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

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In this video we start by walking through some of the basics. We look at why we use neural networks and how they function. We do an overview of network architecture (input layer, hidden layers, output layer). We talk a bit about how you choose how many hidden layers and neurons to have. We also look at hyperparameters like batch size, learning rate, optimizers (adam), activation functions (relu, sigmoid, softmax), and dropout. We finish the first section of the video talking a little about the differences between keras, tensorflow, & pytorch.

Next, we jump into some coding examples to classify data with neural nets. In this section we load in data, do some processing, build our network, fit our data to it, and then finally evaluate our model. The examples get more complex as we go along. Some setup instructions for the coding portion of the video are found below.

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

I’m going to post a follow up video to this soon where we walk through a real world example where we automatically classify images of hands for the game of rock, paper, scissors. Hopefully that should be up about 2 weeks from now. (EDIT: part 2 has been posted, link below)

If you enjoyed this video, make sure to like & subscribe. Feel free to leave any questions in the comments section.

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 (architecture basics)
6:11 Hyperparameter overview (batch size, optimizer, dropout, learning rate, epochs)
7:53 How do we choose layers, neurons, & other parameters?
9:08 Why do we need an activation function?
10:20 What activation function should I use?
11:25 Keras vs Tensorflow vs PyTorch
12:30 Coding starts (github & setup)
14:07 Writing our first neural network (linear example)
18:45 Selecting optimizer & loss function (model.compile)
23:45 Fitting training data to our model (model.fit)
27:31 Shuffle order of training data
30:12 Evaluate model on test data (model.evaluate)
32:00 Example #2: Classifying quadratic data
36:06 Example #3: Classifying 6 clusters of data (try on your own)
41:03 Using network to predict a single data point (model.predict)
43:27 Example #4: Classifying multiple labels at a time (BinaryCrossentropy loss)
55:19 Example #5: Classifying our complex data from start of video
59:00 Conclusion & Next steps of learning neural nets

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