## Check your understanding of the lesson 1

(post original in portuguese at Deep Learning Brasília - Lição 1)

Hi guys,

I did watch again the video of the lesson 1 (part 1) to get the whole image and I took notes of the **vocabulary** used by @jeremy.

**Let’s play ! OK ?**

Can you give a definition / a url / an explanation for all the followings terms and expressions ?

**If yes, you are done with the first lesson !!!**

*PS : you do not want to test yourself or you want to check your answers ? Go to the blog post “Deep Learning 2: Part 1 Lesson 1” of @hiromi : " super travail !!! "*

- course Fastai
- forum Fastai
- GPU
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- NVIDIA
- Crestle / PaperSpace
- jupyter notebook
- Data Science
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- bash command
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- Fastai Machine Learning course : prerequesite or not ?
- image Classifier
- label
- keras
- plt.imread
- plt.imshow
- python 3.6 format string
- img.shape
- 3 dimensional array (rank 3 tensor)
- Red Green Blue (RGB) pixel values between 0 and 255
- kaggal competition
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- resnet24
- ImageNet competition
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- accuracy
- train a model
- 3 lines of code
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- learning rate
- loss function
- cross entropy loss
- validation and testing set accuracy
- Fastai library
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- pytorch
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- network architecture
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- validation set dependent variable val_y
- data.classes
- classes
- object data
- object learn
- the model
- prediction on validation set
- learn.predict()
- log of the predictions : log_preds
- get the predictions on validation set np.argmax(log_preds, axis=1)
- get probabilities on dogs : np.exp(log_preds[:,1])
- numpy
- top-down, the whole game
- code driven approach
- world class neural network
- stalelite images
- structured data
- NLP classifier
- recommendation system
- text generator
- create our own architecture from scratch
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- alphago
- image classifier for fraude dectection
- machine learning
- Arthur Samuels, 1950s, ML father
- IBM mainframe
- play checkers
- traditional Machine Learning
- features engineering
- domaine experts and specialits
- algorithm (Deep Learning) :

** infinitely flexible function

** all-purpose parameters fitting

** fast and scalable
- neural network, number of simple linear layers interspersed with a number of non linear layers
- universal approximation theorem
- Fit parameters, Gradient Descent (how good are they, find a minimum on loss function curve, local miminim)
- minimum time, GPU 10 time faster than a CPU
- hidden layer
- increase of number of parameters by layer is a problem but increase number of layers is teh solution
- DL = neural network with multiple hidden layers
- Google starts using DL in 2012
- Geoffrey Hinton, DL father
- Andrej Karpathy
- inBox by Gmail
- Skype Translator
- Semantic Style Transfer
- cancer detection
- true/false positive/negative
- CNN, Convolucional Neural Network
- convolucional
- find edges
- multiplication of pixels values by a kernel (filter)
- linear operation
- linear layer
- non linear layer
- sigmoid
- Relu
- element wise multiplication
- michael Neslon
- Stochastic Gradient Descent
- derivative
- small step
- learning rate
- combine convolution, non linearity, gradient descent
- picture of what each layer learns
- parameters of the kernels are learnt using gradient descent
- learn.fit()
- learning rate not too high, but not too low as well
- choosing a learning rate
- learn.lr_find()
- best improvement of the loss before it gets worse
- learn.shed.plot_lr()
- learn.sched.plot()
- mini batches
- traing loss
- validation loss
- validation accuracy
- overfitting : stop fitting your model
- tab to get list of function
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- 1 question mark : documentation
- 2 question mark : pops up source code
- H to get short codes in jupyter notebooks
- Stop your Crestle or PaperSpace machine !
- use the fastai forum !