Dirbtinio Intelekto Studijos

Šios Studijos padės studentams išmokti panaudoti dirbtinio intelekto ir giliojo mokymosi aplikacijas verslo sprendimams.

Apžvalga

Ką išmoksi?

Studijų metu analizuosime bei mokysimės geriausių dirbtinio intelekto bei giliojo mokymosi praktikų, paremtų Python programavimu bei papildomomis bibliotekomis. Viską, ką išmoksite teorinėse paskaitose, iš karto panaudosite praktikoje, tai leis geriau įsiminti medžiagą.

 

Kur pritaikysi?

Šios Studijos padės studentams išmokti panaudoti dirbtinio intelekto ir giliojo mokymosi aplikacijas verslo sprendimams. Pabaigus šias Studijas studentai galės identifikuoti problemas, kurios gali būti sprendžiamos naudojant šias dirbtinio intelekto ir giliojo mokymosi technologijas, kokie keliami reikalavimai bei kokie veiksmai turi būti taikomi problemoms spręsti. Įgyjamos kompetencijos – kompiuterinė vizija (computer vision), natūralios kalbos apdorojimas, laiko eilučių analizė (time-series analysis), rekomenduojančių sistemų kūrimas ir mokymosi stiprinimas.

Kur dirbsi?

Baigus šį kursą, studentai galės spręsti įvairaus sudėtingumo giliojo mokymosi problemas ir dirbti technologijų startuoliuose.

Finansavimas

Neatidėk mokslų ateičiai!

Luminor suteikia galimybę už mokslus mokėti išsimokėtinai.

Už studijas sumokėti gali su mūsų vartojimo paskola, kuri tinka mokymosi, kelionių ir kitoms reikmėms apmokėti, o mokėjimo laikotarpis nuo 1 iki 5 metų.

Plačiau

General Financing suteikia galimybę už mokymus mokėti išsimokėtinai. General Financing užtikrina itin lanksčias ir patogias atsiskaitymo sąlygas – visi norintys gali mokėti lizingu iki 12 mėnesių be jokio pabrangimo.

Plačiau

Valstybė gali finansuoti mokymus bei suteikti papildomas kompensacijas mokymosi laikotarpiu: mokymosi stipendiją ir už keliones į mokymo vietą ir atgal. Susisiekite ir sužinokite Jums skiriamas finansavimo galimybes.

Plačiau

Karjeros planavimas

Workshop’ai

CodeAcademy tikslas yra ne tik suteikti žinių, tačiau ir padėti Jums persikvalifikuoti. Siekiant geriausių rezultatų Studijų studentams organizuojame 3 dalių karjeros dirbtuves, kuriuose analizuojame rinką bei Lietuvoje veikiančias įmones ir planuojame karjerą.
  • CV/Linkedin
  • Portfolio
  • Rinkos analizė

Asmeninės konsultacijos

Kiekvienam CodeAcademy studentui skiriamas individualus laikas, skiriamas padėti pasiruošti darbo pokalbiams su būsimu darbdaviu.

Įsidarbinimo galimybės

Apžvalga

Galimas Valstybės finansavimas

Valstybė gali finansuoti mokymus bei suteikti papildomas kompensacijas mokymosi laikotarpiu: mokymosi stipendiją ir už keliones į mokymo vietą ir atgal. Susisiekite ir sužinokite Jums skiriamas finansavimo galimybes. 

Programa

  1. Image Classification 75 val.

    We will start the course immediately tackling the most important and most useful application of artificial intelligence – computer vision. In this section of the course we will learn the basics of machine and deep learning. We will learn about the types of neural networks and concentrate our attention to convolutional neural networks. We’ll also spend some time learning about the current applications of deep learning and why they became so popular and effective just a few years ago. The main focus of this section are the five portfolio projects: you will build image classifiers with vastly different architectures, formats and number of classes. While working on  the projects you’ll learn the most advanced architectures, best activation functions and differences between various optimizers.

  2. Regression With Neural Networks 30 val.

    In the second section of this course we will move our focus from computer vision to structured data, which is extremely important in business, but often neglected in most of the courses neglect in the academia and research. We will do two portfolio projects predicting asset prices and sales.

  3. Inverse Image Search 15 val.

    In the third section of the course we will move return to computer vision and build a reverse image search model capable of finding similar items to the one provided by the user. This project will help us understand the underlying meaning of the weights in the deep learning models and prepare us for the recommender systems.

  4. Recommender Systems 30 val.

    In the fourth section of the course we will build two different recommender systems. While not new recommender systems saw a huge improvement in accuracy with the coming of the deep learning models. While working on the recommender systems we will learn about embeddings and collaborative filtering.

  5. Time-Series Prediction With Recurrent Neural Networks 15 val.

    Finally it is time to make some money! We will try to predict stock market movements using recurrent neural networks. While working on this portfolio project we will learn the differences between recurrent neural networks, long short-term memory networks and gated recurrent units, when to use each of those architectures and their strengths and weaknesses.

  6. Generating Music With Recurrent Neural Networks 15 val.

    We will continue our analysis of recurrent neural networks with totally different task – generating music.

  7. Creating Language Models (15 val.)
 15 val.

    In this section we will learn how neural networks learn the representations of natural language. While natural language processing (NLP) is totally new to us, we will use the familiar recurrent neural networks to tackle this problem. At the same time we will learn work2vec and glove – older linear models, reasons behind their popularity and the benefits of creating your own language model from scratch.

  8. Question matching 15 val.

    In this section we will learn how to use embeddings of recurrent neural network models to create a question answering model. We will see how to use a part of a neural network to predict the values we are after even if the initial goal of the model was to predict something completely different.

  9. Creating Chatbots 30 val.

    In this section we will see how to build a chatbot. We’ll put together everything what we have learned about natural language processing and create a production version model of the chatbot.

  10. Generating Images With Deep Dream 15 val.

    In this section we will return to computer vision once again. We will apply our generation skills learned in the natural language processing to computer vision and create a convolutional neural network capable of generating images aka deep dreaming.

  11. Neural Style Transfer 15 val.

    We will learn how to visualize the activations of convolutional neural networks, how to capture the style of the image and how to transfer this style to another image without altering it’s content.

  12. Colorizing Black & White Photos 15 val.

    We will learn how to use colorize black and white photos. We will also cover generative adversarial networks and variational autoencoders.

  13. Face Unlock 15 val.

    iPhone X introduced unlocking your phone with just your face. We will learn how to implement this functionality yourself.

  14. Advanced Computer Vision 45 val.

    In this section we will focus on advanced computer vision topics such as object detection and segmentation. You will learn how to implement YOLO and SSD algorithms.

  15. Deep Reinforcement Learning 30 val.

    If deep learning is the hottest field of the artificial intelligence then reinforcement learning is the hottest part of deep learning. We will learn about the underlying fundamentals of reinforcement learning and move to the current implementations such as asynchronous actor critic agents and deep Q-learning.

  16. Doing Inference on the Client 30 val.

    Sometimes we need that our models work while the user is offline or we have simple models and the latency costs are just too high. We will learn how to transform our models and use them on the browser with Tensorflow.js

  17. Capstone Project 75 val.

    During the final part of the course you will work on your capstone project. You will be able to apply everything that you learned during the course to create a great AI project. While you are working on the project we will also review your Github portfolio, LinkedIn profile and conduct mock interviews to prepare you for getting a job as a deep learning/machine learning/artificial intelligence engineer.

Dėstytojai

Dovydas Čeilutka

Linkedin Python
Machine learning engineer at Vinted and the dean of Vilnius School of AI.

Datos ir kainos

  • Laikotarpis
    2019 m. sausis
    Trukmė
    720 val. (480 kontaktinės val.)
    Laikas
    18:00 - 22:00
    Kaina
    nuo 3500 € arba 100€/mėn. išsimokėtinai. Galimas valstybės finansavimas

Registracija