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Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you will develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets. We’ll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks.


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 assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

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Course Credit: Google

Course Curriculum

From Machine Learning to Deep Learning
What is Deep Learning? 00:01:00
Course Overview 00:02:00
Solving Problems – Big and Small 00:02:00
Let’s Get Started! 00:01:00
Supervised Classification 00:01:00
Quiz: Classification For Detection 00:01:00
Quiz: Classification For Ranking 00:01:00
Let’s make a deal 00:01:00
Training Your Logistic Classifier 00:02:00
Quiz: Softmax 00:01:00
Quiz: Softmax Quiz Part 2 00:01:00
Quiz: Softmax Quiz Part 3 00:02:00
One-Hot Encoding 00:01:00
Quiz: One-Hot Encoding Quiz 00:01:00
Cross Entropy 00:02:00
Minimizing Cross Entropy 00:02:00
Practical Aspects of Learning 00:01:00
Numerical Stability 00:01:00
Normalized Inputs and Initial Weights 00:03:00
Lather. Rinse. Repeat. 00:01:00
Measuring Performance 00:04:00
Transition: Overfitting -> Dataset Size 00:01:00
Validation and Test Set Size 00:02:00
Quiz: Validation Set Size 00:01:00
Validation Test Set Size Continued 00:01:00
Optimizing a Logistic Classifier 00:01:00
Stochastic Gradient Descent 00:03:00
Momentum and Learning Rate Decay 00:02:00
Parameter Hyperspace! 00:02:00
Assignment: notMNIST
Getting Started with notMNIST 00:03:00
Problem 1: Display some images 00:01:00
Convert image dataset into 3D array 00:02:00
Problem 2: Verify normalized images 00:01:00
Problem 3: Verify data is balanced 00:01:00
Splitting the dataset into batches 00:01:00
Problem 4: Shuffle samples and verify 00:02:00
Problem 5: Find overlapping samples 00:00:00
Problem 6: Train a simple ML model 00:02:00
Deep Neural Networks
Intro to Deep Neural Networks 00:01:00
Quiz: Number of Parameters 00:01:00
Linear Models are Limited 00:02:00
Quiz: Rectified Linear Units 00:01:00
Network of ReLUs 00:01:00
No Neurons 00:01:00
The Chain Rule 00:01:00
Backprop Through time 00:01:00
Training a Deep Learning Network 00:02:00
Regularization Intro 00:01:00
Regularization 00:01:00
Regularization Quiz 00:01:00
Dropout 00:02:00
Dropout Pt. 2 00:01:00
Next Assignment: Regularization 00:01:00
Convolutional Neural Networks
Intro To CNNs 00:01:00
Color 00:01:00
Statistical Invariance 00:02:00
Convolutional Networks 00:04:00
Feature Map Sizes 00:01:00
Convolutions continued 00:01:00
Explore The Design Space 00:03:00
1×1 Convolutions 00:02:00
Inception Module 00:02:00
Conclusion 00:01:00
Next Assignment: ConvNets 00:01:00
Deep Models for Text and Sequences
Train a text embedding model 00:01:00
Semantic Ambiguity 00:01:00
Unsupervised Learning 00:01:00
Embeddings 00:02:00
Word2Vec 00:01:00
tSNE 00:01:00
Word2Vec Details 00:02:00
Quiz: Word Analogy Game 00:01:00
Analogies 00:01:00
Sequences of Varying Length 00:01:00
RNNs 00:02:00
Backprop Through time 00:01:00
Vanishing / Exploding Gradients 00:01:00
LSTM 00:01:00
Memory Cell 00:01:00
LSTM Cell 00:02:00
LSTM Cell 2 00:01:00
Regularization 00:01:00
Beam Search 00:02:00
Play Legos 00:01:00
Captioning and Translation 00:02:00
Course Outro 00:01:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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