⌨️ () Introduction
⌨️ () What is Deep Learning
Why no one is talking about this funny cat 😂😂😂Sooo cuteee 😍😍😘😘😘😘😘😘😘😘😘
In 2016, Alphago beated Lee Sedol. Not in 2015. Am i wrong ?
⌨️ () Introduction to Neural Networks
⌨️ () How do Neural Networks LEARN?
() That formula is incorrect. There is only one bias value per neuron, not "n". Thus, the formula should have "b" not "b_i".
* - "Channels have weight". And the slide matches it. But 7:08 says the weight is something of a neuron (how important is the neuron, rather than how important is the relationship). I think it is confusing; aren't weights a property of the relationship/channel, in graph theory?
() Weight is assigned to a link and not a neuron right? And the bias term shifts the graph up or down, not left or right.
⌨️ () Core terminologies used in Deep Learning
⌨️ () Activation Functions
just saving :)
gmoid function ( on your video) is not [0,1] but rather ]0,1[ as a 0 and a 1 can only be obtained towards +-infinity. Same applies to the hyperbolic tangent ranging from ]-1,+1[ and not from [-1,+1]
why not just use multiple activation functions?if your goal was to find a specified output does running data through multiple "filters" be optimal?
⌨️ () Loss Functions
⌨️ () Optimizers
* inside slides that list advantages/disadvantages you might use the color red for disadvantages, and not just always green to highlight terms. Ex. "small" at .* I like the examples of descending the Everest, and the one about memorising songs.
⌨️ () Parameters vs Hyperparameters
⌨️ () Epochs, Batches & Iterations
⌨️ () Conclusion to Terminologies
⌨️ () Introduction to Learning
⌨️ () Supervised Learning
* - the slides disappears till 40:57
⌨️ () Unsupervised Learning
⌨️ () Reinforcement Learning
⌨️ () Regularization
⌨️ () Introduction to Neural Network Architectures
⌨️ () Fully-Connected Feedforward Neural Nets
* same at ; till 53:11
⌨️ () Recurrent Neural Nets
* - "sometimes you may find the ???? depicted over time" (the automatic subtitles don't get it either)
* - audio says "input gate, output gate, and a forget gate"; but the slides shows "Update, Reset & Forget gates".
⌨️ () Convolutional Neural Nets
xD
⌨️ () Introduction to the 5 Steps to EVERY Deep Learning Model
⌨️ () 1. Gathering Data
* - "although if you are interested I'll leave them in the notes below"; yet, it would be useful if a list (or a link to more info) could be added in the description.
⌨️ () 2. Preprocessing the Data
⌨️ () 3. Training your Model
⌨️ () 4. Evaluating your Model
⌨️ () 5. Optimizing your Model's Accuracy
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