AI is a broad area of computer science that makes machines seems like they have human intelligence and can perform tasks like learning, seeing, talking, problem-solving, and more. It put objects, categories, properties, and relations together to initiate reasoning, common sense, and problem-solving features in machines. Artificial Intelligence is a perfect amalgamation of machine learning, knowledge engineering, and robotics.
Gradient checking is done to detect hidden bugs in complicated software that manifest themselves under very specific conditions. It is a debugging process done on the back prop algorithm, it also performs a derivative checking procedure, and ensure that the implementation is correct.
|Inductive Machine Learning||Deductive Machine Learning|
|Observe and learn from the set of instances and then draw the conclusion||Derives conclusion and then work on it based on the previous decision|
|It is Statistical machine learning like KNN (K-nearest neighbour) or SVM (Support Vector Machine)||Machine learning algorithm to deductive reasoning using a decision tree|
|A ⋀ B ⊢ A → B (Induction)||A ⋀ (A → B) ⊢ B (Deduction)|
Breath-First search involves traversing a binary search tree one level at a time. Starting with the root node, proceeding through neighboring nodes moving towards the next level of nodes. As this process can be performed utilizing FIFO (First in First Out) data structure. This strategy gives the shortest path to the solution. BFS assigns two values to each node: distance and predecessor.
If there is no path from the source node to node “v”, then v's distance is infinite, and it is assumed that predecessor has the same special value as the source's predecessor.
Basically, machine learning is pattern recognition. Like Youtube’s video recommendations, Facebook’s News Feeds, etc. are a perfect example of pattern recognition. Machines observe patterns and learn from the examples. The type of videos you see on YouTube, you get video recommendations of similar type. The outcome of the machine learning program keeps improving with every attempt and trials.
The Depth-First Search (DFS) algorithm is a recursive algorithm that uses the method of backtracking. It involves extensive traversing through all the nodes by going ahead, if possible, else by backtracking.
When you are moving forward and there are no more nodes left along the current path, you go back on the same path to find new nodes to traverse. The next path will only be selected if all the nodes on the current path will be visited. DFS is performed using the stack and it goes as follows:
Pick a source/starting node and push all the adjacent nodes into a stack. Now pick a node from the stack to select the next node to visit and push all its adjacent nodes into a stack.
Repeat the process until the stack is empty. To ensure that all the visited nodes are marked, as this will prevent you from re-visiting the same node. If you do not mark the visited nodes and you may visit the same node more than once and end up in an infinite loop.
Classification is used when the output variable is a category such as “red” or “blue”, “spam” or “not spam”. It is used to draw a conclusion from observed values. Differently from, regression which is used when the output variable is a real or continuous value like “age”, “salary”, etc. When we must identify the class, the data belongs to we use classification over regression. Like when you must identify whether a name is male or female instead of finding out how they are correlated with the person.
A game is the most visible area of progress in the AI system. AI systems could be improved using game theory, which requires more than one participant to narrows the field quite a bit. It serves two fundamental roles:
|Parametric Model||Non-parametric Model|
|Use a finite number of parameters to predict new data||Use the unbounded number of parameters|
|Simple, Quick, and Less data||Powerful, Flexible, and unlimited data|
|Data prediction depends on just parameters||Data prediction depends upon parameters and a current state of data|
TensorFlow is an open source machine learning framework. It is fast, flexible and a low-level toolkit meant for doing a complex algorithm and offers customizability to build experimental learning architectures. AlphaGo and Google Cloud Vision are built on Tensorflow framework.
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