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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