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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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.