CNN for Computer Vision with Keras and TensorFlow in Python
Created by Abhishek And Pukhraj, Last Updated 23-Jan-2020, Language:English
CNN for Computer Vision with Keras and TensorFlow in Python
Python for Computer Vision & Image Recognition - Deep Learning Convolutional Neural Network (CNN) - Keras & TensorFlow 2
Created by Abhishek And Pukhraj, Last Updated 23-Jan-2020, Language:English
What Will I Get ?
- Get a solid understanding of Convolutional Neural Networks (CNN) and Deep Learning
- Build an end-to-end Image recognition project in Python
- Learn usage of Keras and Tensorflow libraries
- Use Artificial Neural Networks (ANN) to make predictions
- Use Pandas DataFrames to manipulate data and make statistical computations.
Requirements
- Students will need to install Python and Anaconda software but we have a separate lecture to help you install the sameStudents will need to install Python and Anaconda software but we have a separate lecture to help you install the same
Description
You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in Python, right?
You've found the right Convolutional Neural Networks course!
After completing this course you will be able to:
Identify the Image Recognition problems which can 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 clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.
If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical.
Why should you choose this course?
This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks.
Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses - with over 300,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman - Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Practice test, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your 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 in short span of time
Course Content
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Introduction
2 Lectures 00:03:29-
Introduction
Preview00:03:29 -
Course resources
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Setting up Python and Jupyter Notebook
9 Lectures 01:37:58-
Installing Python and Anaconda
Preview00:03:04 -
Opening Jupyter Notebook
Preview00:09:06 -
Introduction to Jupyter
Preview00:13:26 -
Arithmetic operators in Python: Python Basics
Preview00:04:28 -
Strings in Python: Python Basics
00:19:07 -
Lists, Tuples and Directories: Python Basics
00:18:41 -
Working with Numpy Library of Python
00:11:54 -
Working with Pandas Library of Python
00:09:15 -
Working with Seaborn Library of Python
00:08:57
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Single Cells - Perceptron and Sigmoid Neuron
3 Lectures 00:31:27-
Perceptron
00:09:47 -
Activation Functions
00:07:30 -
Python - Creating Perceptron model
00:14:10
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Neural Networks - Stacking cells to create network
3 Lectures 00:44:31-
Basic Terminologies
00:09:47 -
Gradient Descent
00:12:17 -
Back Propagation
00:22:27
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Important concepts: Common Interview questions
1 Lectures 00:12:44-
Some Important Concepts
00:12:44
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Standard Model Parameters
1 Lectures 00:08:19-
Hyperparameters
00:08:19
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Tensorflow and Keras
2 Lectures 00:07:08-
Keras and Tensorflow
00:03:04 -
Installing Tensorflow and Keras
00:04:04
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Python - Dataset for classification problem
2 Lectures 00:13:18-
Dataset for classification
00:07:19 -
Normalization and Test-Train split
00:05:59
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Python - Building and training the Model
4 Lectures 00:34:17-
Different ways to create ANN using Keras
00:01:58 -
Building the Neural Network using Keras
00:12:24 -
Compiling and Training the Neural Network model
00:10:34 -
Evaluating performance and Predicting using Keras
00:09:21
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Saving and Restoring Models
1 Lectures 00:19:49-
Saving - Restoring Models and Using Callbacks
00:19:49
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Hyperparameter Tuning
1 Lectures 00:09:05-
Hyperparameter Tuning
00:09:05
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CNN - Basics
6 Lectures 00:35:31-
CNN Introduction
00:07:42 -
Stride
00:02:51 -
Padding
00:05:07 -
Filters and Feature maps
00:07:48 -
Channels
00:06:31 -
PoolingLayer
00:05:32
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Creating CNN model in Python
3 Lectures 00:18:56-
CNN model in Python - Preprocessing
00:05:42 -
CNN model in Python - structure and Compile
00:06:24 -
CNN model in Python - Training and results
00:06:50
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Analyzing impact of Pooling layer
1 Lectures 00:06:20-
Comparison - Pooling vs Without Pooling in Python
00:06:20
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Project : Creating CNN model from scratch
5 Lectures 00:28:35-
Project - Introduction
00:07:04 -
Data for the project
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Project - Data Preprocessing in Python
00:09:19 -
Project - Training CNN model in Python
00:09:05 -
Project in Python - model results
00:03:07
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Project : Data Augmentation for avoiding overfitting
2 Lectures 00:13:12-
Project - Data Augmentation Preprocessing
00:06:46 -
Project - Data Augmentation Training and Results
00:06:26
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Transfer Learning : Basics
5 Lectures 00:15:48-
ILSVRC
00:04:10 -
LeNET
00:01:31 -
VGG16NET
00:02:00 -
GoogLeNet
00:02:52 -
Transfer Learning
00:05:15
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Transfer Learning in Python
1 Lectures 00:19:40-
Project - Transfer Learning - VGG16
00:19:40
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Abhishek And Pukhraj
Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.
Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey.
Founded by Abhishek Bansal and Pukhraj Parikh.
Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python.
Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.