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Deep Learning ( 9 REVIEWS )
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Machine learning is one of the fastest-growing and most exciting elds out there, and deep learning represents its true bleeding …
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2 HOURS, 3 MINUTES
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Machine learning is one of the fastest-growing and most exciting elds 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 largescale datasets. We’ll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks.
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 assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certi cate at a cost of £49. Having an Of cial Edukite Certi cation is a great way to celebrate and share your success. You can: Add the certi cate to your CV or resume and brighten up your career Show it to prove your success Course Credit: Google
COURSE CURRICULUM https://edukite.org/course/deep-learning/
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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 Classi cation
00:01:00
Quiz: Classi cation For Detection
00:01:00
Quiz: Classi cation For Ranking
00:01:00
Let’s make a deal
00:01:00
Training Your Logistic Classi er
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
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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: Over tting -> 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 Classi er
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
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Splitting the dataset into batches
00:01:00
Problem 4: Shuf e 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: Recti ed 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
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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
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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
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Course Outro
00:01:00
Assessment
Submit Your Assignment
00:00:00
Certi cation
00:00:00
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