Explanation of 3 common activation functions on Deep learning by usability.
Why Activation Function so Important?
We can imagine activation Function is a thing that firing our brain (in this case neuron) to think. Maybe that illustration makes you more confuse :P
Anyway.. Without activation Function every calculation in each layer doesn’t have a meaning…
If the Sequence is matter, then LSTM is good for your Machine Learning Layer.
The Purpose of Recurrent neural network (RNN)
When we want to Predict sequential series of data. For examples sentences, cryptocurrency or stock. We can train the datasets using RNN.
LSTM on RNN
And then the layer that used to RNN training usually we use LSTM.
What is LSTM
We…
Understanding the weights calculation with dot product and biases behind of Deep Learning
Weight
First of all if you use Keras as library, Keras provides some random weights. These weights will be the multiplied by features given from training parameter. And sum all the results.
The value of the weight will be optimized during training process, with optimizer, depends on the result of each…
Over Fitting
When is it happened?
- When the delta from your Train Accuracy and Test Accuracy is too much.
- When Test Accuracy value is larger than Train Accuracy
Why is it Happened?
Usually overfitting occurs when you have too much Layer or too much training for your model.
How can we reduce OverFitting?
- Making more simple training model, like remove some layer. Or use DropOut.
- Make Augmentation from our current dataset. (do pivot, flip or resizing)
- Adding more training data set.
It looks easy when you read the tutorial about convert model, with 2 lines of codes.
converter=tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
tflite_model=converter.convert()Yes it works, but in some conditions.
Three things that you must know about TFLite Converter before you use the converter:
1. Consider the Supported types
At that article, we know that TFLite converter doesn’t support string and float16, at least not yet.
Step by Step to Train your own Image Dataset for Deep Learning Using Tensorflow
Actually there is an easiest way to train you own Image. You can use Firebase Machine Learning. You only have to upload your images and define the labels. …
Yesterday, NVIDIA has released a new series for RTX… 3090.
We know that GPU makes training process for machine learning faster than use CPU. Because GPU consists of hundreds of core.
did you know, Nvidia is using 10,496 CUDA cores on the RTX 3090.
WOW!!! it’s not hundreds man!!!
I cannot imagine how fast my train execution with this machine.
Actually i don’t have this stuff yet, not yet released on my country :(
If you want to use GPU for your machine learning, you can follow this tutorial.
Next time, once i have this GPU.. i will compare the training execution time. cheers..
Anyway,, If you want to learn how to train model with easy steps, you can read this.
If you want to train your data set, then at least you must know these 3 Layers.
Layers
Dense Layer
We called this “the most well-known type of the Layer”. You can use this layer if you want to adding more Layer because your model need to more memorize some values.
Too much…
I have TFlite Model and i want to run checking image on Desktop.
Preparation
Python
If you don’t have Python on your PC then you can install it from: Getting Start with Python.
TFLite Model
You can use my sample TFLite Model. Inside my TFLite model there are 4 labels. indosiar_iklan, indosiar, sctv and…
Checking State from Image with TensorFlow is very easy.
Background Story
One day at office time, i am staring at local TV Channel playing on our App. My job is develop OTT App that provides lot of awesome content and some Local TV Channels. While i am stare at the app i…
