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The application of Artificial Intelligence is now visible in healthcare systems, delivering personalized education, precision agriculture and even in self-driving cars. This course will help you to learn about these amazing technologies.

This is a highly sought out course in many countries of the world which specializes on neural networks and deep learning, hyperparameter tuning, regularization and optimization, structuring your machines, convolutional neural networks and natural neural processing to build amazing things.

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 – 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 – Logistic Regression 00:06:00
3 Logistic Regression Cost Function 00:08:00
Neural Networks – Gradient Descent 00:11:00
Neural Networks – Derivatives 00:07:00
Neural Networks – More Derivative Examples 00:10:00
Neural Networks – Computation graph 00:00:00
Neural Networks – Derivatives with a Computation Graph 00:15:00
Module 02
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
Neural Networks – Pieter Abbeel interview 00:16:00
Module 03
Neural Networks – What is a neural network 00:07:00
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
Neural Networks – Ian Goodfellow interview 00:15:00
Module 04
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
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
Module 05
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
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
Module 06
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
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
Module 07
Neural Networks – Deep learning frameworks 00:04:00
Neural Networks – TensorFlow 00:16:00
Neural Networks – Why ML Strategy 00:03:00
Neural Networks – Orthogonalization 00:11:00
Neural Networks – Single number evaluation metric 00:07:00
Neural Networks – Satisficing and Optimizing metric 00:06:00
Neural Networks – Traindevtest distributions 00:07:00
Neural Networks – Size of the dev and test sets 00:06:00
Neural Networks – When to change devtest sets and metrics 00:11:00
Neural Networks – Why human level performance 00:06:00
Neural Networks – Avoidable bias 00:07:00
Neural Networks – Understanding human level performance 00:11:00
Neural Networks – Surpassing human level performance 00:06:00
Neural Networks – Andrej Karpathy interview 00:15:00
Neural Networks – Improving your model performance 00:06:00
Module 08
Neural Networks – ML Strategy 2 00:11:00
Neural Networks – Cleaning up incorrectly labeled data 00:13:00
Neural Networks – Cleaning up incorrectly labeled data 00:13:00
Neural Networks – Training and testing on different distributions 00:11:00
Neural Networks – Bias and Variance with mismatched data distributions 00:18:00
Neural Networks – Addressing data mismatch 00:10:00
Neural Networks – Transfer learning 00:11:00
Neural Networks – Multi task learning 00:13:00
Neural Networks – Whether to use end to end deep learning 00:10:00
Neural Networks – Whether to use end to end deep learning 00:10:00
Neural Networks – Ruslan Salakhutdinov interview 00:17:00
Neural Networks – Computer Vision 00:06:00
Neural Networks – Edge Detection Example 00:12:00
Neural Networks – More Edge Detection 00:08:00
Neural Networks – Padding 00:10:00
Neural Networks – Strided Convolutions 00:09:00
Neural Networks – Convolutions Over Volume 00:11:00
Module 09
Neural Networks – One Layer of a Convolutional Network 00:16:00
Neural Networks – Simple Convolutional Network Example 00:09:00
Neural Networks – Pooling Layers 00:10:00
Neural Networks – CNN Example 00:13:00
Neural Networks – Why Convolutions 00:10:00
Neural Networks – Why look at case studies 00:03:00
Neural Networks – Classic Networks 00:18:00
Neural Networks – ResNets 00:07:00
Neural Networks – Why ResNets Work 00:09:00
Neural Networks – Networks in Networks and 1×1 Convolutions 00:07:00
Neural Networks – Inception Network Motivation 00:10:00
Neural Networks – Inception Network 00:09:00
Neural Networks – Using Open Source Implementation 00:06:00
Neural Networks – Transfer learning 00:11:00
Neural Networks – Data Augmentation 00:10:00
Neural Networks – State of Computer Vision 00:13:00
Neural Networks – Object Localization 00:12:00
Neural Networks – Landmark Detection 00:06:00
Neural Networks – Object Detection 00:06:00
Module 10
Neural Networks – Convolutional Implementation of Sliding Windows 00:11:00
Neural Networks – Bounding Box Predictions 00:15:00
Neural Networks – Intersection Over Union 00:04:00
Neural Networks – Non max Suppression 00:08:00
Neural Networks – Anchor Boxes 00:10:00
Neural Networks – YOLO Algorithm 00:07:00
Neural Networks – Region Proposals 00:06:00
Neural Networks – What is face recognition 00:05:00
Neural Networks – One Shot Learning 00:05:00
Neural Networks – Siamese Network 00:05:00
Neural Networks – Triplet Loss 00:16:00
Neural Networks – Face Verification and Binary Classification 00:06:00
Neural Networks – What is neural style transfer 00:02:00
Neural Networks – What are deep ConvNets learning 00:08:00
Neural Networks – Cost Function 00:00:00
Neural Networks – Content Cost Function 00:04:00
Neural Networks – Style Cost Function 00:13:00
Neural Networks – 1D and 3D Generalizations 00:09:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

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