Deep Learning SIMPLIFIED: The Series Intro – Ep. 1
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Deep Learning SIMPLIFIED: The Series Intro – Ep. 1

If you’re like most beginners, trying to learn about Deep Learning feels like taking a drink from a firehose you’re hit with too much complicated info too quickly, and most of it ends up seeping out of your mind If you’re tired of all that, then you’re gonna love the series I’ve created for you! My goal is to simplify everything so that you know just enough to make sense out of all those technical details If you’ve ever tried to look into Deep Learning in the past, you probably immediately came across terms like Deep Belief Nets Convolutional Nets, Backpropagation, non-linearity, Image recognition, and so on Or maybe you came across the big Deep Learning researchers like Andrew Ng, Geoff Hinton, Yann LeCun, Yoshua Bengio, Andrej Karpathy If you follow tech news you may have even heard about Deep Learning in big companies Google buying DeepMind for 400 million dollars, Apple and its self-driving Car nVidia and its GPUs and Toyota’s billion dollar AI research investment. But there’s one thing that’s always hard to find: an explanation of what Deep Learning really is in simple language that anyone can understand Videos on the topic are usually either too mathematical have too much code or are so confusingly high level and out of reach that they might as well be 100,000 feet up in the air In this series, I’m going to explain Deep Learning to you without scaring you away with all that math and code It’s not that the technical side of Deep Learning is bad. In fact, if you want to go far in this field, you’ll need to learn about it at some point. But if you are like me, you probably just want to skip to the point where Deep Learning is no longer scary and everything just makes sense. I know it sounds intimidating since there’s so much information, but that’s why I’m here to help! At the very least, I want to get you to the point where you know how to take advantage of all the great Deep Learning software and libraries that are available. If you’ve ever struggled with finding clear information on Deep Learning, please comment and let me know your thoughts! Over the next several videos, I wanna bring you along step by step until you know just enough where everything starts to make sense. You won’t know everything about the field, but you’ll have a better idea of what there is to learn and where to go next if you’re interested in learning more. We’ll start with some basic concepts about Deep learning. We’ll touch on the different kinds of models and some ideas for choosing between them. And don’t worry – like I promised, we’ll skip the math and go straight to the intuition. Later, you’ll learn about some different use cases for Deep Learning. Then after that, we’ll get to the practical stuff – first you’ll see some platforms that allow you to build your own deep nets, and then you’ll learn about software libraries you can use for your own personal apps. YouTube is a great channel for these lessons because communication doesn’t have to be one way. If you ever feel that I’m being unclear or there’s anything you’d like to add, feel free to leave a comment and contribute. The other viewers and I all want to hear from you!


  • OHM-968692

    Looks like a good start. I was actually looking for a super simple, basic explanation of how it works. But if you can explain all of that simply and clearly, then why not!

  • Steve Lee

    I watch the first 10 series of your video and this is completely scripted. I don't believe you understand this topic well enough to be presenting Deep Learning. There are other video much better than your series. Perhaps you can learn from others on how to present Deep Learning.

  • Andrea Madonia

    I really like this intro and now I'm going to start with the serie. You have a nice voice and a clear way to explain the arguments. Now you have a new subscribed!
    I'm sorry if I write something wrong, but I'm italian 😉

  • eric paul

    thank you very much for this awesome simplified detailed series
    i have watched dozens of videos ,but your series is the best ever

  • NightLurk

    After spending 2 minutes watching the video tell me how they're gonna show me what I came here to watch, I paused, left a dislike and this comment and left! Bye!

  • tony james

    don't watch this video,

    all they are going to tell here how they are going to explain deep learning in their future videos.

  • Oscar Alsing

    This is wonderful! I strongly argue that everyone should have some knowledge in AI, and that the general understanding of IT will be essential for everyone (regardless of their profession), in the future.

    Thank you for sharing this with us.

    If you're interested, I'm hosting a series on "Artificial Intelligence For Everyone", which briefly explains all of the various topics involved in Artificial Intelligence, Deep Learning and Machine Learning! 🙂

  • Sam

    The title is literally "The Series intro", wtf you all complaining about? You expecting to make a self driving car from the first video or some shit? STFU.

  • Ishank Goel

    Nice Intro. thanks.. 🙂 can you tell me how to make videos like that or the software used ?? Will be a great help.. thanks in advance

  • Henry Alexander

    Lets unifie, make money , and be more than selfgorified well know fact king and queens, and become in the image of a deity.

  • Charles Aydin

    Thanks a lot! As a data scientist, I find the series very useful for the lay audience. Which brings me to my next point about some of the, let's just say really unfortunate comments. I think it's made clear from the beginning that this is a high level introductory series, and the first video will be accordingly, well even more so introductory. I wonder what some of the commenters were expecting before watching the video (or even the entire series), that someone would be inserting a disk into their brain like in the movie Matrix and "install" Deep Learning? Not to mention that nobody is forcing anyone to watch videos on Youtube, it's a personal choice…

  • voltaspeeder17

    If you put a word "SIMPLIFIED" into the title, you should AT LEAST tell us a basic definition of the thing or something close to it. I feel i know even less about the "Deep learning" than i did before watching the video.

  • dhiya-eddine Nadour

    Hi I love your présentation of deep learnign. It helps me to do a little work for my school.

    What is the name of the software you use to do your présentation? I mean with connected cell, zoom in, and translation point by point?

    It can help me for seduce my professors haha

  • AlphaMineron

    I did first 2 chapters of Michael Nielsen’s online book on Machine Learning. Today I’m starting your videos, let’s see where this journey takes me

  • Bostonite1985

    Here is Deep Learning in a simple explanation: People who watched this video may have different expressions on their faces. Some may say, "This is complete waste of time"…"WTF did I just watch. It does not explain anything"….Some might think, "This video is fantastic. The lady has clearly explained what deep learning is."… The AI in each viewers' computer reads the facial expressions of thousands of viewers, reads their comments, interacts with AI in all other computers and comes up with a conclusion of what majority of people think of this video and throws up a meme in the screen hoping that the uploader of the video gets the message. That is Deep Learning simplified.

  • BddyMe a

    Never struggled finding clear information on Deep Learning because your video is the first video on Deep Learning for me so its clear. Thanks haha 🙂

  • Hannes Space Nerd

    Hi There. I simply love your presentations. Can i as what tool you are using. I am looking for something I can visually explain things

  • Shadab Kazi

    Deep learning explained in simple words

  • Thiago Carvalho

    OK, vou te indicar alguns pensamentos meus sobre métodos de ensino.
    Primeiro: Acredito que nós só aprendemos em duas situações: Por prazer ao assunto e por necessidade. No prazer, você tem que conquistar a pessoa de uma forma parecida ao que acontece na propaganda e no cinema. Envolva as emoções e sensações básicas dos seres humanos. E para isso você precisa, por exemplo, fazer conexões com situações que a pessoa já passou. Mas este é só um pequeno exemplo. Já no caso da necessidade você precisa mostrar a aplicação daquele conhecimento no cotidiano da pessoa. Na vida dela. Ou no mínimo, no trabalho/profissão dela. Mas tem que ser algo bem de imediato. E digo de imediato pois as vezes lidamos com crianças ou adolescentes forçados a aprender algo. Nestes casos, uma possível saída, é tentar cativá-los com a descrição do cotidiano e dos resultados no nosso mundo de uma determinada profissão/carreira. No clássico: O que você quer ser quando crescer?
    Segundo: Nós somos bem visuais. Então exemplos visuais são importantes. Audio-visual também ajuda.
    Terceiro: Outra situação são coisas como: exemplos, repertório, experiências. Pense num artista que precise desenhar um guepardo. Quanto mais informação(principalmente visual, neste caso) ele tiver de guepardos, melhor ficará o seu desenho final. Repertórios são importantes para desenvolver uma base de experiências para aprendermos algo. Aliás, não é bem assim que as máquinas aprendem? Por vários imputs de referência?
    Quarto: Bons meios de se ensinar quaisquer coisas são: A história sobre aquele tema. Reportagens sobre o assunto. De preferência atuais, senão vai se encaixar na história. Ou neste próximo exemplo: Livros e literatura sobre o tema(inclui artigos, documentários, fóruns…). Glossários e termos sobre o assunto/tema. Imagens e multimídia que ajude no repertório e nas experiências do aluno.
    Quinto: Trate o assunto de forma filosófica, artistica, matemática/exata e científica. Qualquer assunto! Sério 🙂 !
    Neste caso, nós temos as abordagens filosóficas envolvendo conceitos de IA. Usar técnicas clássicas e modernas de filosofia, ou seja, de pensamento organizado, para podermos compreender melhor algo em torno de um assunto. Como IA de forma genérica. Ou mesmo: Como resolver um determinado problema? E como resolvê-lo usando deep learning?
    Trabalhar de forma artística significa, basicamente, não há regras. Também significa que você tem que ensinar principios de criatividade, e de coisas como psicologia(não-clínica); enfim de processo criativo. Como os conceitos no livro Roube como um Artista(Cópia, Modificação e Combinação). Aliás, este trio é mais um elemento de IA, neste caso ligado à criação e arte, não?
    Científica vem da ideia do uso do revolucionário método científico. Apesar de metódico ele é muito preciso e eficiente. Você só precisa de coisas como pesquisas, referências, experimentos, estudos, e várias outras coisas para ir testando hipóteses suas. E então ir compartilhando e discutindo os resultados dos seus pensamentos e ideias com outras pessoas para encontrar fatos sobre o assunto e chegar a conclusões melhores.
    Já a matemática se aplica à interpretação matemática de um a determinada situação. O que por sua vez, ao meu ver, envolve um pouco de arte, filosofia e ciência. Só que aplicado à área de exatas e à precisão, controle e previsibilidade dos números. Infelizmente, neste caso, sempre vai se exigir conhecimentos mais técnicos de matemática. Conhecimentos estes que podem existir ou ter sido um tanto destruidos pelos métodos de ensino(forçado) da escola.

  • Hannah Humphreys

    Statistics Essentials for Analytics

    > Understanding the data
    > Probability and its uses
    > Statistical Inference
    > Data Clustering
    > Testing the data
    > Regression Modelling

    #Statistics #DataAnalytics

  • Juan Nagle

    Deep Learning is a great way for terrorists, disgruntled postal workers, and students with test anxiety to program an artificial intelligence drone, airplane, truck or car full of explosives to go blowup a university, airport, governmental installation or their grandmother's house.

  • Rachit Sharma

    But what is deep learning????? I was expecting an answer like…. 'it's a revolutionary technology that helps in…… '. What i got was… 'this course do wonders and it is so well formed…. '. U never reached a point!

  • Hannah Humphreys

    Introduction to Data Science- Bootcamp
    > Build a foundation in R programming and statistics.
    > Be ready to apply for data analytics and junior data scientist roles, and take on freelance projects.

    #DataScience #Bootcamp # R

    Enroll :

  • Павел Рябов

    Unfortunately, there was no answer to the question "What is deep learning?" until the moment I had enough patience to watch – 2:05

  • Tigga321

    This video was useless. The entire video was about how complex deep learning is – but it doesn't explain what it is LOL. I'm guessing that's EP2?

  • B

    "Sooo… Because most people are fucking retarted and can't handle a 40' fully comprenhensible video on what the heck deep learning is, I decided to make a MOTHERFUCKING THIRTY ONE PART series for 4th graders."
    I swear, that's what I heard.

  • corvus monedulas

    I'll totally watch the next video but I just wanted to let you know that a big "yo here's what I'm gonna do" for 4 minutes gives off huge priming/sales pitch/scam vibes which could potentially deter a lot of people.

  • Ali Asad

    This is such a great intro video. I've started following Andrew Ng course on Machine Learning last year, but I didn't complete it because the course gets harder and much more complicated week by week. I'm glad that I've come across your video and I hope I will be persistence throughout the course and will get some confidence on the subject.

  • naima azizi

    This is so awesome! I'm glad I found you! thanks so much for doing this!
    Thanks for creating this series. Looking forward to the next videos.

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