Python - is Archive: Data Processing What you'll learn Understand the intuition behind Artificial Neural Networks.
Archive: Python - Data Processing What you'll learn Understand the intuition behind Artificial Neural Networks
Deep Learning Models is Build.
Build Deep Learning Models
all 36 lectures02:29:53 is Expand.
Expand all 36 lectures02:29:53
05:12 What is is Deep Learning?.
05:12 What is Deep Learning?
04:26 Drop is out.
04:26 Drop out
Gradient Problem is 01:23 Vanishing.
01:23 Vanishing Gradient Problem
this course is Then is for you!.
Then this course is for you!
This course is is designed in a very simple and easily understandable content..
This course is designed in a very simple and easily understandable content.
You is might have seen lots of buzz on deep learning and you want to figure out where to start and explore..
You might have seen lots of buzz on deep learning and you want to figure out where to start and explore.
This course is is designed exactly for people like you!.
This course is designed exactly for people like you!
basics is If are strong, we can do bigger things with ease..
If basics are strong, we can do bigger things with ease.
My focus in this course is is to build complicated things starting from very basics In this course, I will cover the following things Session 1 - Introductory material on Deep learning, its applications and significance..
My focus in this course is to build complicated things starting from very basics In this course, I will cover the following things Session 1 - Introductory material on Deep learning, its applications and significance.
Session 2 - is Introduces the fundamental building block of deep learning Session 3 - Logistic Regression, Activation Functions, Perceptron, One Hot Encoding, XOR problem and Multi-Layer Perceptron models Session 4 - Training of Neural Networks: Cross Entropy, Loss Function, Gradient descent Algorithm, Non-Linear Models, Feed Forward, Backward propagation, Overfitting problem, Early stopping, Regularization, drop out and Vanishing Gradient problem..
Session 2 - Introduces the fundamental building block of deep learning Session 3 - Logistic Regression, Activation Functions, Perceptron, One Hot Encoding, XOR problem and Multi-Layer Perceptron models Session 4 - Training of Neural Networks: Cross Entropy, Loss Function, Gradient descent Algorithm, Non-Linear Models, Feed Forward, Backward propagation, Overfitting problem, Early stopping, Regularization, drop out and Vanishing Gradient problem.
Session 5 - is Convolution Neural Networks: Feature Extraction, Convolution Layer, Pooling Layer, Relu, Flattening and Deep Convolution Neural Networks..
Session 5 - Convolution Neural Networks: Feature Extraction, Convolution Layer, Pooling Layer, Relu, Flattening and Deep Convolution Neural Networks.
Session 6 - is Sequence Models: Recurrent Neural Networks, LSTMs Are there any course requirements or prerequisites?.
Session 6 - Sequence Models: Recurrent Neural Networks, LSTMs Are there any course requirements or prerequisites?
this course is Who is for: Anyone interested in Machine Learning and Deep Learning Students who have high school knowledge in mathematics and who want to start learning Deep Learning Any intermediate level people who know the basics of machine learning, who want to learn more advanced topics like deep learning Any students in college who want to start a career in Data Science Any data analysts who want to level up in Machine Learning and Deep Learning Any people who are not satisfied with their job and who want to become a Data Scientist Any people who want to create added value to their business by using powerful Learning tools Build a foundation on the principles of Deep Learning to understand the latest trends Who this course is for: Anyone interested in Machine Learning and Deep Learning.
Who this course is for: Anyone interested in Machine Learning and Deep Learning Students who have high school knowledge in mathematics and who want to start learning Deep Learning Any intermediate level people who know the basics of machine learning, who want to learn more advanced topics like deep learning Any students in college who want to start a career in Data Science Any data analysts who want to level up in Machine Learning and Deep Learning Any people who are not satisfied with their job and who want to become a Data Scientist Any people who want to create added value to their business by using powerful Learning tools Build a foundation on the principles of Deep Learning to understand the latest trends Who this course is for: Anyone interested in Machine Learning and Deep Learning
Students is who have high school knowledge in mathematics and who want to start learning Deep Learning.
Students who have high school knowledge in mathematics and who want to start learning Deep Learning
Any intermediate level people is who know the basics of machine learning, who want to learn more advanced topics like deep learning.
Any intermediate level people who know the basics of machine learning, who want to learn more advanced topics like deep learning
Any students is in college who want to start a career in Data Science.
Any students in college who want to start a career in Data Science
Any data analysts is who want to level up in Machine Learning and Deep Learning.
Any data analysts who want to level up in Machine Learning and Deep Learning
Any people is who are not satisfied with their job and who want to become a Data Scientist..
Any people who are not satisfied with their job and who want to become a Data Scientist.
Any people is who want to create added value to their business by using powerful Learning tools..
Any people who want to create added value to their business by using powerful Learning tools.
a foundation is Build on the principles of Deep Learning to understand the latest trends Get Python - Data Processing download.
Build a foundation on the principles of Deep Learning to understand the latest trends Get Python - Data Processing download