Discover the super-fast Intelligence - ‘Deep Learning’ to be destined…

Discover the super-fast Intelligence

- ‘Deep Learning’ to be destined…

Introduction:

Deep Learning is a sub-classification of ML which deals with algorithms formatted by the structure and function of the brain called Artificial Neural Networks (ANN’s). In this, we will discover exactly what DL is and its proposition.

Let’s dive in…

The deep in DL stands for the idea of successive layers of representations. How many layers contribute to a model of the data is called the depth of the model. It’s a facet of ML that involves the use of neural networks to improve things like speech recognition, vision, and language processing in computers or robots. 

DL relates to the field of ML-based algorithms, focusing on how neurons in the brain work and applying a similar analogy to help machines learn by the use of networks of networks. The simplest unit of Neural Network, a Perceptron takes in inputs, runs through the process, and finally provides a response.

The network consumes large amounts of input data and operates them through multiple layers. Any Deep neural network will consist of three types of layers:

The Input Layer: It receives all the inputs

The Hidden Layer: All the layers in between input and output layers are called hidden layers

The Output Layer: It provides the desired output

How it is used?

In DL, a computer model learns to perform classification tasks directly from images, text, or sound. DL models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.

To re-wire the human brain, DL studies the basic unit of a brain called a brain cell or a neuron.

If we focus on the structure of a neuron, it has dendrites which are used to receive inputs. These inputs are summed in the cell body and using the Axon it is passed on to the next biological neuron.

Similarly, a perceptron receives multiple inputs, applies various transformations and functions and provides an output. As we know that our brain consists of multiple connected neurons called a neural network, we can also have a network of artificial neurons called perceptron’s to form a Deep neural network

Prerequisites:

• Neuron:  A neuron receives an input, processes it and generates an output
• Weights: When input enters the neuron, it is multiplied by a weight
• Bias: It is added to the result of weight multiplication to the input
• Neural Networks: Typically, neurons are organized in layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the input to the output layer, possibly after traversing the layers multiple times
• Gradient Descent: It is the function that adjusts weights according to the error they caused
• Encoders: These find patterns in raw data to form compact and useful representations
  

• Decoders: These generate high-resolution data from those representations
• Autoencoders: These are one of the finest and simpler forms of unsupervised learning taking the encoder-decoder architecture and learning to generate an exact facsimile of the input data
• Convolutional Neural Networks (CNNs): CNN's are feed-forward neural networks that use a spatial-invariance trick to efficiently learn local patterns
• Recurrent Neural Networks (RNNs): RNNs are networks that have cycles and therefore have state memory, in which data can flow in any direction
• Generative Adversarial Networks (GANs): GANs are a framework for training networks optimized for generating new realistic samples from a representation
• Deep Reinforcement Learning (Deep RL): When the learning is done by a neural network, we refer to it as Deep Reinforcement Learning (Deep RL)
• Recursive Neural Network: These are a generalization of RNN’s to a tree-like structure
• Activation Function: To allow Neural Networks to learn complex decision boundaries, we apply a nonlinear activation function to some of its layers
• Softmax: It normalizes the scores by exponentiating and dividing by a normalization constant
• Multi-Layer perceptron: In this, stacks of neurons are used to generate the desired outputs

• Linear regression: It allows us to summarise and study relationships between two continuous variables
• Logistic regression: It used for analyzing the dataset in which there are one or more independent variables that determine an outcome
• Optimization: A technique used to minimize the loss function of the network
• Feed-forward neural networks: The information's flows start at the input layer, goes to the hidden layers, and end at the output layer in a forward direction

Future:

• Deep learning has enabled many practical applications of ML and by extension the overall field of AI. DL breaks down tasks in simpler and advanced ways that makes all kinds of machine assists seem possible, even likely. AI is the present and the future of everything.
• Natural language understanding is another area in which DL is poised to make a large impact over the next few years. We expect systems that use RNNs to understand much better when they learn strategies.
• Ultimately and undoubtedly, the major development in DL will come…

Applications:

• Speech Recognition, Virtual Assistants, Driverless Cars, and Chatbots
• Facial Recognition, Entertainment, Aerospace, Defense, and Medical
• Research, Automation, Electronics, NLP, Financial and Military

Developer Take-A-Ways!

• Blocks, Caffe, ConvNet, convnet.js, CXXNET, DeepLearnToolbox, Deep Belief Networks, deeplearning4j, DeepDist, deepmat, deepnet, Deepwalk, dl-machine, Gnumpy, H2O, Keras, Learning Deep Boltzmann Machines, mPoT, Nengo, DIGITS , ND4J, Neon , OpenDL, RNNLM, Tensorflow, Torch, Theano, Lasagne, Matlab Deep Learning, CNTK, Neural Designer, Microsoft Cognitive Toolkit, IBM PowerAI, and Brighter AI
• https://aws.amazon.com/machine-learning/amis/
• https://www.edx.org/professional-certificate/ibm-deep-learning
• https://www.deeplearning.ai/
• https://www.coursera.org/specializations/deep-learning
• https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning
• https://www.udemy.com/deeplearning/
• https://www.datacamp.com/courses/deep-learning-in-python
• https://developers.google.com/machine-learning/crash-course/
• https://www.kaggle.com/learn/deep-learning
• https://software.intel.com/en-us/ai/courses/deep-learning

Conclusion:

It can be concluded that the DL models can be used in a variety of tasks due to their capability of simulating the human brain. A lot of research has been done in the area and a lot of research is going to be done in the near future.

I’m going to share a bunch of tools for developers at the Developer Take-A-Ways Section of the story, but feel free to comment, share or send me any other interesting videos or links you might have found. ✌ It’s a massive opportunity to work on. I hope you found this article useful.

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