tutorials with the best possible guide who could help you in mastering the concepts. We aim to create a solid basic understanding for you before moving onward to the advanced version. High-quality video content, meaningful course material, evaluating questions, course notes, and handouts are some of the perks that you will get. You can approach our friendly team in case of any queries.
The in-depth course consists of the following topics:
- a. What can a Convolutional Neural Network (CNN) do?
- i. Real-world applications
- ii. CNNs in Reinforcement Learning: AlphaGo
- b. When to model CNN?
- i. Images
- ii. Videos
- iii. Speech
- a. Image Processing
- i. Image Blurring
- ii. Image sharpening
- iii. General Image Filtering
- iv. Convolution Operation
- v. Edge detection
- vi. Parametric shape detection
- vii. Exercises
- b. Object Detection
- i. Image blocks
- ii. Sliding Window
- iii. Feature Extraction
- iv. Classification
- v. Shift Invariance
- vi. Scale Invariance
- vii. Rotation Invariance
- viii. Person Detection: A Case Study
- ix. Exercises
- a. Perceptron
- i. Convolution
- ii. Bias
- iii. Activation
- iv. Loss
- v. Back Propagation
- vi. Exercises
- b. Multilayered Perceptron
- i. Why multilayered architecture?
- ii. Universal approximation theorem
- iii. Overfitting in DNNs
- iv. Early stopping
- v. Dropout
- vi. Stochastic Gradient Descent
- vii. Mini Batch Gradient Descent
- viii. Batch Normalization
- ix. Optimization algorithms
- x. Exercises
- 4. Convolutional Neural Networks (CNNs)
- a. Architecture of a CNN
- i. Filters
- ii. Strides
- iii. Paddings
- iv. Volumes
- v. Pooling
- vi. Tensors
- vii. Exercises
- b. Gradient descent in CNN
- i. Derivatives
- ii. Backpropagation
- iii. Worked Example
- iv. Implementing a CNN in NumPy
- v. Exercises
- c. Introduction to TensorFlow
- i. Implementing CNNs in TensorFlow
- ii. Exercises
- d. Classical CNNs
- i. LeNet
- ii. AlexNet
- iii. InceptionNet
- iv. GoogLeNet
- v. Resnet
- vi. Exercises
- e. Transfer Learning
- i. What is transfer learning?
- ii. When is it possible?
- iii. Practical techniques for transfer learning
- iv. Implementation of transfer learning using TensorFlow-hub
- v. Exercises
- f. YOLO: A Case Study
- a. Neural Style Transfer (using TensorFlow-hub)
- b. Face Verification (using VGGFace2)
- Understand the methodology of CNNs with Data Science using real datasets.
- Relate the concepts and theories in computer vision with CNNs.
Who this course is for:
- Beginners in Data Science and Deep Learning
- People who want to learn CNNs with real datasets in Data Science.
- People who want to learn CNNs along with its implementation in realistic projects.
- People who want to master their data speak.