In this video we build on last week Multilayer perceptrons to allow for more flexibility in the architecture! However, we need to be careful about the layer of abstraction we put in place in order to facilitate the work of the user who want to simply fit and predict. Here we make use of the following three concept: Network, Layer and Neuron. These three components will be composed together to make a fully connected feedforward neural network neural network. For those who don't know a fully connected feedforward neural network is defined as follows (From Wikipedia): "A feedforward neural network is an artificial neural network wherein connections between the nodes do not form a cycle. As such, it is different from its descendant: recurrent neural networks. The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network."
In the past year I have also worked with Deep Learning techniques, and I would like to share with you how to make and train a Convolutional Neural Network from scratch, using tensorflow. Later on we can use this knowledge as a building block to make interesting Deep Learning applications. The pictures here are from the full article. Source code is also provided. Before you continue, make sure you understand how a convolutional neural network works.
Neural Networks and Deep Learning are currently the two hot buzzwords that are being used nowadays with Artificial Intelligence. The recent developments in the World of Artificial intelligence can be attributed to these two as they have played a significant role in improving the intelligence of AI. Look around, and you will find more and more intelligent machines around. Thanks to Neural Networks and Deep Learning, jobs and capabilities that were once considered the forte of humans are now being performed by machines. Today, Machines are no longer made to eat more complex algorithms, but instead, they are fed to develop into an autonomous, self-teaching systems capable of revolutionizing many industries all around.
Enjoying a surge in research and industry, due mainly to its incredible successes in a number of different areas, deep learning is the process of applying deep neural network technologies - that is, neural network architectures with multiple hidden layers - to solve problems. Deep learning is a process, like data mining, which employs deep neural network architectures, which are particular types of machine learning algorithms. As defined above, deep learning is the process of applying deep neural network technologies to solve problems. Like data mining, deep learning refers to a process, which employs deep neural network architectures, which are particular types of machine learning algorithms.
In the field of Artificial Intelligence, we tend to move full speed ahead in building and training models without considering the philosophical aspects of what we're doing. It's great to focus on development, but we also have a responsibility to consider the bigger picture from time to time. One of the things that drew me to Artificial Intelligence in the first place is that there is an equal amount of philosophical and technical depth to the discipline. Furthermore, I have found that the philosophy and the theory enrich one another. One question AI Engineers don't often stop to think about is, ironically, the concept of Intelligence itself.