In 4 days, learn the most common algorithms of Deep Learning, the most popular Artificial Intelligence application today, and how Artificial Neural Networks work
With this course, you will get one step closer to developing your own projects by learning how we can integrate Deep Learning into our lives
This course inspired by world-renowned and best-in-class curricula, tutorials and books
Jump-start your career by entering professional fields such as Computer Vision and Natural Language Processing with this course
Be one step ahead in interviews with projects developed with Tensorflow, one of the world’s most popular Deep Learning libraries, included in the course
WHY TAKE THIS COURSE?
Although deep learning is a sub-branch of machine learning, it has become a profession and a field of expertise today. This is because it performs much better than conventional machine learning techniques, especially in Image processing and Natural language processing.
This course is a great introductory course that gives you an explanatory introduction to the sub-branches of Deep Learning and how neural networks work, developed and optimized, which allows you to solve more complex AI problems.
With this course, you will understand how the most promising and surprising artificial intelligence projects affect our lives and the underlying technologies. In this course, where the most popular deep learning libraries are used, you will develop projects using Python language, and you will have the skills needed to start your career in this field.
HUB
You are invited to join our Introduction to Deep Learning Hub and use this space to discuss topics related to the course, share interesting and relevant material and links, ask questions and engage with peers.
ALL OUR PROGRAMS INCLUDE
Additional access to active mentoring by experts of the Global AI Hub
A joint certificate issued by Global AI Hub for each successful learner
The certificate you will earn in this training is valid for privileged membership applications under theCoreRelation Program
Sponsored by
Thanks to the Swiss-based AI Business School and the «10million.AI» project this course is free.
It is part of the national education campaigns aiming at educating more than 10 million learners for free on AI and other digital technologies.
What is the Place of Deep Learning in the World of Artificial Intelligence?
MODULE 1
Introduction to Artificial Intelligence
What is Artificial Intelligence?
Disambiguation in Artificial Intelligence
Introduction to Machine Learning
What is Machine Learning?
Types of Machine Learning
Traditional Programming and Machine Learning
Supervised Learning
What is Supervised Learning?
Supervised Learning Algorithms and Architectures
Regression and Classification
Machine Learning Real Life Examples
Unsupervised Learning
What is Unsupervised Learning?
Why Do We Need Unsupervised Learning?
Unsupervised Learning Algorithms
Deep Learning
Historical Development of Deep Learning
What are Artificial Neural Networks and How Do They Work?
Usages of Deep Learning in Daily Life
Why Deep Learning Has Become Popular?
Deep Learning vs Machine Learning
Tools Used in Deep Learning
Deep Learning Resources
MODULE 2
Basic Regression Mathematics
Linear Regression
Logistic Regression
Sigmoid Activation Function
Artificial Neural Networks and Mathematics
Weight, Bias, Input, Output Concepts
Matrix Algebra
A Simple Perceptron Coding
Solution of Logic Gates with Neural Networks
Fahrenheit Celsius conversion
Activation Functions
What is Non-linearity and Why Do We Need It?
ReLU, Softmax, Tanh Activation Functions
Optimization of Neural Networks
Forward and Back Propagation Algorithms in Deep Learning
Overfitting and Underfitting
Regularization
Dropout, Early Stopping, Data Augmentation
Hyperparameter Optimization
Common Optimization Algorithms
ADAM
Stochastic Gradient Descent
RMSPROP
MODULE 3
Computer Vision and Image Processing
What is Image Processing?
Signal and Noise Concept
Computer Vision Usage Areas
Feature Extraction
Convolutional Neural Networks (CNN)
CNN Layer Types
Common Activation Functions
Pre-trained CNN Models
What is Pre-trained Model and What Does It Do?
LeNet-5
VGG16
ResNet50
Recurrent Neural Networks (RNN)
What is RNN?
RNN and Computer Vision
Other Techniques Used in Convolutional Networks
Data Augmentation
Transfer Learning
One-shot Learning
Object Detection
What is Object Detection?
Object Detection Usage Areas
Object Detection Architectures
Faster R-CNN, YOLO, SSD
Object Detection Datasets
COCO, Pascal VOC()
Image Classification
What is Image Classification?
Object Localization in Image
Image Classification Datasets
MNIST, ImageNet, CIFAR10
Project: Classification of Flower Images with TensorFlow
MODULE 4
Natural Language Processing (NLP)
What is Natural Language Processing?
Why Do We Need NLP?
Natural Language Processing Real-Life Uses
Text Data Structure and Comparison with Other Data Structures
Text Encodings
Bag of Words
Integer Encoding
Word Embedding
Recursive Neural Networks (RNN)
RNN Mathematics
Why RN?
Vanishing Gradient Problem
LSTM
GRU
Decoder – Encoder Architecture
Attention
Transformers
BERT
Project: Sentiment Analysis Workshop
EPILOGUE
Practical Use Of What Has Been Learned
Further Projects
What’s Next?
LEARNING ACTIVITIES
The course includes a series of lessons that lead you through the content in small, bite-sized learning blocks. Each lesson includes exciting video sessions followed by thought-provoking assessment questions.
Video sessions have to be marked as complete and can be accessed freely after the completion of each lesson.
Assessment questions are graded for the calculation of certification progress.
Each day has a “Materials” section to help you revise the topics that are seen that day.
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