In this tutorial, learn how to use convolutional autoencoders to create a Content-based Image Retrieval system (i.e., image search engine) using Keras and TensorFlow.
Here;s something a little lighter given the news of late.
We’ve all seen programming as depicted in movies and television and laughed at the sheer absurdity of it.
Joma Tech ponders what programming would look like in Anime.
A junior engineer tries solve a coding problem, but he has trouble finding the solution. He needs help, will he be able to solve it?
Every language has libraries.
To be a good programmer, it is not necessary to know all the libraries there are in any language. However, there are some libraries that every developer should know well.
Heres a good run down of data science and AI libraries every Python developer should know.
The integrity of Python has led to many developers building fresh libraries for it. Because of the vast number of libraries, Python is becoming very popular among machine learning professionals too.
So if programmers get to know the following 23 libraries really well, they can accomplish a lot in their day-to-day jobs.
Sometimes as data scientists we forget what are we paid for. We are primarily developers, then researchers and then maybe mathematicians.
Here’s an interesting post about the place of software engineering disciplines in data science and ML Engineering.
If it came to hiring between a great data scientist and a great ML engineer, I will hire the later.
Learn how to use TensorFlow 2.0 in this full tutorial course for beginners.
This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence.
Throughout the 8 modules in this course you will learn about fundamental concepts and methods in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning.
- Module 2: Introduction to TensorFlow – https://colab.research.google.com/drive/1F_EWVKa8rbMXi3_fG0w7AtcscFq7Hi7B#forceEdit=true&sandboxMode=true
- Module 3: Core Learning Algorithms – https://colab.research.google.com/drive/15Cyy2H7nT40sGR7TBN5wBvgTd57mVKay#forceEdit=true&sandboxMode=true
- Module 4: Neural Networks with TensorFlow – https://colab.research.google.com/drive/1m2cg3D1x3j5vrFc-Cu0gMvc48gWyCOuG#forceEdit=true&sandboxMode=true
- Module 5: Deep Computer Vision – https://colab.research.google.com/drive/1ZZXnCjFEOkp_KdNcNabd14yok0BAIuwS#forceEdit=true&sandboxMode=true
- Module 6: Natural Language Processing with RNNs – https://colab.research.google.com/drive/1ysEKrw_LE2jMndo1snrZUh5w87LQsCxk#forceEdit=true&sandboxMode=true
- Module 7: Reinforcement Learning – https://colab.research.google.com/drive/1IlrlS3bB8t1Gd5Pogol4MIwUxlAjhWOQ#forceEdit=true&sandboxMode=true
- ⌨️ Module 1: Machine Learning Fundamentals (00:03:25)
- ⌨️ Module 2: Introduction to TensorFlow (00:30:08)
- ⌨️ Module 3: Core Learning Algorithms (01:00:00)
- ⌨️ Module 4: Neural Networks with TensorFlow (02:45:39)
- ⌨️ Module 5: Deep Computer Vision – Convolutional Neural Networks (03:43:10)
- ⌨️ Module 6: Natural Language Processing with RNNs (04:40:44)
- ⌨️ Module 7: Reinforcement Learning with Q-Learning (06:08:00)
- ⌨️ Module 8: Conclusion and Next Steps (06:48:24)
For Mathematics, trees are more useful than strings.
Professor Thorsten Altenkirch takes us through a functional approach to coding them in Python.
On Friday, someone asked me about linear regression with neural networks.
I didn’t have a good answer – I knew that you *could* do linear regression but neural networks, but never had actually done it in practice.
Promising to learn more, I came across this video by giant_neural_network on YouTube.
Are you interested in learning Python?
We live in a time unique in human history, there are numerous of course options at all skill levels and price points.
You can acquire the tools and knowledge you need at a price that’s affordable for you.
There are tons of course options at all skill levels and price points. So, you’ll acquire the tools and knowledge you need at a price that’s affordable for you.
Python has been on a relentless ascent to distinction over the last few years and currently is one of the most well-known programming dialects on the planet.
Here’s an interesting article on why Python is so popular and what can be done with it.
Computer-based intelligence or Artificial Intelligence has made a universe of chances for application engineers. Computer-based information permits Spotify to prescribe artisans and melodies to clients, or Netflix to comprehend what shows you’ll need to see straight away. It is additionally utilized widely by organizations in client assistance to drive self-administration and improve work processes and worker efficiency.