As a recent grad, you’ve probably had at least a couple of experience working in a “real world” office. But the question is, what changes when you’re a full-time employee and not just a summer or semester-long intern?
Lucky for you, we scoured the web for the advice you need to know as you take your first steps into the big, bad workforce.
- College won’t teach you about these seven things you need to know about entering the workforce. (Mashable)
- Understanding that grammar counts is just one of the many pieces of unconventional career advice you should learn before you start your first job. (Forbes)
- Sheryl Sandberg has some great words of wisdom for recent grads just starting to look at the job market. First things first? Banish self-doubt. (Entrepreneur)
- Are you really all that prepared for the workforce? Studies show you may not be. (Slate)
- Soft skills? Yeah, those are really, really important when you’re starting off your career. (Fox Business)
- Forget what you need to do when starting out; here’s what not to do. (The New York Times)
- A lot of times new grads forget that there is in fact a transition period between college and the real world. (Quintessential Careers)
- Once you get settled in, there are nine things you should do during the first week of your job. (Business Insider)
For robotics applications, many consider Robot Operating System (ROS) as the default go-to solution. The version of ROS that runs on the NVIDIA Jetson Nano Developer Kit is ROS Melodic. Installing ROS on the Jetson Nano is simple. Looky here:
At a higher level, ROS provides facilities and tools for a Robot Description Language, diagnostics, pose estimation, localization, navigation, and visualization.
You can read more about the Core Components here.
Usage: ./installROS.sh [[-p package] | [-h]] -p | --package <packagename> ROS package to install Multiple Usage allowed The first package should be a base package. One of the following: ros-melodic-ros-base ros-melodic-desktop ros-melodic-desktop-full
$ ./setupCatkinWorkspace.sh [optionalWorkspaceName]
$ python train.py
$ python train.py -h usage: train.py [-h] [--embedding_size EMBEDDING_SIZE] [--num_layers NUM_LAYERS] [--num_hidden NUM_HIDDEN] [--keep_prob KEEP_PROB] [--learning_rate LEARNING_RATE] [--batch_size BATCH_SIZE] [--num_epochs NUM_EPOCHS] [--max_document_len MAX_DOCUMENT_LEN] optional arguments: -h, --help show this help message and exit --embedding_size EMBEDDING_SIZE embedding size. --num_layers NUM_LAYERS RNN network depth. --num_hidden NUM_HIDDEN RNN network size. --keep_prob KEEP_PROB dropout keep prob. --learning_rate LEARNING_RATE learning rate. --batch_size BATCH_SIZE batch size. --num_epochs NUM_EPOCHS number of epochs. --max_document_len MAX_DOCUMENT_LEN max document length.
Language Model Training LossThanks & CheersStack Exchanges