Introduction to Neural Networks in Python (what you need to know) | Tensorflow/Keras


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In this video we begin by strolling by a number of the fundamentals. We take a look at why we use neural networks and the way they perform. We do an outline of community structure (enter layer, hidden layers, output layer). We speak a bit about how you select what number of hidden layers and neurons to have. We additionally take a look at hyperparameters like batch dimension, studying price, optimizers (adam), activation capabilities (relu, sigmoid, softmax), and dropout. We end the primary part of the video speaking a bit concerning the variations between keras, tensorflow, & pytorch.

Next, we bounce into some coding examples to classify information with neural nets. In this part we load in information, do some processing, construct our community, match our information to it, after which lastly consider our mannequin. The examples get extra advanced as we go alongside. Some setup directions for the coding portion of the video are discovered beneath.

To set up Tensorflow, obtain Anaconda: https://docs.anaconda.com/anaconda/set up/
Data & code used in tutorial: https://github.com/KeithGalli/neural-nets

I’m going to put up a comply with up video to this quickly the place we stroll by an actual world instance the place we mechanically classify pictures of arms for the sport of rock, paper, scissors. Hopefully that needs to be up about 2 weeks from now. (EDIT: half 2 has been posted, hyperlink beneath)

If you loved this video, be sure that to like & subscribe. Feel free to depart any questions in the feedback part.

Part 2!
https://youtu.be/44U8jJxaNp8

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Video timeline!
0:00 Video overview
1:34 Why use neural networks
3:08 How neural nets work (structure fundamentals)
6:11 Hyperparameter overview (batch dimension, optimizer, dropout, studying price, epochs)
7:53 How will we select layers, neurons, & different parameters?
9:08 Why will we need an activation perform?
10:20 What activation perform ought to I exploit?
11:25 Keras vs Tensorflow vs PyTorch
12:30 Coding begins (github & setup)
14:07 Writing our first neural community (linear instance)
18:45 Selecting optimizer & loss perform (mannequin.compile)
23:45 Fitting coaching information to our mannequin (mannequin.match)
27:31 Shuffle order of coaching information
30:12 Evaluate mannequin on check information (mannequin.consider)
32:00 Example #2: Classifying quadratic information
36:06 Example #3: Classifying 6 clusters of knowledge (attempt by yourself)
41:03 Using community to predict a single information level (mannequin.predict)
43:27 Example #4: Classifying a number of labels at a time (BinaryCrossentropy loss)
55:19 Example #5: Classifying our advanced information from begin of video
59:00 Conclusion & Next steps of studying neural nets

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Comment List

  • Keith Galli
    November 10, 2020

    Hey everyone! Quick update, looks like the code for example #4 (clusters_two_categories) has been causing some people issues. Running the code that I demo in the video is resulting in low accuracy scores. I'm guessing something changed with one of the libraries used behind the scenes.

    I challenge you to try to rewrite this network from scratch and see if you are able to classify the data properly. If you are able to do this, please let me know what you changed! I want to share with everyone who is running into this problem. I'm happy to give you a shoutout if you find a solution :).

    A few suggestions that might help as you try to rewrite the network… You'll see some immediate performance boosts if you normalize the data between 0 and 1 instead of the range it currently is in. I also recommend playing around with the hyperparameters to the network (number of layers, neurons per layer, learning rate, loss function, optimizer, etc.). Maybe try using different methods to vectorize the data. Let me know if you are able to find a solution!

  • Keith Galli
    November 10, 2020

    A pure knowledge hour delivered in such lucid a manner that makes one feel comfortable and know that even if they mess up things, it can still be ok.
    No Data Science taboos when Keith is here.

  • Keith Galli
    November 10, 2020

    Its really hard to understand!! Totally baffles try to make vdo's simpler to understand

  • Keith Galli
    November 10, 2020

    Understanding the tf documentation can be overwhelming when first trying to learn the library but this was really helpful and explained clearly, thank you

  • Keith Galli
    November 10, 2020

    Thank you so much for your effort. The video really helped.
    Question: Sir, I need your guide on where to get construction management dataset relating to cost, schedule, risk, safety management, etc.
    I have been facing challenges on a dataset that I can use to practice building ANN or Deep Neural nets for project cost prediction given project features.
    Generally, since I started learning (5months ago) ANN, DL, ML, AI I discovered that the construction management data set are very difficult to find compared to other fields. I will be very glad if you can with any web link.
    Also, I would patiently wait for the part of this video.

    Thank you once again for the great job you are doing.

  • Keith Galli
    November 10, 2020

    Thanks bro love from india

  • Keith Galli
    November 10, 2020

    11:19 isn't it softmax is for multi-class and Sigmoid is for binary (yes/no type)? Thanks!

  • Keith Galli
    November 10, 2020

    Keith, when I'm running your code on network clusters with 2 categories (network_clusters_2.py) I only get acurracy of about 0.1
    Do you know what might be the problem?

  • Keith Galli
    November 10, 2020

    good content ,thanks . would you mind putting up some content of Neural Networks with pytorch kindly

  • Keith Galli
    November 10, 2020

    Hello Keith i love your tutorials!! 1 question about this one: I'm using jupyter notebook and i get very low accuracy in example #4. I just copied pasted your code, all data are loaded right and i dont know whats wrong.. Any ideas ?

  • Keith Galli
    November 10, 2020

    Really nice video

  • Keith Galli
    November 10, 2020

    We need more videos on machine learning libraries and conceptual machina learning videos like this one!! Please consider making more videos on tensor flow for image classification and NLP or a video of various applications/ideas with TensorFlow ML libraries. But i cant stress how crazy helpful conceptual videos like this are so make more of them!

  • Keith Galli
    November 10, 2020

    Mantap videonya.
    Saya juga ada nih rekomendasi channel lain buat belajar neural network siapa tau cocok hehe.

    https://youtu.be/vyAsO_fzNF8

  • Keith Galli
    November 10, 2020

    Hi Keith,i am learning python,and with that i went through your numpy and pandas tutorial.I want to apply some sort of machine learning in trading.Can you recommend me something that isnt too complicated for a beginner?Your pandas and numpy tutorials had a good pace and they were not too hard.I watched 12 min of this video and it seems so intimidating.Can you help please?Thank you

  • Keith Galli
    November 10, 2020

    Thanks, Geith for such a nice video on the subject. Please make a video on Generative Adversarial Networks (GAN).

  • Keith Galli
    November 10, 2020

    thanks so much bro. I love all your tutorial videos. As always, I learn so much from this tutorial. I am looking forward to your future videos

  • Keith Galli
    November 10, 2020

    Love your videos. I'm a high school student and I really like your vids compared to other resources I've used so far.

  • Keith Galli
    November 10, 2020

    I have a question. When you check your model's performance on the validation data, that would allow the model to see that data. Wouldn't that increase its performance the next time it sees the validation data? (Like if you tune some parameters and try again) Then that would make its future performances on the validation data not an accurate measure of its performance. That's why there is test data right? If my thinking is correct then: Is that inaccuracy so small that it can be ignored when you are tuning your hyperparameters? Or, is there no artificially increased accuracy for validation data when you re-create and re-fit the model?

  • Keith Galli
    November 10, 2020

    Keith, I did Python Course from Coursera and was looking for practicing in Data Science and Machine Learning. Luckily i found your channel and have already gone through videos of Pandas, Numpy and Sales analysis videos related to data science. Feels Super motivated after that as i learned so many things. Kindly upload more real world projects, with deeper analysis required. Love your skills, it is helping me and polishing my skills, developing my Github profile 😀 Now jumping to ML and AI related topics as I want to switch my career to DS and ML, so to be successful in that learning ML models are key. Also, following Andrew Ng. Can you refer me good books or resources where you learnt from. I would be glad.. Best Online Teaching Channel Ever.

  • Keith Galli
    November 10, 2020

    Yea bro Plzz make videos more often I learn many things from Ur video's tq very much bro love Ur video's

  • Keith Galli
    November 10, 2020

    Can you please make a setup tour?

  • Keith Galli
    November 10, 2020

    youre not like the other tutorial bois. you explain like youre talking to a person and not a robot in taking info and the amount of useful info that makes you understand perfectly is extremely balanced and i just love you and please keep posting. you dont know how many people you help , and the help is freakin amazing too. love u

  • Keith Galli
    November 10, 2020

    When I run your clusters with two categories code, I get about 10% accuracy (while you get above 90% accuracy in the video), any idea what is wrong?

  • Keith Galli
    November 10, 2020

    when part 2 😛

  • Keith Galli
    November 10, 2020

    Hi Keith!
    I thought you're just working on data manipulation tasks since your Pandas tutorial. But it seems like you're covering the same topic I'm interested in. So I was wondering if you have any study group. If yes, then I'd like to join you and proceed along with you. Don't know how much I can help you, but it might be very helpful for me. So please let me know if you have any study group or want to open a discord channel to discuss the topics. Maybe it will also help you making new videos. Looking forward to hearing from you. Good luck!

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