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This course on deep learning focuses on how deep learning is enabling the creation of brand new products and businesses, better healthcare, personal education and precision agriculture.

You will learn how to develop a neural network and their practical application in our life. You can apply this technology on image and language processing problems in the web and how to build them on a sequence model.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

Having an Official Edukite Certification is a great way to celebrate and share your success. You can:

  • Add the certificate to your CV or resume and brighten up your career
  • Show it to prove your success

 

Course Credit: Deep Learning AI

Course Curriculum

Module 01
Neural Networks – Welcome 00:06:00
Neural Networks – What is a neural network 00:07:00
Neural Networks – Supervised Learning with Neural Networks 00:00:00
Neural Networks – Why is Deep Learning taking off 00:10:00
Neural Networks – About this Course 00:02:00
Neural Networks – Geoffrey Hinton interview 00:40:00
Neural Networks – Binary Classification 00:08:00
Neural Networks – Vectorizing Logistic Regression 00:08:00
3 Logistic Regression Cost Function 00:08:00
Neural Networks – Logistic Regression Gradient Descent 00:07:00
Neural Networks – Derivatives 00:07:00
Neural Networks – More Derivative Examples 00:10:00
Module 02
Neural Networks – Computation graph 00:00:00
Neural Networks – Derivatives with a Computation Graph 00:15:00
Neural Networks – Logistic Regression Gradient Descent 00:07:00
Neural Networks – Gradient Descent on m Examples 00:08:00
Neural Networks – Vectorization 00:08:00
Neural Networks – More Vectorization Examples 00:06:00
Neural Networks – Vectorizing Logistic Regression 00:08:00
Neural Networks – Vectorizing Logistic Regression’s Gradient Output 00:10:00
Neural Networks – Broadcasting in Python 00:11:00
Neural Networks – A note on pythonnumpy vectors 00:07:00
Neural Networks – Quick tour of Jupyteri Python Notebooks 00:04:00
Neural Networks – Explanation of logistic regression cost function 00:07:00
Module 03
Neural Networks – Pieter Abbeel interview 00:16:00
Neural Networks – Overview 00:04:00
Neural Networks – Neural Network Representation 00:05:00
Neural Networks – Computing a Neural Network’s Output 00:10:00
Neural Networks – Vectorizing across multiple examples 00:09:00
Neural Networks – Explanation for Vectorized Implementation 00:08:00
Neural Networks – Activation functions 00:11:00
Neural Networks – Why do you need non linear activation functions 00:06:00
Neural Networks – Derivatives of activation functions 00:08:00
Neural Networks – Gradient descent for Neural Networks 00:10:00
Neural Networks – Backpropagation intuition 00:16:00
Neural Networks – Random Initialization 00:08:00
Module 04
Neural Networks – Ian Goodfellow interview 00:15:00
Neural Networks – Deep L layer neural network 00:06:00
Neural Networks – Forward Propagation in a Deep Network 00:07:00
Neural Networks – Getting your matrix dimensions right 00:11:00
Neural Networks – Why deep representations 00:11:00
Neural Networks – Building blocks of deep neural networks 00:09:00
Neural Networks – Forward and Backward Propagation 00:11:00
Neural Networks – Parameters vs Hyperparameters 00:07:00
Neural Networks – What does this have to do with the brain 00:03:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

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