Machinelearning, abdsc, developer, node & much more…
Python News Monday, July 9
- Comparison of top data science libraries for Python, R and Scala [Infographic]
- Free eBooks on Data Visualization and Machine Learning
Developer, node, nodejs, coding, js, reactjs
- Awesome Python Newsletter – Issue 111, Jul 06, 2018
- Deep Learning for Computer Vision with Python: Master Deep Learning Using My New Book
- Cisco DevNet Learning Labs
- Write the basic front-end UI tests
- rubiest あがりの python 初心者がループ中の next に殺られた件 – Qiita
- プログラミング言語 Python
- So while many languages can be useful for a data scientist, these three remain the most popular and are developed to implement data science and machine learning solutions.
- In this post, we have prepared an infographic which shows top 20 libraries in each programming language which are beneficial to data scientists and data engineers work.
- Although there are many specific fields of application of different data science packages, we want to focus on those that are perfectly suited for machine learning, visualization, mathematics and engineering, data manipulation and analysis, and reproducible research.
- Therefore, the language has many great libraries for machine learning and engineering; however, it lacks data analysis and visualization possibilities comparing to previous languages.
- These are the languages and libraries that have proved to be extremely useful in various data science use cases.
@analyticbridge: Comparison of top data science libraries for Python, R and Scala [Infographic] https://t.co/OKutQ27Wvs
- What You Need to Know about Machine Learning – – This eBook offers you the perfect place to lay the foundation for your work in the world of Machine Learning, providing the basic understanding, knowledge, and skills that you can build on with experience and time.
- What you will learn: – – Click here to get the free eBook – – Get a complete grounding in the exciting visual world of Canvas and HTML5 using this recipe-packed cookbook.
- What you will learn: – – Click here to get the free eBook – – Expand your knowledge of Python data with the power of machine learning with this eBook.
- What you will learn: – – Click here to get the free eBook – – This eBook is designed to give you the knowledge you need to start succeeding in data analysis.
- What you will learn: – – Click here to get the free eBook – – Tech consultants and BI experts will charge a lot to help you turn data into actionable insights.
@KirkDBorne: *Free* eBooks on Data Visualization and #MachineLearning for DataScientists : https://t.co/dguZdOeHMM #abdsc… https://t.co/YXxCXHmbe9
- The following example shows the addition of two tensor (make sure that the dimensions or shapes of both the tensors are same; they must be incompliance with the Matrix mathematics rules): – – let twoDimensionalTensor = tf.tensor([1,2,3,4], shape=[2,2], dtype=float32); – let addition = should expect the element-wise addition.
- The output is as follows: – Tensor – [[2, 4], – [6, 8]] (as expected) – – Multiplication using Tensors – – We can multiple two tensors (element-wise) by using the following code: – – let twoDimensionalTensor = tf.tensor([1,2,3,4], shape=[2,2], dtype=float32); – let multiplcation = output is: – Tensor -…
- This is how you can multiple two tensors (real matrix multiplication): – – let twoDimensionalTensor = tf.tensor([1,2,3,4], shape=[2,2], dtype=float32); – let multiplcation = the above case, as the number of columns of first matrix is same as the number of rows of the second matrix (in this case the same…
- The output is: – Tensor – [[7 , 10], – [15, 22]] – – Summary – – In this article, we have learned the basics of Machine or Deep Learning in general and TensorFlow.js in particular.
@PythonLibHunt: Awesome #Python Weekly #111 is out https://t.co/cXfW3BmdhsFeaturing @activewizards @dicedotcom @realpython @realpython
- First, it’s important to understand that Deep Learning for Computer Vision with Python is the most complete, comprehensive deep learning education online (the ImageNet Bundle is over 900+ pages).
- Each library in the book is thoroughly reviewed to ensure you understand how to build & train your own deep learning networks.
- That said, a little bit of OpenCV experience goes a long way, so if you’re new to OpenCV I highly recommend (1) purchase a copy of Deep Learning for Computer Vision with Python and (2) work through my other book, Practical Python and OpenCV to learn the fundamentals.
- The ImageNet Bundle covers very advanced deep learning techniques on massive datasets, so make sure you make the necessary hardware preparations.
- I personally use the NVIDIA Titan X (12GB) on a daily basis for training my own deep learning networks.
@PyImageSearch: Surprise! All video guides/tutorials for Deep Learning for Computer Vision with Python (https://t.co/rQgpAflp52) we… https://t.co/t15AWmFVVJ
@CiscoDevNet: Introduction to Coding Fundamentals https://t.co/tSRAaEv9xtGet started with coding basics by learning the fundame… https://t.co/GNx6MJdaXf
@qiita_python: rubiest あがりの python 初心者がループ中の next に殺られた件 – https://t.co/mmoSLFeLQ2
@atsuoishimoto: https://t.co/zAmOm5gxO8 でSlackをDiscordに切り替える話 https://t.co/qRLyWn9Ve5