Data Science & Machine Learning
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📈 تحلیل کانال تلگرام Data Science & Machine Learning
کانال Data Science & Machine Learning (@datasciencefun) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 77 633 مشترک است و جایگاه 2 017 را در دسته آموزش و رتبه 3 821 را در منطقه الهند دارد.
📊 شاخص‌های مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 77 633 مشترک جذب کرده است.
بر اساس آخرین داده‌ها در تاریخ 13 نوامبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -142 و در ۲۴ ساعت گذشته برابر -20 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 2.37% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.01% واکنش نسبت به کل مشترکان کسب می‌کند.
- دسترسی پست‌ها: هر پست به طور میانگین 1 843 بازدید دریافت می‌کند. در اولین روز معمولاً 781 بازدید جمع‌آوری می‌شود.
- واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 3 است.
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“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data”
به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 14 نوامبر, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کرده‌اند.
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آرشیو پست ها
How to set the learning rate?
There is no straightforward way of finding an optimum learning rate for a model. It involves a lot of hit and trial. Usually starting with a small values such as 0.01 is a good starting point for setting a learning rate and further tweaking it so that it doesn't overshoot or converge too slowly.
What happens when the learning rate is too large? Too small?
A large learning rate can accelerate the training. However, it is possible that we "shoot" too far and miss the minimum of the function that we want to optimize, which will not result in the best solution. On the other hand, training with a small learning rate takes more time but it is possible to find a more precise minimum. The downside can be that the solution is stuck in a local minimum, and the weights won't update even if it is not the best possible global solution.
What’s the learning rate?
The learning rate is an important hyperparameter that controls how quickly the model is adapted to the problem during the training. It can be seen as the "step width" during the parameter updates, i.e. how far the weights are moved into the direction of the minimum of our optimization problem.
How do we use SGD (stochastic gradient descent) for training a neural net?
SGD approximates the expectation with few randomly selected samples (instead of the full data). In comparison to batch gradient descent, we can efficiently approximate the expectation in large data sets using SGD. For neural networks this reduces the training time a lot even considering that it will converge later as the random sampling adds noise to the gradient descent.
Which optimization techniques for training neural nets do you know?
Gradient Descent
Stochastic Gradient Descent
Mini-Batch Gradient Descent(best among gradient descents)
Nesterov Accelerated Gradient
Momentum
Adagrad
AdaDelta
Adam(best one. less time, more efficient)
What is backpropagation? How does it work? Why do we need it?
The Backpropagation algorithm looks for the minimum value of the error function in weight space using a technique called the delta rule or gradient descent. The weights that minimize the error function is then considered to be a solution to the learning problem.
We need backpropogation because,
Calculate the error – How far is your model output from the actual output.
Minimum Error – Check whether the error is minimized or not.
Update the parameters – If the error is huge then, update the parameters (weights and biases). After that again check the error.
Repeat the process until the error becomes minimum.
Model is ready to make a prediction – Once the error becomes minimum, you can feed some inputs to your model and it will produce the output.
What if we set all the weights of a neural network to 0?
If all the weights of a neural network are set to zero, the output of each connection is same (W*x = 0). This means the gradients which are backpropagated to each connection in a layer is same. This means all the connections/weights learn the same thing, and the model never converges.
How we can initialize the weights of a neural network?
Proper initialization of weight matrix in neural network is very necessary. Simply we can say there are two ways for initializtions.
Initializing weights with zeroes. Setting weights to zero makes your network no better than a linear model. It is important to note that setting biases to 0 will not create any troubles as non zero weights take care of breaking the symmetry and even if bias is 0, the values in every neuron are still different.
Initializing weights randomly. Assigning random values to weights is better than just 0 assignment.
a) If weights are initialized with very high values the term np.dot(W,X)+b becomes significantly higher and if an activation function like sigmoid() is applied, the function maps its value near to 1 where the slope of gradient changes slowly and learning takes a lot of time.
b) If weights are initialized with low values it gets mapped to 0, where the case is the same as above. This problem is often referred to as the vanishing gradient.
What is ReLU? How is it better than sigmoid or tanh?
ReLU is an abbreviation for Rectified Linear Unit. It is an activation function which has the value 0 for all negative values and the value f(x) = x for all positive values. The ReLU has a simple activation function which makes it fast to compute and while the sigmoid and tanh activation functions saturate at higher values, the ReLU has a potentially infinite activation, which addresses the problem of vanishing gradients.
How do you select the number of trees in the gradient boosting model?
Most implementations of gradient boosting are configured by default with a relatively small number of trees, such as hundreds or thousands. Using scikit-learn we can perform a grid search of the n_estimators model parameter
How do you approach tuning parameters in XGBoost or LightGBM?
Depending upon the dataset, parameter tuning can be done manually or using hyperparameter optimization frameworks such as optuna and hyperopt. In manual parameter tuning, we need to be aware of max-depth, min_samples_leaf and min_samples_split so that our model does not overfit the data but try to predict generalized characteristics of data (basically keeping variance and bias low for our model).
What happens when we have correlated features in our data?
In random forest, since random forest samples some features to build each tree, the information contained in correlated features is twice as much likely to be picked than any other information contained in other features.
In general, when you are adding correlated features, it means that they linearly contains the same information and thus it will reduce the robustness of your model. Each time you train your model, your model might pick one feature or the other to "do the same job" i.e. explain some variance, reduce entropy, etc.
How do we know how many trees we need in random forest?
The number of trees in random forest is worked by n_estimators, and a random forest reduces overfitting by increasing the number of trees. There is no fixed thumb rule to decide the number of trees in a random forest, it is rather fine tuned with the data, typically starting off by taking the square of the number of features (n) present in the data followed by tuning until we get the optimal results.
How do we select the depth of the trees in random forest?
The greater the depth, the greater amount of information is extracted from the tree, however, there is a limit to this, and the algorithm even if defensive against overfitting may learn complex features of noise present in data and as a result, may overfit on noise. Hence, there is no hard thumb rule in deciding the depth, but literature suggests a few tips on tuning the depth of the tree to prevent overfitting:
• limit the maximum depth of a tree
• limit the number of test nodes
• limit the minimum number of objects at a node required to split
• do not split a node when, at least, one of the resulting subsample sizes is below a given threshold
• stop developing a node if it does not sufficiently improve the fit.
What are the main parameters of the random forest model?
max_depth: Longest Path between root node and the leaf
min_sample_split: The minimum number of observations needed to split a given node
max_leaf_nodes: Conditions the splitting of the tree and hence, limits the growth of the trees
min_samples_leaf: minimum number of samples in the leaf node
n_estimators: Number of trees
max_sample: Fraction of original dataset given to any individual tree in the given model
max_features: Limits the maximum number of features provided to trees in random forest model
How do we handle categorical variables in decision trees?
Some decision tree algorithms can handle categorical variables out of the box, others cannot. However, we can transform categorical variables, e.g. with a binary or a one-hot encoder.
When do we need to perform feature normalization for linear models? When it’s okay not to do it?
Feature normalization is necessary for L1 and L2 regularizations. The idea of both methods is to penalize all the features relatively equally. This can't be done effectively if every feature is scaled differently.
Linear regression without regularization techniques can be used without feature normalization. Also, regularization can help to make the analytical solution more stable, — it adds the regularization matrix to the feature matrix before inverting it.
What is the area under the PR curve? Is it a useful metric?
The Precision-Recall AUC is just like the ROC AUC, in that it summarizes the curve with a range of threshold values as a single score.
A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate.
