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I would like to have a good intuition on how close is what animal to which one and how large each group is by number of species and population. I was wondering if there is a good website with good demographics and graphs that could help me memorize it because it's really so hard to make up the tree myself from wikipedia pages!
There is a very good website and application for that. It's called Onezoom tree of life explorer which I believe is for London imperial collage.
Another alternative is the Tree of Life web project, which includes species richness information in the lower levels of the tree and allows reasonably easy navigation of subtrees. You can find the animal portal here.
EDIT -- another alternative, which is in active development, is this integration of Wikipedia and the Open Tree of Life Hyperbolic Tree.
This lesson explores the classification system used to identify animals. Most children are fascinated by animals and often have an animal that is a particular favorite, possibly even an animal the child has never seen before. Children also like to order and sort things, and this lesson melds both of these interests.
This lesson is specifically designed to move quickly beyond the knowledge level to high-level thinking. This lesson can be taught to an entire classroom or given as a self-directed extension activity.
Learning Objectives After completing the lessons in this unit, students will be able to:
- Know and understand the seven levels of classification.
- Apply that knowledge as they practice classifying animals.
- Evaluate and compare the classification of animals.
- Devise a classification system for the objects in their homes.
- Create a new species and classify it according to the principles of classification.
- Print the lesson plan on a color printer.
- Have access to the Internet for student(s).
- Gather supplies including paper, pen or pencil, crayons, colored pencils and/or fine-tip markers.
Concepts in biology are often hierarchical and require students to arrange categories based on size, increasing similarities, order within a pathway, or relationships to each other. Converting units of measurement, relating cell or organ types within and between organ systems, diagnosing diseases from symptoms, and scaling levels of biological organization from cells to the biosphere all use hierarchical systems of organization. Here, I introduce an active-learning strategy, similar to a concept map (e.g., Vanides et al., 2005), in which students work as a class to place cue cards on a string to develop a hierarchical system of organization. Although the method can be utilized in numerous other fields, I describe the activity as it pertains to animal taxonomy and evolutionary relationships and provide all the materials needed for this lesson.
“The physical nature of this activity, adding/removing and sliding cards along the string, allows students to literally visualize hierarchical relationships.”
Students sometimes struggle to visualize evolutionary relationships among organisms, given the vastness of geological time and the disparity of phenotypes in modern forms (Meir et al., 2007 Jenner, 2014). Students can, however, intuitively recognize evolutionary relatedness among well-known organisms based on their shared traits. For example, students acknowledge that medical studies performed on mice are more relevant to humans than studies using fruit flies, because humans are more closely related to mice than either is to fruit flies. The activity described here utilizes this intuitive grasp of animal relationships to illustrate how organisms are named and classified (taxonomy) as well as how organisms are related to each other and how traits are shared through common ancestry (evolution and systematics).
In this activity, students use cue cards and a string to group easily recognizable taxa according to shared traits. Each card illustrates a unique animal and contains a short description of the animal that does not provide information regarding higher taxonomy, such as “mammal.” By comparing cards containing recognizable animals, the class works cooperatively to arrange them on the string according to their shared traits. They then draw and interpret phylogenetic trees based on the defined relationships of the provided taxa. This activity encourages students to communicate their perceptions about evolutionary relationships, allowing instructors to address the unique alternative conceptions students bring to the classroom.
These are probably the simplest algorithms in machine learning. You have features x1,…xn of objects (matrix A) and labels (vector b). Your goal is to find the most optimal weights w1,…wn and bias for these features according to some loss function, for example, MSE or MAE for a regression problem. In the case of MSE there is a mathematical equation from the least squares method:
In practice, it’s easier to optimize it with gradient descent, that is much more computationally efficient. Despite the simplicity of this algorithm, it works pretty well when you have thousands of features, for example, bag of words or n-gramms in text analysis. More complex algorithms suffer from overfitting many features and not huge datasets, while linear regression provides decent quality.
To prevent overfitting we often use regularization techniques like lasso and ridge. The idea is to add the sum of modules of weights and the sum of squares of weights, respectively, to our loss function. Read the great tutorial on these algorithms at the end of the article.
Finally, time to start training.
Well, before I could get some water, my model finished training. So let’s evaluate its performance.
Picture showing the power of Transfer Learning.
We clearly see that we have achieved an accuracy of about 96% in just 20 epochs. Super fast and accurate.
If the dogs vs cats competition weren’t closed and we made predictions with this model, we would definitely be among the top if not the first.
And remember, we used just 4000 images from a total of about 25,000.
What happens when we use all 25000 images for training combined with the technique ( Transfer learning) we just learnt?
Well, a very wise scientist once said…
A not-too-fancy algorithm with enough data would certainly do better than a fancy algorithm with little data.
And it has been proven true!
Okay, we’ve been talking numbers for a while now, let’s see some visuals…
Without changing your plotting code, run the cell block to make some accuracy and loss plots.
And we get these plots below…
So what can we read of this plot?Well, we can clearly see that our validation accuracy starts doing well even from the beginning and then plateaus out after just a few epochs.
Now you know why I decreased my epoch size from 64 to 20.
Finally, let’s see some predictions. We are going to use the same prediction code. Just run the code block.
Predictions for ten Images from our test set
After running mine, I get the prediction for 10 images as shown below…
And our classifier got a 10 out of 10. Pretty nice and easy right?
Well, This is it. This is where I stop typing and leave you to go harness the power of Transfer learning.
Some amazing post and write-ups I referenced.
Questions, comments and contributions are always welcome.
How to learn and memorize animals classification to get an intuition about them? - Biology
The kingdom of animals is fascinating. The interaction, survival, and beauty of animals is worth understanding and studying. Not that we're biased or anything, but we think ducks are the best animals ever. Check out your favorite animal or type of animal below to learn more about them. We also have lots of fun facts about animals, so enjoy, and we hope you learn something about animals along the way.
Colorado River Toad
Gold Poison Dart Frog E
There may be nothing more beautiful than to observe animals in their natural habitat. Here is a picture of our favorite animal (the amazing duck!) in it's natural habitat hanging out on the water.
If you love animals, you might also like to check out our list of animal movies for kids.
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Lopez A, Miranda P, Tejada E, Fishbein DB: Outbreak of human rabies in the Peruvian jungle. Lancet. 1992, 339: 408-411. 10.1016/0140-6736(92)92006-2.
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Goodwin GG, Greenhall AM: Review of the bats of Trinidad and Tobago. Bull Am Mus Hist. 1961, 122: 187-302.
Kurten L, Schmidt U, Schafer K: Warm and cold receptors in the nose of the vampire bat Desmodus rotundus. Naturwissenschaften. 1984, 71: 327-328. 10.1007/BF00396621.
Campbell AL, Naik RR, Sowards L, Stone MO: Biological infrared imaging and sensing. Micron. 2002, 33: 211-225. 10.1016/S0968-4328(01)00010-5.
Mann G: Neurobiologia de Desmodus rotundus. Invest Zool Chile. 1960, 6: 79-99.
Schmidt U: Olfactory threshold and odour discrimination of the vampire bat (Desmodus rotundus). Period biol. 1973, 75: 89-92.
Schmidt U, Schlegel P, Schweizer H, Neuweiler G: Audition in vampire bats, Desmodus rotundus. J Comp Physiol [A]. 1991, 168: 45-51. 10.1007/BF00217102.
Miller J: The sampling distribution of d'. Percept Psychophys. 1996, 58: 65-72.
Su LS, Li KP, Fu KS: Identification of speakers by use of nasal coarticulation. J Acoust Soc Am. 1974, 56: 1876-1882. 10.1121/1.1903526.
Green DM, Mason CR, Kidd G: Profile analysis: critical bands and duration. J Acoust Soc Am. 1984, 75: 1163-1167. 10.1121/1.390765.
Hartmann WM, Pumplin J: Noise power fluctuations and the masking of sine signals. J Acoust Soc Am. 1988, 83: 2277-2289. 10.1121/1.396358.
Terhardt E: Akustische Kommunikation – Grundlagen mit Hörbeispielen. 1998, Berlin/Heidelberg: Springer
How did the six kingdom of classification come to be?
The German biologist Earnst Haeckel in 1866, in his book Generelle Morphologie der Organismen, had classified the living world into three kingdoms : Protista, Plants and Animals. The group Protista included all single celled organisms that are intermediate in many respects between plants and animals.
R H Whittaker, an American Taxonomist, classified all living things in a five kingdom classification in 1969. They were Monera, Protista, Fungi, Plants and Animals.
They were classified on the basis of:
- Complexity of cell structure
- Complexity of body organisation
- The mode of nutrition
- Life style (ecological role)
- Phylogenetic (evolutionary) relationships
The six kingdoms of classification which is the current standard of classification of all living things was defined around 1980. It was defined by Carl Richard Woese, an American microbiologist.
He based this classification on his studies of ribosomal RNA. His studies made it possible to divide the prokaryotes into two kingdoms, called Eubacteria and Archaebacteria.
Understand what each means and make sure you can recognize an example of each.
The amount of energy associated with an object’s motion may be quantified through a calculation of its kinetic energy (KE), or energy of motion. Any increase in an object’s velocity (in units of meters per second in the metric system) will result in a dramatic increase in the object’s KE. Specifically, any doubling of the velocity will cause the KE to increase by a factor of four times.
The amount of stored energy in an object may be quantified through a calculation of its potential energy (PE), or stored energy. Energy may be stored in several ways, as in a common battery cell or the gasoline in a fuel tank. The Earth’s gravity may also store energy when an object is held at a certain height. Specifically, any doubling of the height will also double the PE.
KE and PE have a unique connection. PE can be used to produce an object’s motion, which is KE. Both forms of energy have a dynamic interplay through the conservation of energy. Conservation of energy occurs when a total constant of energy is maintained by the conversion of energy between kinetic and potential. If a system is considered closed and isolated (no mass or energy is entering or leaving), then energy may only be converted from one form of energy to another. Generally, the energy of motion (KE) may be increased, but only at the expense of converting stored energy (PE). The reverse conversion may also occur (kinetic to potential), but the total energy for the system must remain fixed. In short, the Law of Conservation of Energy says that energy is not lost but rather transferred back and forth between KE and PE. Given a fixed amount of total energy in a system, an increase in KE will result in a decrease in PE (and vice versa), but the total amount of energy will remain the same.
The End: when the war with the machines?
The main problem here is that the question "when will the machines become smarter than us and enslave everyone?" is initially wrong. There are too many hidden conditions in it.
We say "become smarter than us" like we mean that there is a certain unified scale of intelligence. The top of which is a human, dogs are a bit lower, and stupid pigeons are hanging around at the very bottom.
If this were the case, every human must beat animals in everything but it's not true. The average squirrel can remember a thousand hidden places with nuts — I can't even remember where are my keys.
So intelligence is a set of different skills, not a single measurable value? Or is remembering nuts stashed locations not included in intelligence?
An even more interesting question for me - why do we believe that the human brain possibilities are limited? There are many popular graphs on the Internet, where the technological progress is drawn as an exponent and the human possibilities are constant. But is it?
Ok, multiply 1680 by 950 right now in your mind. I know you won't even try, lazy bastards. But give you a calculator — you'll do it in two seconds. Does this mean that the calculator just expanded the capabilities of your brain?
If yes, can I continue to expand them with other machines? Like, use notes in my phone to not to remember a shitload of data? Oh, seems like I'm doing it right now. I'm expanding the capabilities of my brain with the machines.