AI-From-Scratch | Understanding Artificial Intelligence from the scratch | Machine Learning library
kandi X-RAY | AI-From-Scratch Summary
kandi X-RAY | AI-From-Scratch Summary
We are a species who are highly intelligent capable of turning events. We can make war, we can make coffee and start a revolution. Time in its very essence has revealed invaluable secrets to human beings about its nature which has allowed us to evolve and hence made us greater than any other species around us. The capabilities of human beings are just expanding, from creating a wheel to splitting the atom, we have seen everything that makes up a species like us. And now we are aiming for the stars. Our intelligence is expanding at an incredible rate. The thought of what we can do relies on the deepest part of our brain. At one point we are aimless, with no purpose in our lives -- we feel contempt about ourselves, we feel invaluable -- but other times we are as strong like never before. Our intelligence is completely different from what we feel, the ground truth is we have a system build inside of us that has invaluable information stored within itself. It is only a matter of choosing. Once we choose to rely on the truth, we can unravel all the mysteries of the universe. Being intelligent is all about attaining skills and knowledge to make a decision and comprehend the world in all its beauty. Our brain is one of the most important parts of our body. But, why it is important? I believe because they can observe, store, think and make decisions. The brain is one of the most complex organs in a human system. It carefully observes all the information and stores it inside of it. Memories aren’t stored in just one part of the brain. Different types are stored across different, interconnected brain regions. For explicit memories – which are about events that happened to you (episodic), as well as general facts and information (semantic) – there are three important areas of the brain: the hippocampus, the neocortex and the amygdala. Implicit memories, such as motor memories, rely on the basal ganglia and cerebellum. Short-term working memory relies most heavily on the prefrontal cortex. The role of a brain in a human system is not limited to observing and storing information but far more than that. It controls the whole human system. Right from the eyes to the ears, our heart, our lungs, every part is controlled by the brain. Its main function is to survive, evolve and add meaning to life. Besides talking about the subconscious, the brain has played in the major part for the survival of the Human race. But what if we can replicate the brain outside the Human system? Yes, we can. Welcome to Artificial Intelligence. What is Artificial Intelligence?. It is derived from two words Artificial and Intelligence. Let's break them down. Artificial is something that is not real and which is kind of fake because it is simulated. The simplest thing what I can think of which is artificial grass or even light. Artificial grass is not real grass nor is light, so the latter is kind of fake. It is used to substitute real grass for various reason. Artificial grass is often used for sports because it is more resistant and therefore can be used longer than real grass. It is also easier to care than real grass. And when we talk about artificial light like a bulb or led they can be used whenever we want them to use, unlike sunlight which is wrap-around time and which can only be used when time allows us to use. But that is not the point I want to make. The point is, that there are reasons why some things are artificial and substitute real things. Intelligence is a very complex term. It can be defined in many different ways like *logic, understanding, self-awareness, learning, emotional knowledge, planning, creativity and of course problem-solving. We call us, humans, intelligent because we all do these mentioned things. We perceive our environment, learn from it and take action based on what we discovered. The same applies to animals. The interesting point about intelligence on animals is, that there are many different species and because of that we can compare intelligence on between species. In both cases -- human intelligence and animal intelligence -- we talk about Natural intelligence. Next to humans and animals there has been argued about plant intelligence. Intelligence in plants shows off the kind of different from humans or animals. The main reason is here that plants are not having a brain or neuronal network, but they react to their environment. Plant intelligence is a very interesting topic on its own because plant intelligence is not instantly visible through reactions through movement or lute. But, one question arises. If both animal and human beings have brains then why do human being stand on top of the evolutionary cycle?. The answer I got while reading a book called 'Homo Deus' by Noah Yuval Harari. He mentioned that "Humans nowadays completely dominate the planet not because the individual human is far smarter and more nimble-fingered than the individual chimp or wolf, but because Homo sapiens is the only species on earth capable of cooperating flexibly in large numbers. Intelligence and toolmaking were very important as well. But if humans had not learned to cooperate flexibly in large numbers, our crafty brains and deft hands would still be splitting flint stones rather than uranium atoms". Coming back to our topic. We should keep in mind that if we talk about Artificial Intelligence (AI) we refer to a subfield of Computer Science. To understand more about Artificial Intelligence we look at the history of Artificial Intelligence to see what it is capable of and how his status quo is related to the present.
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Trending Discussions on Machine Learning
QUESTION
I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.
I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.
I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.
...ANSWER
Answered 2022-Feb-17 at 10:47You should try to export the model using torch.onnx. The page gives you an example that you can start with.
An alternative is to use TorchScript, but that requires torch libraries.
Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html
ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)
A running exampleJust modifying a little your example to go over the errors I found
Notice that via tracing any if/elif/else, for, while will be unrolled
QUESTION
I'm trying to implement a gradient-free optimizer function to train convolutional neural networks with Julia using Flux.jl. The reference paper is this: https://arxiv.org/abs/2005.05955. This paper proposes RSO, a gradient-free optimization algorithm updates single weight at a time on a sampling bases. The pseudocode of this algorithm is depicted in the picture below.
I'm using MNIST dataset.
...ANSWER
Answered 2022-Jan-14 at 23:47Based on the paper you shared, it looks like you need to change the weight arrays per each output neuron per each layer. Unfortunately, this means that the implementation of your optimization routine is going to depend on the layer type, since an "output neuron" for a convolution layer is quite different than a fully-connected layer. In other words, just looping over Flux.params(model)
is not going to be sufficient, since this is just a set of all the weight arrays in the model and each weight array is treated differently depending on which layer it comes from.
Fortunately, Julia's multiple dispatch does make this easier to write if you use separate functions instead of a giant loop. I'll summarize the algorithm using the pseudo-code below:
QUESTION
This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.
BackgroundI would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.
Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.
After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?
An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image
...ANSWER
Answered 2021-Nov-24 at 13:26What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of y_true
and y_pred
.
QUESTION
I am trying to train a model using PyTorch. When beginning model training I get the following error message:
RuntimeError: CUDA out of memory. Tried to allocate 5.37 GiB (GPU 0; 7.79 GiB total capacity; 742.54 MiB already allocated; 5.13 GiB free; 792.00 MiB reserved in total by PyTorch)
I am wondering why this error is occurring. From the way I see it, I have 7.79 GiB total capacity. The numbers it is stating (742 MiB + 5.13 GiB + 792 MiB) do not add up to be greater than 7.79 GiB. When I check nvidia-smi
I see these processes running
ANSWER
Answered 2021-Nov-23 at 06:13This is more of a comment, but worth pointing out.
The reason in general is indeed what talonmies commented, but you are summing up the numbers incorrectly. Let's see what happens when tensors are moved to GPU (I tried this on my PC with RTX2060 with 5.8G usable GPU memory in total):
Let's run the following python commands interactively:
QUESTION
I am a bit confusing with comparing best GridSearchCV model and baseline.
For example, we have classification problem.
As a baseline, we'll fit a model with default settings (let it be logistic regression):
ANSWER
Answered 2021-Nov-04 at 21:17No, they aren't comparable.
Your baseline model used X_train
to fit the model. Then you're using the fitted model to score the X_train
sample. This is like cheating because the model is going to already perform the best since you're evaluating it based on data that it has already seen.
The grid searched model is at a disadvantage because:
- It's working with less data since you have split the
X_train
sample. - Compound that with the fact that it's getting trained with even less data due to the 5 folds (it's training with only 4/5 of
X_val
per fold).
So your score for the grid search is going to be worse than your baseline.
Now you might ask, "so what's the point of best_model.best_score_
? Well, that score is used to compare all the models used when searching for the optimal hyperparameters in your search space, but in no way should be used to compare against a model that was trained outside of the grid search context.
So how should one go about conducting a fair comparison?
- Split your training data for both models.
QUESTION
I am not able to access jupyter lab created on google cloud
I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.
I tried the diagnostic tool, which gave the following result:
but didn't fix it.
Thanks in advance.
...ANSWER
Answered 2021-Aug-20 at 14:00You should try this Google Notebook trouble shooting section about 524 errors : https://cloud.google.com/notebooks/docs/troubleshooting?hl=ja#opening_a_notebook_results_in_a_524_a_timeout_occurred_error
QUESTION
I am new to Machine Learning.
Having followed the steps in this simple Maching Learning using the Brain.js library, it beats my understanding why I keep getting the error message below:
I have double-checked my code multiple times. This is particularly frustrating as this is the very first exercise!
Kindly point out what I am missing here!
Find below my code:
...ANSWER
Answered 2021-Sep-29 at 22:47Turns out its just documented incorrectly.
In reality the export from brain.js is this:
QUESTION
IF we are not sure about the nature of categorical features like whether they are nominal or ordinal, which encoding should we use? Ordinal-Encoding or One-Hot-Encoding? Is there a clearly defined rule on this topic?
I see a lot of people using Ordinal-Encoding on Categorical Data that doesn't have a Direction. Suppose a frequency table:
...ANSWER
Answered 2021-Sep-04 at 06:43You're right. Just one thing to consider for choosing OrdinalEncoder
or OneHotEncoder
is that does the order of data matter?
Most ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases e.g., for ordered categories such as:
quality = ["bad", "average", "good", "excellent"]
orshirt_size = ["large", "medium", "small"]
but it is obviously not the case for the:
color = ["white","orange","black","green"]
column (except for the cases you need to consider a spectrum, say from white to black. Note that in this case, white
category should be encoded as 0
and black
should be encoded as the highest number in your categories), or if you have some cases for example, say, categories 0 and 4 may be more similar than categories 0 and 1. To fix this issue, a common solution is to create one binary attribute per category (One-Hot encoding)
QUESTION
I am using sentence-transformers for semantic search but sometimes it does not understand the contextual meaning and returns wrong result eg. BERT problem with context/semantic search in italian language
by default the vector side of embedding of the sentence is 78 columns, so how do I increase that dimension so that it can understand the contextual meaning in deep.
code:
...ANSWER
Answered 2021-Aug-10 at 07:39Increasing the dimension of a trained model is not possible (without many difficulties and re-training the model). The model you are using was pre-trained with dimension 768, i.e., all weight matrices of the model have a corresponding number of trained parameters. Increasing the dimensionality would mean adding parameters which however need to be learned.
Also, the dimension of the model does not reflect the amount of semantic or context information in the sentence representation. The choice of the model dimension reflects more a trade-off between model capacity, the amount of training data, and reasonable inference speed.
If the model that you are using does not provide representation that is semantically rich enough, you might want to search for better models, such as RoBERTa or T5.
QUESTION
I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.
Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?
Table example:
...ANSWER
Answered 2021-Aug-11 at 15:55You could build a model like this.
x = features you have. y = true_lable
from that you can extract features importance. also, if you want to go the extra mile,you can do Bootstrapping, so that the features importance would be more stable (statistical).
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