Hi! Though the 2019 conference season is not over yet, we'd like to talk about the bug-finding challenges we offered to visitors at our booth during the past conferences. Starting with the fall of 2019, we've been bringing a new set of challenges, so we can now reveal the solutions to the previous tasks of 2018 and the first half of 2019 – after all, many of them came from previously posted articles, and we had a link or QR code with information about the respective articles printed on our challenge leaflets.

Though it seems we are just a few months away from reaching 2020, these months are also important in the field of software development. Here in this article, we will see how the coming year 2020 will change the lives of software developers!

Future Software Development Is Here!

Traditional software development is about developing software by writing code and following some fixed rules. But the present-day software development witnessed a paradigm shift with advances in Artificial Intelligence, Machine Learning, and Deep Learning. With the integration of these three technologies, developers will be able to build software solutions that learn the instructions and add extra features and patterns in data that are needed for the desired outcome.

Let’s Try Out With Some Code

Over time, the neural network software development systems have become more complex in terms of integrations as well as layers of functionality and interfaces. Developers can build a very simple neural network with Python 3.6. Here’s an example of a program that does binary classification with 1 or 0.

Of course, we can start by creating a neural network class:


import numpy as np

X=np.array([[0,1,1,0],[0,1,1,1],[1,0,0,1]])
y=np.array([[0],[1],[1]])

Applying the Sigmoid function:

def sigmoid ():
   return 1/(1 + np.exp(-x))
def derivatives_sigmoid ():
   return x * (1-x)

Training the Model With Initial Weights and Biases:

epoch=10000
lr=0.1
inputlayer_neurons = X.shape[1]
hiddenlayer_neurons = 3
output_neurons = 1

wh=np.random.uniform(size=(inputlayer_neurons,hiddenlayer_neurons))
bh=np.random.uniform(size=(1,hiddenlayer_neurons))
wout=np.random.uniform(size=(hiddenlayer_neurons,output_neurons))
bout=np.random.uniform(size=(1,output_neurons))

For beginners, if you need help regarding neural networks, you can get in touch with top software development company.Or, you can hire AI/ML developers to work on your project.

The cryptocurrency space is incredibly popular among investors and traders alike. Many of the most popular crypto assets have grown a lot in recent years. Let’s take Bitcoin.

Statistics about startup funding show that 77% of small businesses rely on personal savings for their initial funds. One-third of such companies use less than $5,000, while $10,000 is generally needed to get a business started. Only 0.05% of startups raise venture capital.

These numbers show the importance of being able to save resources every time you can. And today, we will look into some practical ways of doing that and mention some handy tools.