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This course teaches you the practical aspects of neural networks ranging from topics like hyperparameter tuning to how your optimization algorithm runs in the system.

You will be working on making critical decisions on improving your network by selecting number of layers and hidden units, the learning rate and activation functions in this course. This is a highly intuitive course which enables you to convert your ideas to code and experiments and get result.


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.


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 – Train Dev Test sets 00:12:00
Neural Networks – Bias Variance 00:09:00
Neural Networks – Basic Recipe for Machine Learning 00:06:00
Neural Networks – Regularization 00:10:00
Neural Networks – Why regularization reduces overfitting 00:07:00
Neural Networks – Dropout Regularization 00:09:00
Neural Networks – Understanding Dropout 00:07:00
Neural Networks – Other regularization methods 00:08:00
Neural Networks – Normalizing inputs 00:06:00
Neural Networks – Vanishing Exploding gradients 00:06:00
Neural Networks – Weight Initialization for Deep Networks 00:06:00
Neural Networks – Numerical approximation of gradients 00:07:00
Module 02
Neural Networks – Gradient checking 00:07:00
Neural Networks – Gradient Checking Implementation Notes 00:05:00
Neural Networks – Yoshua Bengio interview 00:26:00
Neural Networks – Mini batch gradient descent 00:11:00
Neural Networks – Understanding mini batch gradient descent 00:11:00
Neural Networks – Exponentially weighted averages 00:06:00
Neural Networks – Understanding exponentially weighted averages 00:10:00
Neural Networks – Bias correction in exponentially weighted averages 00:04:00
Neural Networks – Gradient descent with momentum 00:09:00
Neural Networks – RMSprop 00:08:00
Neural Networks – Adam optimization algorithm 00:07:00
Neural Networks – Learning rate decay 00:07:00
Neural Networks – The problem of local optima 00:05:00
Module 03
Neural Networks – Yuanqing Lin interview 00:14:00
Neural Networks – Tuning process 00:07:00
Neural Networks – Using an appropriate scale to 00:09:00
Neural Networks – Hyperparameters tuning in prac 00:07:00
Neural Networks – Normalizing activations in a n 00:09:00
Neural Networks – Fitting Batch Norm into a neur 00:13:00
Neural Networks – Why does Batch Norm work 00:12:00
Neural Networks – Batch Norm at test time 00:06:00
Neural Networks – Softmax Regression 00:12:00
Neural Networks – Training a softmax classifier 00:10:00
Neural Networks – Deep learning frameworks 00:04:00
Neural Networks – TensorFlow 00:16:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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