Aparajita Ojha
Aparajita Ojha
  • Видео 96
  • Просмотров 106 641
Artificial Intelligence: Introduction -Past, Present and Future
Starting with the early developments in AI, the video gives an introduction to various types of AI based on capabilities or functionalities. A brief introduction to machine learning and deep learning is also given.
Просмотров: 374

Видео

Introduction to Generative Adversarial Networks -Part 2
Просмотров 3084 года назад
Introduction to Generative Adversarial Networks -Part 2
Introduction to Generative Adversarial Networks - Part 1
Просмотров 3564 года назад
Introduction to Generative Adversarial Networks - Part 1
Robot Motion Planning - Artificial Potential Field Method
Просмотров 17 тыс.4 года назад
Robot Motion Planning - Artificial Potential Field Method
Roadmap Based Path Planning: Visibility Graph and Generalised Voronoi Diagrams as roadmaps
Просмотров 13 тыс.4 года назад
Roadmap Based Path Planning: Visibility Graph and Generalised Voronoi Diagrams as roadmaps
Path Planning and Robot Navigation: Part 1 - Introduction and Bug algorithms
Просмотров 8 тыс.4 года назад
Path Planning and Robot Navigation: Part 1 - Introduction and Bug algorithms
You Only Look Once: Object Detection Algorithm Part 2
Просмотров 7 тыс.4 года назад
You Only Look Once: Object Detection Algorithm Part 2
You Only Look Once - YOLO: Object Detection using Convolutional Neural Networks
Просмотров 36 тыс.4 года назад
You Only Look Once - YOLO: Object Detection using Convolutional Neural Networks
Fractionally Strided or Transposed Convolution through an example
Просмотров 5 тыс.4 года назад
Fractionally Strided or Transposed Convolution through an example

Комментарии

  • @harshalbhat5638
    @harshalbhat5638 Месяц назад

    Really good Explanation!

  • @கோபிசுதாகர்
    @கோபிசுதாகர் 7 месяцев назад

    Thank you, professor Aparajita.

  • @yashkhairnar9166
    @yashkhairnar9166 Год назад

    Can you please share the slides ?

  • @suriyars4487
    @suriyars4487 Год назад

    What is the book name , I couldn’t understand the name !

    • @aparajitaojha807
      @aparajitaojha807 Год назад

      Principles of Robot Motion Theory, Algorithms, and Implementations by Howie Choset et al.

    • @suriyars4487
      @suriyars4487 Год назад

      thank you !@@aparajitaojha807

  • @praveen-z4w
    @praveen-z4w Год назад

    where i can find the previous part of this session?

    • @aparajitaojha807
      @aparajitaojha807 Год назад

      What exactly do you want to learn ?

    • @praveen-z4w
      @praveen-z4w Год назад

      i want to learn hyperledger fabric writing, testing of the chaincode. he mentioned we discuss in previous session was curios waht did i missed@@aparajitaojha807

  • @MohitThehuman
    @MohitThehuman Год назад

    Hi thank you for the tutorial I tried to delete a car from couchdb container it got deleted (that is fine ) But when i try to query it from queryallCars.js It is also not showing there Does the query script fetch data from couchdb or ledger ?

  • @learnprogam
    @learnprogam Год назад

    Super

  • @quocanhnguyen7275
    @quocanhnguyen7275 Год назад

    AMAZING. Thank you professor

  • @quocanhnguyen7275
    @quocanhnguyen7275 Год назад

    Thank you so much Professor. Amazing and understandable lecture

  • @mohammedsohaib9724
    @mohammedsohaib9724 Год назад

    Thanks mam for thid lecture very usefull.

  • @ecemozturk1507
    @ecemozturk1507 Год назад

    all the dark blue and dark red text colors on terminal makes impossible to read the commands. Wish you had used a white background or lighter colored text

    • @aparajitaojha807
      @aparajitaojha807 Год назад

      Thanks for the input. We will use light back ground from now onwards.

  • @aghanjack
    @aghanjack 2 года назад

    is there a link to these collaboration notebooks - would help the pedagogy.

  • @arulprasad4786
    @arulprasad4786 2 года назад

    The last error, thats exactly where i am stuck now.

  • @shabanaoc9750
    @shabanaoc9750 2 года назад

    Useful for my project 👏

  • @hamzabrahmi8147
    @hamzabrahmi8147 2 года назад

    thank you

  • @arivujothi0092
    @arivujothi0092 2 года назад

    thanks mam

  • @rajshreesrivastava7650
    @rajshreesrivastava7650 2 года назад

    Hello mam, Nice explanation Can u plZ tell how we can use gan for data augmentation and deep learning alexnet /resnet-50/ vgg 16 for classification

  • @rajshreesrivastava7650
    @rajshreesrivastava7650 2 года назад

    Hello sir, Nice explanation sir Can u plZ tell how we can use gan for data augmentation and deep learning alexnet /resnet-50/ vgg 16 for classification

  • @veereshpatel.46
    @veereshpatel.46 2 года назад

    If possible please do course on 1... Go programming language 2... Web3. 0 with Ethereum 3... Frontend development with Reactjs

  • @veereshpatel.46
    @veereshpatel.46 2 года назад

    Madam please add what topic going on

  • @veereshpatel.46
    @veereshpatel.46 2 года назад

    Chain code by -----)) javascript, nodejs & golang

  • @selvichandran9229
    @selvichandran9229 2 года назад

    Detailed explanation. Very clear sir.

  • @newborn7850
    @newborn7850 2 года назад

    Sir React js on frontend and solidity , connecting with metamask n ganache is been a a hard topic specially in frontend react js n web3 js is having their own stuff which is very difficult to understand. Pls do one series on Frontend React web3 connecting to ganache using metamask

  • @sanjalishreya2657
    @sanjalishreya2657 2 года назад

    Thanks a lot Sir for elaborated with eg

  • @pankajjoshi8292
    @pankajjoshi8292 2 года назад

    from where will we get Zoom Link to jopin live seessions pls

  • @amanraja8499
    @amanraja8499 2 года назад

    Truffle develop not working showing Error

  • @pankajjoshi8292
    @pankajjoshi8292 2 года назад

    One of the best solidity use cases described from scratch. Sir we need Supply Chain System and IOT Smart contract in solidity

  • @learnconcepts1608
    @learnconcepts1608 3 года назад

    Excellent mam. Very useful content

  • @AjayPateldeveloper
    @AjayPateldeveloper 3 года назад

    I really loved the concept. Very few tutorials teach with mathematics behind and you explained everything very well. Thank you so much Ma'am. I would be happy to know about you ma'am. Please update about section of this channel.

  • @HemanthKumar-cz8vl
    @HemanthKumar-cz8vl 3 года назад

    You made an one hour concept in 14:28 Minutes...Thanks a lot

  • @johnwright8002
    @johnwright8002 3 года назад

    pul vun.fyi

  • @brsrikrishna
    @brsrikrishna 3 года назад

    This was very helpful, ma'am. Thank you for making and sharing this!

  • @dongryulkim7407
    @dongryulkim7407 3 года назад

    Thank you. I have a question. How can i find matlab code in potential field method?

  • @WalidsChannel
    @WalidsChannel 3 года назад

    Thank you. If I have my own control law for robot speed based on the commanded poses, is it possible to only leverage the position coordinates from composite pot field? I am only interested in the positions that robot will traverse during the gradient descent

  • @shubhranshuranjansharma4892
    @shubhranshuranjansharma4892 3 года назад

    Well explained Mam... with excellent content..

  • @monexsharma3515
    @monexsharma3515 3 года назад

    5:34 Is d(q,qg) = (q-qg). Then Grad potential function = Zhi

  • @kumarparth444
    @kumarparth444 3 года назад

    Please explain more videos on ml for example air index, rainfall, stock prediction, etc

  • @harehnkaundun1406
    @harehnkaundun1406 4 года назад

    Nice video

  • @umamaheswararaomeleti8878
    @umamaheswararaomeleti8878 4 года назад

    Good explanation, clear enough with suitable example. it will be great if you can share the slides.

  • @ishanarora2325
    @ishanarora2325 4 года назад

    Hello mam can u explain that if a image contains 4 objects what is the label vector for that?

    • @aparajitaojha807
      @aparajitaojha807 4 года назад

      For 4 objects, the label will be like this. [ 1, x1,y1, h 1,w1, this is for object 1, 1, x2, y2, h2, w2, ...., 1, x4,y4, h4, w4]

  • @ssp3839
    @ssp3839 4 года назад

    how to give threshold for for 7 x 7 grid ?

    • @aparajitaojha807
      @aparajitaojha807 4 года назад

      What threshold are you talking about ? If it is related to IOU, we generally give a threshold of 0.5 or 0.6. If the data is having very complex samples, then even a small threshold will do.

    • @ssp3839
      @ssp3839 4 года назад

      @@aparajitaojha807 IOU mam thanks now it's clear

  • @jawaharali5568
    @jawaharali5568 4 года назад

    loved it

  • @navanitpankajdubey4687
    @navanitpankajdubey4687 4 года назад

    Ma'am what about A* algorithms ? Does they also come in Path planning algorithms?

  • @mekaladharani7233
    @mekaladharani7233 4 года назад

    hlo mam......can u explain how to develop and execute a code for object detection using cnn

  • @drilyasmeo
    @drilyasmeo 4 года назад

    Nice explaination Prof. Please keep it up & share more and more videos in Robot Nav area

    • @aparajitaojha807
      @aparajitaojha807 4 года назад

      Thank you, glad you like it. Right now busy with my current semester courses. Sure, I will. Regards

  • @sachinbahade9982
    @sachinbahade9982 4 года назад

    If I get an early reply, it would be very helpful, output feature size is 19x19. How they will create a label this downsampled size. How it mapped on to the original size.

    • @aparajitaojha807
      @aparajitaojha807 4 года назад

      19X19 gives information about each grid cell. So each cell corresponds to a pixel block, where one can find if there is an object present or not, using the confidence score. And if the confidence score is high, it predicts the class of the object, by multiplying confidence score with class score. for that pixel block [ cell]. Then for object of the predicted class, it takes into account the box estimation [anchor box], centre height and width. Finally for each grid cell, it gives you object class, and its bounding boxes, if the object is predicted in the box. Hope this is clear.

  • @Cherry-xd7xe
    @Cherry-xd7xe 4 года назад

    Simplest way to detect object using Machine learning Checkout the work here. ruclips.net/channel/UCGAhCh-ELf0zFQJZt86622w Enroll here: tiny.cc/googleForm

  • @nazmultakbir6816
    @nazmultakbir6816 4 года назад

    Thanks for the very clear explanation.

  • @ankitasontakke138
    @ankitasontakke138 4 года назад

    Simple and nice explanation

  • @47lokeshkumar74
    @47lokeshkumar74 4 года назад

    Hi Mam, The artificial potential method for path planning used the step gradient descent method but the grad of attraction and repulsion where to fit to get main matrix in path planning. lokesh.singh.in@gmail.com

    • @aparajitaojha9741
      @aparajitaojha9741 4 года назад

      Thanks for watching. Suppose you are at a position x(t) at time step t. The next position at the time step t+1 will be defined using gradient descent. x (t+1) = x)t) - (alpha) grad U(x) . Here we update the sensor inputs to find out if there are obstacles around. Then modify our total potential function U(x) accordingly. Find out it’s gradient by combining both the attractive and repulsive potential. Use it in the formula. Then update x(t+1).

    • @47lokeshkumar74
      @47lokeshkumar74 4 года назад

      @@aparajitaojha9741 can you ur email id. I will write some code to you in matlab.

    • @aparajitaojha807
      @aparajitaojha807 4 года назад

      I do not use matlab. My email is aojha@iiitdmj.ac.in

    • @47lokeshkumar74
      @47lokeshkumar74 4 года назад

      @@aparajitaojha807 Then u use python for path planning computation

    • @aparajitaojha9741
      @aparajitaojha9741 4 года назад

      Lokesh Kumar yes