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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
Показати більше📈 Аналітичний огляд Telegram-каналу Data Science & Machine Learning
Канал Data Science & Machine Learning (@datasciencefun) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 75 837 підписників, посідаючи 2 107 місце в категорії Освіта та 4 219 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 75 837 підписників.
За останніми даними від 22 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 728, а за останні 24 години на -2, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 3.00%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.05% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 2 278 переглядів. Протягом першої доби публікація в середньому набирає 794 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 3.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як learning, accuracy, distribution, panda, dataset.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“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”
Завдяки високій частоті оновлень (останні дані отримано 23 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.
75 837
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Hands_On_Machine_Learning_with_Scikit_Learn,_Keras,_and_TensorFlow.pdf72.75 MB
Data Science - John D. Kelleher, Brendan Tierney (The MIT Press, 2018)
Python 3 for Machine Learning - Oswald Campesato (Mercury Learning, 2020)
Artificial Intelligence - An Illustrated History - From Medieval Robots to Neural Networks - Clifford A. Pickover (Sterling, 2019)
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.
