CNN for Computer Vision with Keras and TensorFlow in Python

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Description : 

You're looking for an entire Convolutional Neural Network (CNN) course that teaches you everything you would like to make a Image Recognition model in Python, right?

You've found the proper Convolutional Neural Networks course!

After completing this course you'll be able to:

Identify the Image Recognition problems which may be solved using CNN Models.

Create CNN models in Python using Keras and Tensorflow libraries and analyze their results.

Confidently practice, discuss and understand Deep Learning concepts

Have a transparent understanding of Advanced Image Recognition models like LeNet, GoogleNet, VGG16 etc.

How this course will help you?

A Verifiable Certificate of Completion is presented to all or any students who undertake this Convolutional Neural networks course.

If you're an Analyst or an ML scientist, or a student who wants to find out and apply Deep learning in world image recognition problems, this course will offer you a solid base for that by teaching you a number of the foremost advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical.

Why do you have to choose this course?

This course covers all the steps that one should fancy create a picture recognition model using Convolutional Neural Networks.

Most courses only specialise in teaching the way to run the analysis but we believe that having a robust theoretical understanding of the concepts enables us to make an honest model . And after running the analysis, one should be ready to judge how good the model is and interpret the results to truly be ready to help the business.

What makes us qualified to show you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics consulting company , we've helped businesses solve their business problem using Deep learning techniques and that we have used our experience to incorporate the sensible aspects of knowledge analysis during this course

We also are the creators of a number of the foremost popular online courses - with over 300,000 enrollments and thousands of 5-star reviews like these ones:

This is excellent , i really like the very fact the all explanation given are often understood by a layman - Joshua

Thank you Author for this excellent course. you're the simplest and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and that we are committed thereto . If you've got any questions on the course content, practice sheet or anything associated with any topic, you'll always post an issue within the course or send us an immediate message.

Download Practice files, take Practice test, and complete Assignments

With each lecture, there are class notes attached for you to follow along. you'll also take practice test to see your understanding of concepts. there's a final practical assignment for you to practically implement your learning.

What is covered during this course?

This course teaches you all the steps of making a Neural network based model i.e. a Deep Learning model, to unravel business problems.

Below are the course contents of this course on ANN:

Part 1 (Section 2)- Python basics

This part gets you started with Python.

This part will assist you found out the python and Jupyter environment on your system and it will teach you ways to perform some basic operations in Python. we'll understand the importance of various libraries like Numpy, Pandas & Seaborn.

Part 2 (Section 3-6) - ANN Theoretical Concepts

This part will offer you a solid understanding of concepts involved in Neural Networks.

In this section you'll study the only cells or Perceptrons and the way Perceptrons are stacked to make a specification . Once architecture is about , we understand the Gradient descent algorithm to seek out the minima of a function and find out how this is often wont to optimize our network model.

Part 3 (Section 7-11) - Creating ANN model in Python

In this part you'll find out how to make ANN models in Python.

We will start this section by creating an ANN model using Sequential API to unravel a classification problem. We find out how to define specification , configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we find out how to save lots of and restore models.

We also understand the importance of libraries like Keras and TensorFlow during this part.

Part 4 (Section 12) - CNN Theoretical Concepts

In this part you'll study convolutional and pooling layers which are the building blocks of CNN models.

In this section, we'll start with the essential theory of convolutional layer, stride, filters and have maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.

Part 5 (Section 13-14) - Creating CNN model in Python
In this part you'll find out how to make CNN models in Python.

We will take an equivalent problem of recognizing fashion objects and apply CNN model thereto . we'll compare the performance of our CNN model with our ANN model and see that the accuracy increases by 9-10% once we use CNN. However, this is often not the top of it. we will further improve accuracy by using certain techniques which we explore within the next part.

Part 6 (Section 15-18) - End-to-End Image Recognition project in Python
In this section we build an entire image recognition project on colored images.

We take a Kaggle image recognition competition and build CNN model to unravel it. With an easy model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to just about 97% (as good because the winners of that competition).

By the top of this course, your confidence in creating a Convolutional Neural Network model in Python will soar. you will have a radical understanding of the way to use CNN to make predictive models and solve image recognition problems.



Go ahead and click on the enroll button, and I'll see you in lesson 1!



Cheers

Start-Tech Academy



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Below are some popular FAQs of scholars who want to start out their Deep learning journey-



Why use Python for Deep Learning?

Understanding Python is one among the precious skills needed for a career in Deep Learning.

Though it hasn’t always been, Python is that the programing language of choice for data science. Here’s a quick history:

In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

In 2017, it overtook R on KDNuggets’s annual poll of knowledge scientists’ most used tools.

In 2018, 66% of knowledge scientists reported using Python daily, making it the amount one tool for analytics professionals.

Deep Learning experts expect this trend to continue with increasing development within the Python ecosystem. And while your journey to find out Python programming could also be just beginning, it’s nice to understand that employment opportunities are abundant (and growing) also .

What is the difference between data processing , Machine Learning, and Deep Learning?

Put simply, machine learning and data processing use an equivalent algorithms and techniques as data processing , except the sorts of predictions vary. While data processing discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.

Deep learning, on the opposite hand, uses advanced computing power and special sorts of neural networks and applies them to large amounts of knowledge to find out , understand, and identify complicated patterns. Automatic language translation and medical diagnoses are samples of deep learning.

Who this course is for:
People pursuing a career in data science
Working Professionals beginning their Deep Learning journey
Anyone curious to master image recognition from Beginner level briefly span of your time