Practical guide to hyperparameters search for deep learning models
- The end-to-end workflow is really quite simple: a student devises a new experiment that she follows through all the steps of the learning process (from data collection to feature map visualization), then will she iterates sequentially on the hyperparameters until she runs out time (usually due to a deadline) or motivation.
- Click this button to open a Workspace on FloydHub. You can use the workspace to run the code below (Grid Search using Scikit-learn and Keras) on a fully configured cloud machine.
- Click this button to open a Workspace on FloydHub. You can use the workspace to run the code below (Random Search using Scikit-learn and Keras.) on a fully configured cloud machine.
- This search strategy builds a surrogate model that tries to predict the metrics we care about from the hyperparameters configuration.
Teaching my robot with TensorFlow
- In this post, I'm going to show you how you can teach your own Cozmo to recognize everyday objects using transfer learning with TensorFlow on FloydHub. Install the Cozmo Python SDK, create a new virtualenv, and clone the cozmo-tensorflow project to your local machine.
- Side note - if that last sentence sounded like a handful, then just know that FloydHub takes care of configuring and optimizing everything on your cloud machine so that it's ready for your GPU-powered deep learning experiments.
- You can specify the exact deep learning framework you'd like to use - whether that's TensorFlow 1.4 or PyTorch 0.3 or more - and FloydHub will make sure your machine has everything you need to start training immediately.
- For our current project, I've created a simple Flask app that will receive an image from Cozmo in a POST request, evaluate it using the model we trained in our last step, and then respond with the model's results.
How to make a racist AI without really trying
- This model is not the point of that paper, so don’t take this as an attack on their results; it was there as an example of a well-known way to use word vectors.
- Read on, and I hope you’ll come out of this tutorial with the desire to use modern, high-quality word embeddings, especially those that are aware of algorithmic bias and try to mitigate it.
- It requires finding a source of examples of neutral words, because Liu’s data only lists positive and negative words.
- We use a logistic function as the loss, so that the resulting classifier can output the probability that a word is positive or negative.
- Let’s define a function that we can use to see the sentiment that this classifier predicts for particular words, then use it to see some examples of its predictions on the test data.
Siemen's Self-Driving Street Car Puts Autonomous Tech on Track
- Which makes it easier to understand why engineers in Potsdam, Germany, have taken the KISS idea to an extreme: They've put their autonomous vehicles on tracks.
- They use lidars, radars, cameras, and machine-learning software to interact with cars, pedestrians, and other denizens of the urban world on a 4-mile stretch of the existing network—chosen to be not too crazy busy, but not too easy either.
- The trial is giving Siemens a chance to experiment in a way that isn’t easy with free-range cars and learn lessons that should aid the development of driver assistance and autonomous features for both road and rail vehicles.
- The work in Potsdam is an extension of earlier trials in Ulm, Germany, of the “Siemens Tram Assistant.” This is similar to the crash avoidance tools that higher-end cars now come with.
Cisco UCS 480 ML M5 Server – Performance and Capacity for AI
- The C480 ML M5 rack server, developed in partnership with NVIDIA, a leader in AI computing, supports eight NVIDIA Tesla V100 Tensor Core GPUs with NVIDIA NVLink interconnect.
- It provides a holistic and unified approach to managing distributed computing environments regardless of the server form factor, workload, or location.
- Only Cisco can deliver AI/ML/DL solutions delivered as part of an integrated system that supports processing data whether it is at the edge or in the datacenter regardless of which server in the portfolio is the best match to solve your problem.
- Regardless of industry, you won’t do better than the C480 ML M5 with the NVIDIA Tesla V100 Tensor Core GPUs. The system provides maximum performance that’s easy to consume as part of the UCS platform with the industry’s only uniform, cloud-powered, automated operations model.
Here's what I've learned by building my first app.
- For this app, I sketched out a plan in my notebook, skipped any further design, and went forward to the code.
- While it initially seemed obvious to me what the data would be, I started building off sketches to later realize that it wasn't as straight-forward as I thought.
- The thing I've enjoyed most about building my own little app is that the road bumps don't feel like trickery.
- In building this little app, I've learned that I really enjoy programming.
- I love visual design, so staying inspired really comes from this drive to build beautiful things.
- The cool thing about building your first app is that it becomes evident what areas of programming you need to work on and continue learning.
- So after this app, I'll build another, and another, and another, (and so on), and the challenges today will be simple the next time.
Don't learn Dvorak
- Dvorak is a keyboard layout said to reduce the movement of your fingers compared to the default QWERTY layout.
- I’ve switched my keyboard layout from QWERTY to Dvorak around five years ago.
- Muscle memory built up from touch typing on a QWERTY Layout for years meant that during the learning phase, I would constantly press the wrong key.
- I did not foresee that my QWERTY muscle memory would be replaced by Dvorak, making it frustrating to use other people’s computers — I had to search for keys and type with two fingers at an annoyingly slow pace.
- A far more valuable skill was the ability to touch type, but this isn’t tied to the Dvorak layout.
- For me, the time investment of learning a new layout isn’t worth the benefits, especially given that you have to relearn QWERTY if you ever want to switch back.
Practical Deep Learning for Coders
- Welcome to the 2018 edition of fast.ai's 7 week course, Practical Deep Learning For Coders, Part 1, taught by Jeremy Howard (Kaggle's #1 competitor 2 years running, and founder of Enlitic).
- You will start with step one—learning how to get a GPU server online suitable for deep learning—and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems.
- I'm a CEO, not a coder, so the idea that I'd be able to create a GPU deep learning server in the cloud meant learning a lot of new things—but with all the help on the wiki and from the instructors and community on the forum I did it!
- But Jeremy and Rachel (Course Professors) believe in the theory of 'Simple is Powerful', by virtue of which anyone who takes this course will be able to confidently understand the simple techniques behind the 'magic' Deep Learning.
Lessons Learned by Mentoring
- I want to share those things to help others interested in mentoring programmers to have as great a mentor-mentee relationship as I’ve had.
- I learned to deliver feedback with love and to keep in mind that the end goal of motivating a student is to help them improve themselves.
- It’s also the thing that will help the student the least and demoralize them the most because they see it as nitpicky, useless feedback that only makes the mentor feel better—and they’re right!
- I know it’s the introvert in me, but my gut instinct is to feel like they don’t really care about my feedback, and they just want me to sign off so they can move on to the next exercise.
- As you’re mentoring, if you keep in mind that your overarching, primary goal is to help the other person understand, learn, be motivated, and grow, you can’t go wrong.
The 'dunce robots' of Japan will help children learn
- By teaching a less intelligent robot, children reinforce their own learning and so become stronger students themselves, Tanaka believes, and research supports this.
- On top of that, a well-designed robot will not make a child feel "pressured," Tanaka said, whereas parents and teachers sometimes do.
- "We can design a robot as a kind of weak existence." Reassured by the robot's unimposing presence, a child will want to play and be able to learn easily instead of feeling the kind of pressure that locks a student's mind.
- "The interaction with the robot and comments that Minnie makes about the books demonstrate some understanding of what is happening in a story but is not presented in an authoritative way," Michaelis said.
- I thought that this is more socially acceptable." Appearance is important, he said, though it's unclear how designers can ensure that robots do not frighten young children.