Best Linkedin posts about TensorFlow

If you feel overwhelmed as a junior dev, I really don't blame you. Coding is a broad field, with so many skills you can learn. The best you can do is narrow your focus as much as possible. Pick one area to double down on. For example: → Front-end (with HTML, CSS and JS + a framework) → Back-end (with PHP + Laravel or Python + Django) → Machine learning (with Python + Tensorflow) Start small. You can always expand and try other areas later. 🔥

The data science tech stack over the course of your career. Beginner: - Excel - SQL - Qlik/Tableau/Power BI Expert: - Excel - Python - SQL - Pandas - NumPy - Matplotlib - Scikit-Learn - Tensorflow/PyTorch - Git - Docker Executive: - Excel Excel always comes out at the number 1 spot 😂. Thoughts? See you in the comments ✌🏼. #datascience #career

Is python mandatory to become a data scientist? No, it is not.  You can start with any other programming language like Java, C, R.  So why do I prefer python?  - It was super easy to learn. I was a C++ fan. Took some time to learn and was amazed by the functionality.  - Tons of libraries. You just name the task and that is available in the well-defined tested library. My favourites are pandas, scikit-learn, TensorFlow and PyTorch.  - Can be used in research and production.  - Shorter development cycle.    Check the link for my video on this topic. Any questions? Post in the video comments. #python #programming #java #datascientist #development #pandas #tensorflow #research

Recruiting for an AI/ML-focused Principal in SF or NYC for one of the world's best VCs. Seed / Series A focus. No VC experience required. CS degree + 1 to 5 years Product Management (ideally) or Corp Dev / BizDev experience at AI-heavy platforms, such as: Deep Mind OpenAI Anthropic Cohere Adept AI Google Brain FAIR Connect with Will for details. Warm intros preferred. #machinelearning #venturecapital #tensorflow #tpu

#MLOps video lesson out! 👇 Learn how to package #HuggingFace GPT-2 model with #Tensorflow using a self-documented API with #FastAPI, then containerize everything and push it to #Docker Hub. All with one of the main foundations of MLOps: #automation !

Python SQL Spark Scala Tableau Power BI Tensorflow Pytorch AWS ... none of this would be useful if you don't understand the business problem. Understand the business problem first. Then use the right tools. Who's with me? 🙋🏻‍ #datascience #careers

#dataengineering and #datascience has its own set of Pepsi or Coke, Android or iPhone questions. - Bayesian or Frequentist - Explainable model or Ensemble model - Databricks or Snowflake - Postgres or MongoDB - AWS or Azure - Python or R - Tensorflow or PyTorch - Airflow or Prefect - Hive or Iceberg - Spark or Flink - Lambda architecture or Kappa architecture Sure you can pick other options outside these two but you’d probably be wrong.

Following the comparison of Python vs R vs C++, today I’ will analyze several deep learning frameworks. Many people compared speed or architecture, but I think nobody has talked about why some frameworks had more adoption than others. The frameworks I want to analyze are Tensorflow, Keras, PyTorch, MXNet, and CNTK. I have used all of them and contributed to MXNet and CNTK. The key questions I answer are: 1. What framework should I use? 2. What was the journey of each of the deep learning frameworks? 3. What are the reasons for their adoption? 4. What were the weaknesses that could explain the lack of adoption? Grab the info in the first comment ____ #ai #machinelearning #deeplearning #datascience