1597917840

In this article, I am going to discuss the Gradient Descent algorithm. The next article will be in continuation of this article where I will discuss optimizers in neural networks. For understanding those optimizers it’s important to get a deep understanding of Gradient Descent.

- Gradient Descent
- Choice of Learning Rate
- Batch Gradient Descent
- Stochastic Gradient Descent
- Mini Batch Gradient Descent
- Conclusion
- Credits

Gradient Descent is a first-order iterative optimization algorithm for finding the local minimum of a differentiable function. To get the value of the parameter that will minimize our objective function we iteratively move in the opposite direction of the gradient of that function or in simple terms in each iteration we move a step in direction of steepest descent. The size of each step is determined by a parameter which is called Learning Rate. Gradient Descent is the first-order algorithm because it uses the first-order derivative of the loss function to find minima. Gradient Descent works in space of any number of dimensions.

Image Source

Steps in Gradient Descent-

- Initialize the parameters(weights and bias) randomly.
- Choose the learning rate (‘η’).
- Until convergence repeat this-

#machine-learning #neural-networks #data-science #deep-learning #artificial-intelligence #deep learning

1603753200

So far in our journey through the Machine Learning universe, we covered several big topics. We investigated some **regression** algorithms, **classification** algorithms and algorithms that can be used for both types of problems (**SVM****, ****Decision Trees** and Random Forest). Apart from that, we dipped our toes in unsupervised learning, saw how we can use this type of learning for **clustering** and learned about several clustering techniques.

We also talked about how to quantify machine learning model **performance** and how to improve it with **regularization**. In all these articles, we used Python for “from the scratch” implementations and libraries like **TensorFlow**, **Pytorch** and SciKit Learn. The word optimization popped out more than once in these articles, so in this and next article, we focus on optimization techniques which are an important part of the machine learning process.

In general, every machine learning algorithm is composed of three integral parts:

- A
**loss**function. - Optimization criteria based on the loss function, like a
**cost**function. **Optimization**technique – this process leverages training data to find a solution for optimization criteria (cost function).

As you were able to see in previous articles, some algorithms were created intuitively and didn’t have optimization criteria in mind. In fact, mathematical **explanations** of why and how these algorithms work were done later. Some of these algorithms are **Decision Trees** and **kNN**. Other algorithms, which were developed later had this thing in mind beforehand. **SVM**is one example.

During the training, we change the parameters of our machine learning model to try and **minimize** the loss function. However, the question of how do you change those parameters arises. Also, by how much should we change them during training and when. To answer all these questions we use **optimizers**. They put all different parts of the machine learning algorithm together. So far we mentioned **Gradient Decent** as an optimization technique, but we haven’t explored it in more detail. In this article, we focus on that and we cover the **grandfather** of all optimization techniques and its variation. Note that these techniques are **not** machine learning algorithms. They are solvers of **minimization** problems in which the function to minimize has a gradient in most points of its domain.

Data that we use in this article is the famous *Boston Housing Dataset* . This dataset is composed 14 features and contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It is a small **dataset** with only 506 samples.

For the purpose of this article, make sure that you have installed the following _Python _libraries:

- **NumPy **– Follow
**this guide**if you need help with installation. - **SciKit Learn **– Follow
**this guide**if you need help with installation. **Pandas**– Follow**this guide**if you need help with installation.

Once installed make sure that you have imported all the necessary modules that are used in this tutorial.

```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import SGDRegressor
```

Apart from that, it would be good to be at least familiar with the basics of **linear algebra**, **calculus** and **probability**.

Note that we also use simple **Linear Regression** in all examples. Due to the fact that we explore **optimization**techniques, we picked the easiest machine learning algorithm. You can see more details about Linear regression **here**. As a quick reminder the formula for linear regression goes like this:

where *w* and *b* are parameters of the machine learning algorithm. The entire point of the training process is to set the correct values to the *w* and *b*, so we get the desired output from the machine learning model. This means that we are trying to make the value of our **error vector** as small as possible, i.e. to find a **global minimum of the cost function**.

One way of solving this problem is to use calculus. We could compute derivatives and then use them to find places where is an extrema of the cost function. However, the cost function is not a function of one or a few variables; it is a function of all parameters of a machine learning algorithm, so these calculations will quickly grow into a monster. That is why we use these optimizers.

#ai #machine learning #python #artificaial inteligance #artificial intelligence #batch gradient descent #data science #datascience #deep learning #from scratch #gradient descent #machine learning #machine learning optimizers #ml optimization #optimizers #scikit learn #software #software craft #software craftsmanship #software development #stochastic gradient descent

1621251999

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1625043360

So far in our journey through the Machine Learning universe, we covered several big topics. We investigated some **regression** algorithms, **classification** algorithms and algorithms that can be used for both types of problems (**SVM, Decision Trees** and **Random Forest**). Apart from that, we dipped our toes in unsupervised learning, saw how we can use this type of learning for **clustering** and learned about several clustering techniques.

We also talked about how to quantify machine learning model **performance** and how to improve it with **regularization**. In all these articles, we used Python for “from the scratch” implementations and libraries like **TensorFlow**, **Pytorch** and **SciKit Learn**. The word optimization popped out more than once in these articles, so in this article, we focus on optimization techniques which are an important part of the machine learning process.

#ai #machine learning #python #artificaial inteligance #artificial intelligence #batch gradient descent #data science #datascience #deep learning #from scratch #gradient descent #machine learning optimizers #ml optimization #optimizers #scikit learn #software #software craft #software craftsmanship #software development

1624226400

Bitcoin Cash was created as a result of a hard fork in the Bitcoin network. The Bitcoin Cash network supports a larger block size than Bitcoin (currently 32mb as opposed to Bitcoin’s 1mb).

Later on, Bitcoin Cash forked into Bitcoin SV due to differences in how to carry on its developments.

That’s Bitcoin Cash in a nutshell. If you want a more detailed review watch the complete video. Here’s what I’ll cover:

0:50 - Bitcoin forks

2:06 - Bitcoin’s block size debate

3:35 - Big blocks camp

4:26 - Small blocks camp

5:16 - Small blocks vs. big blocks arguments

7:05 - How decisions are made in the Bitcoin network

10:14 - Block size debate resolution

11:06 - Bitcoin cash intro

11:28 - BTC vs. BCH

12:13 - Bitcoin Cash (ABC) vs. Bitcoin SV

13:09 - Conclusion

📺 The video in this post was made by 99Bitcoins

The origin of the article: https://www.youtube.com/watch?v=ONhbb4YVRLM

🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.

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#bitcoin #blockchain #bitcoin cash #what is bitcoin cash? - a beginner’s guide #what is bitcoin cash #a beginner’s guide

1597917840

In this article, I am going to discuss the Gradient Descent algorithm. The next article will be in continuation of this article where I will discuss optimizers in neural networks. For understanding those optimizers it’s important to get a deep understanding of Gradient Descent.

- Gradient Descent
- Choice of Learning Rate
- Batch Gradient Descent
- Stochastic Gradient Descent
- Mini Batch Gradient Descent
- Conclusion
- Credits

Gradient Descent is a first-order iterative optimization algorithm for finding the local minimum of a differentiable function. To get the value of the parameter that will minimize our objective function we iteratively move in the opposite direction of the gradient of that function or in simple terms in each iteration we move a step in direction of steepest descent. The size of each step is determined by a parameter which is called Learning Rate. Gradient Descent is the first-order algorithm because it uses the first-order derivative of the loss function to find minima. Gradient Descent works in space of any number of dimensions.

Image Source

Steps in Gradient Descent-

- Initialize the parameters(weights and bias) randomly.
- Choose the learning rate (‘η’).
- Until convergence repeat this-

#machine-learning #neural-networks #data-science #deep-learning #artificial-intelligence #deep learning