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Deep Learning CNN: Convolutional Neural Networks with Python

Deep Learning CNN: Convolutional Neural Networks with Python

Deep Learning CNN: Convolutional Neural Networks with Python

Deep Learning CNN: Convolutional Neural Networks with Python, Use CNN for Image Recognition, Computer vision using TensorFlow & VGGFace2! For Data Science, Machine Learning, and AI

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Created by AI Sciences

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Description

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.


Exhaustive.


Expressive.


Practical with live coding.


Rich with state-of-the-art and recently discovered CNN models by the champions in this field.


How is this course different?


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.




Course Content:


The in-depth course consists of the following topics:




1. Motivations


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


2. Classical Computer Vision Techniques


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


3. Deep Neural Networks: An overview


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


5. Projects:


a. Neural Style Transfer (using TensorFlow-hub)


b. Face Verification (using VGGFace2)




After completing this course successfully, you will be able to:


Understand the methodology of CNNs with Data Science using real datasets.


Relate the concepts and theories in computer vision with CNNs.


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