Introduction to Deep Learning | Học trực tuyến CNTT, học lập trình từ cơ bản đến nâng cao

Thông tin chung

Deep learning can be considered as a subset of machine learning. It is a field that is based on learning and improving on its own by examining computer algorithms. While machine learning uses simpler concepts, deep learning works with deep neural networks, which are designed to imitate how humans think and learn.

The fourth course of the Data Science Program aims at providing you a fundamental of modern neural networks and their various applications on computer vision and natural language processing. Furthermore, learners will be able to build Deep Neutral Networks models with two of the most popular deep learning library – Tensorflow and Keras provided by Google. Various optimization techniques also have been taught for fine-tuning a DNN model. 

To begin the course, let’s take a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments/projects/quizzes you’ll need to complete to pass the course.

Main concepts are delivered through videos, demos and hands-on exercises.

Mục tiêu môn học

After taking this course, the students should all be able to:

Comprehend about gradient descent, stochastic gradient descent, regularization, overfitting

Acquire overview and basic knowledge about deep neural network

Introducing Deep Learning in Computer Vision. Be able to apply CNN and transfer learning to computer vision tasks.

Acquire overview and basic knowledge about unsupervised representation learning in deep learning as autoencoder, word embedding.

Introducing Deep Learning in NLP/sequence models. Be able to apply RNN, LSTM to GRU to NLP tasks.

Proficiently manipulate typical and basic libraries in machine learning with Python: Numpy, Tensorflow, Keras.

Trải nghiệm học tập

Module 1 – Simple Neutral Network

Lesson 1: Introduction to Optimization

Lesson 2: Stochastic methods for optimization

Lesson 3: Introduction to Neutral Network

Module 2 – Deep Learning in Computer Vision

Lesson 4: Deep Learning Framework

Lesson 5: Deep Learning for Images

Lesson 6: Applications of CNNs

Assignment 1 – Project –  Image Classification

Module 3 –  Unsupervised Representation Learning

Lesson 7: Unsupervised Learning

Module 4 – Deep Learning in Natural Language Processing

Lesson 8: Word Embedding

Lesson 9: Introduction to RNN

Lesson 10: Modern RNNs

Assignment 2 – Project – Toxic comment classification

Nguồn học liệu

In modern times, each subject has numerous relevant studying materials including printed and online books. FUNiX Way does not provide a specific learning resource but offers recommendation for students to choose the most appropriate source to them. In the process of studying from many different sources based on that personal choice, students will be timely connected to a mentor to respond to their questions. All the assessments including multiple choice questions, exercises, projects and oral exams are designed, developed and conducted by FUNiX.  

Learners are under no obligation to choose a fixed learning material. They are encouraged to actively find and study from any appropriate sources including printed textbooks, MOOCs or websites. Students are on their own responsibilities in using these learning sources and ensuring full compliance with the source owners’ policies; except for the case in which they have an official cooperation with FUNiX. For further support, feel free to contact FUNiX Academic Department for detailed instructions. 

Learning resources are recommended below. It should be noted that listing these learning sources does not necessarily imply that FUNiX has an official partnership with the source’s owner: CourseratutorialspointedX TrainingUdemyMachine Learning cơ bản, or Towards Data Science.

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