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Introduction to Deep Learning

Andrei Zimovnov, Ekaterina Lobacheva, Alexander Panin, Evgeny Sokolov, Nikita Kazeev, and Зимовнов Андрей Вадимович
The goal of this online course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic...
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The goal of this online course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers. Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image. The prerequisites for this course are: 1) Basic knowledge of Python. 2) Basic linear algebra and probability. Please note that this is an advanced course and we assume basic knowledge of machine learning. You should understand: 1) Linear regression: mean squared error, analytical solution. 2) Logistic regression: model, cross-entropy loss, class probability estimation. 3) Gradient descent for linear models. Derivatives of MSE and cross-entropy loss functions. 4) The problem of overfitting. 5) Regularization for linear models. Do you have technical problems? Write to us: [email protected].
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Know what's good
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Teaches all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers
Explores modern neural networks and their applications in computer vision and natural language understanding
Develops deep neural networks in TensorFlow and Keras frameworks
Has instructors who are recognized for their work in the field
Assumes basic knowledge of machine learning, including linear regression, logistic regression, gradient descent, overfitting, and regularization
Requires students to have basic knowledge of Python, basic linear algebra, and probability

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Reviews summary

Advanced introduction to deep learning

This intermediate-level course on deep learning fundamentals in natural language processing and computer vision is appropriate for students with a foundation in machine learning and Python. Students should be comfortable with linear regression, logistic regression, overfitting, and regularization. The course has a high satisfaction rate among students, due to the rapport built with the instructors, well-structured assignments, and strong course content.
Great for Intermediate Learners
"The course is amazing. Too dificult for me because I was beginner in Machine Learning, but this couse provides a lot of content, and techniques."
Interesting and Challenging Assignments
"Programming assignments are interesting but way too easy, even the final project."
"Programming assignments are tough and interesting but mostly pre-coded."
"The assignments were challenging, allowing participant to dive into deep learning with tensorflow."
Andrei Zimovnov is a Great Lecturer
"Zimovnov is best ML lecturer."
"Andrei Zimovnonv's lectures are really good. His flow, the concepts he provide, all are lucid."
Good Coverage of Deep Learning Basics
"Very complete for an intoduction."
"This course provides good study material, assignment and quizzes. Really enjoyed this course."
Some Instructors Have Pronunciation Difficulties
"One of the speaker - Alexander Panin - has very bad english pronunciation. It was too difficult to understand what he told"
"Alexander Panin's lectures are, I think quit difficult to understand. Most of the times, he suddenly delivers so fast that you can't even hear what he actually said."
Requires Outdated Version of TensorFlow
"Uses old versions and material. Was probably really good few years back"
"TensorFlow code within the assignments must be changed to version 2"

Career center

Learners who complete Introduction to Deep Learning will develop knowledge and skills that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineers specialize in developing and deploying deep learning models. The Introduction to Deep Learning course provides a hands-on introduction to deep learning, with a focus on practical applications in computer vision and natural language understanding. This course will help you develop the skills and knowledge you need to become a successful Deep Learning Engineer.
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data to extract meaningful insights. The Introduction to Deep Learning course provides a strong foundation in the fundamental concepts of deep learning, which is a powerful tool for data analysis. Deep learning models can be used to solve a wide range of problems in data science, such as image recognition, natural language processing, and speech recognition. This course will help you develop the skills and knowledge you need to become a successful Data Scientist.
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. The Introduction to Deep Learning course provides a comprehensive overview of deep learning techniques and algorithms, which are essential for building effective machine learning models. This course will help you develop the skills and knowledge you need to become a successful Machine Learning Engineer.
Computer Vision Engineer
Computer Vision Engineers design and develop computer vision systems that can interpret and understand images and videos. The Introduction to Deep Learning course provides a strong foundation in the fundamentals of deep learning, which is a key technology for computer vision. Deep learning models can be used to solve a wide range of problems in computer vision, such as object detection, image classification, and facial recognition. This course will help you develop the skills and knowledge you need to become a successful Computer Vision Engineer.
Natural Language Processing Engineer
Natural Language Processing Engineers design and develop natural language processing systems that can understand and generate human language. The Introduction to Deep Learning course provides a strong foundation in the fundamentals of deep learning, which is a key technology for natural language processing. Deep learning models can be used to solve a wide range of problems in natural language processing, such as text classification, machine translation, and question answering. This course will help you develop the skills and knowledge you need to become a successful Natural Language Processing Engineer.
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. The Introduction to Deep Learning course provides a strong foundation in the fundamentals of deep learning, which is a rapidly growing field with a wide range of applications. Deep learning models can be used to solve a wide range of problems in marketing, such as customer segmentation, market research, and campaign optimization. This course will help you develop the skills and knowledge you need to become a successful Marketing Manager.
Product Manager
Product Managers are responsible for the development and launch of new products. The Introduction to Deep Learning course provides a strong foundation in the fundamentals of deep learning, which is a rapidly growing field with a wide range of applications. Deep learning models can be used to solve a wide range of problems in product management, such as customer segmentation, market research, and product design. This course will help you develop the skills and knowledge you need to become a successful Product Manager.
Operations Manager
Operations Managers are responsible for the day-to-day operations of a business. The Introduction to Deep Learning course provides a strong foundation in the fundamentals of deep learning, which is a rapidly growing field with a wide range of applications. Deep learning models can be used to solve a wide range of problems in operations, such as supply chain management, inventory optimization, and quality control. This course will help you develop the skills and knowledge you need to become a successful Operations Manager.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. The Introduction to Deep Learning course provides a strong foundation in the fundamentals of deep learning, which is a powerful tool for financial analysis. Deep learning models can be used to solve a wide range of problems in quantitative analysis, such as risk assessment, portfolio optimization, and trading strategy development. This course will help you develop the skills and knowledge you need to become a successful Quantitative Analyst.
Risk Manager
Risk Managers are responsible for identifying and mitigating risks. The Introduction to Deep Learning course provides a strong foundation in the fundamentals of deep learning, which is a powerful tool for risk management. Deep learning models can be used to solve a wide range of problems in risk management, such as fraud detection, credit risk assessment, and operational risk management. This course will help you develop the skills and knowledge you need to become a successful Risk Manager.
Financial Analyst
Financial Analysts use data and analysis to make investment decisions. The Introduction to Deep Learning course provides a strong foundation in the fundamentals of deep learning, which is a powerful tool for financial analysis. Deep learning models can be used to solve a wide range of problems in financial analysis, such as risk assessment, portfolio optimization, and trading strategy development. This course will help you develop the skills and knowledge you need to become a successful Financial Analyst.
Sales Manager
Sales Managers are responsible for leading and motivating sales teams. The Introduction to Deep Learning course provides a strong foundation in the fundamentals of deep learning, which is a rapidly growing field with a wide range of applications. Deep learning models can be used to solve a wide range of problems in sales, such as lead generation, customer segmentation, and sales forecasting. This course will help you develop the skills and knowledge you need to become a successful Sales Manager.
Business Analyst
Business Analysts use data and analysis to help businesses make better decisions. The Introduction to Deep Learning course provides a strong foundation in the fundamentals of deep learning, which is a powerful tool for business analysis. Deep learning models can be used to solve a wide range of problems in business analysis, such as customer segmentation, market research, and fraud detection. This course will help you develop the skills and knowledge you need to become a successful Business Analyst.
Software Engineer
Software Engineers design, develop, and maintain software systems. The Introduction to Deep Learning course provides a strong foundation in the fundamentals of deep learning, which is a rapidly growing field with a wide range of applications. Deep learning models can be used to solve a wide range of problems in software engineering, such as image recognition, natural language processing, and speech recognition. This course will help you develop the skills and knowledge you need to become a successful Software Engineer.
Data Analyst
Data Analysts collect, analyze, and interpret data to extract meaningful insights. The Introduction to Deep Learning course provides a strong foundation in the fundamentals of deep learning, which is a powerful tool for data analysis. Deep learning models can be used to solve a wide range of problems in data analysis, such as fraud detection, customer segmentation, and predictive analytics. This course will help you develop the skills and knowledge you need to become a successful Data Analyst.

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