21 hours (usually 3 days including breaks)
Good understanding of mathematics.
Good understanding of basic statistics.
Basic programming skills are not required but recommended.
Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing “intelligent” tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
Understand the intuition behind Artificial Neural Networks
Build artificial neural networks with Tensorflow
Classify images, data using deep learning
Apply Convolutional Neural Networks in practice
Neural networks are a family of machine learning algorithms that are generating a lot of excitement. They are a technique that is inspired by how the neurons in our brains function. They are based on a simple idea: given certain parameters, it is possible to combine them in order to predict a certain result. For example, if you know the number of pixels in an image, there are ways of knowing which number is written in the image. The data that enters passes through various “ layers” in which a series of adjusted learning rules are applied by a weighted function. After passing through the last layer, the results are compared with the “correct” results, and the parameters are adjusted.
Although the algorithms and the learning process in general are complex, one the network has learned, it can freeze the various weights and function in a memory or execution mode. Google uses these types of algorithms, for example, for image searches.
There is no single definition for the meaning of Deep Learning. In general, when we talk of Deep Learning, we are referring to a group of Machine Learning algorithms based on neural networks that, as we have seen, are characterized by cascade data processing. The entrance signal passes through the various stages, and in each one, they are subjected to a non-linear transformation. This helps to extract and transform the variable according to the determined parameters (weights or boundaries). There isn’t an established limit for the number of stages that a neural network must contain to be considered Deep Learning. However, it is thought that Deep Learning arose in the 80’s, using a model which had 5 or 6 layers. It was (and is) called the neocognitron and was created by the Japanese researcher Kunihiki Fukushima. Neural networks are very effective in identifying patterns.
An example worth highlighting of the application of Deep Learning is the project carried out by Google and the Universities of Stanford and Massachusetts. It aimed to improve the natural language processing techniques of a type of AI called Recurrent Neural Network Language Model (RNNLM). It’s used for automatic translations and creating subtitles, among other thing. Basically, it builds up phrases word by words, basing each word on the previous one and in this way, it can even write poems.
Module 1
1. Introduction to Deep Learning and TensorFlow
2. Basics of Neural Networks
3. Designing a shallow neural network (Scratch and python) (Project)
4. Deeper neural network using TensorFlow. (Project)
You’re going to learn hands-on artificial neural networks with neurolab, a lesser-known and traditional Python library for artificial intelligence.
You’re going to learn hands-on artificial neural networks with neurolab, a lesser-known and traditional Python library for artificial intelligence. This is a hands-on course and you will be working your way through with Python and Jupyter notebooks.
What you will learn:
Machine learning (ML), or deep learning, depends on algorithms to inform what actions are taken and then produce an inferred function. In the future, we may see machines achieve true self-awareness and operate independently from human, data-influenced input. But for now, humans and data will continue to play a critical role in shaping machine-driven predictions. There are two main methods to guide your machine learning model—supervised and unsupervised learning. Depending on what data is available and what question is asked, the algorithm will be trained to generate an outcome using one of these methods. The difference between them is that supervised learning uses a full set of labeled data during training. In unsupervised learning, the data set is provided without explicit instructions on what to do with it; the machine is basically winging it.
The supervised learning technique is more commonly used in machine learning because it deals with straightforward tasks and is easy to implement. Data inputs are labeled with the answer that the algorithm should arrive at, which helps the machine pick out patterns in the future, better differentiate data, or make predictions. Supervised learning is classified into two categories of algorithms and is ideal for problems where there are reference points available.
We regularly use supervised learning to teach ourselves or someone else a new task. It’s kind of like being given a test with the answer key. Once you have the task mastered, this technique can be applied to similar processes and information.
In this technique, the machine learning model learns organically instead of receiving a data set with explicit instructions. It then tries to automatically find structure in the raw data through analysis and interpretation. While supervised learning is easiest, we don’t always have access to complete, perfectly labeled data sets to train the algorithm. Where supervised learning has the “right” answer, unsupervised learning is helpful in situations where analysts (or really anyone) ask questions and the algorithm doesn’t have the answer, or there’s more than one answer. The unsupervised learning model is classified into four different categories of algorithms, which group data based on similarities or relationships among variables:
There are other, less common methods for machine learning that we’re starting to see used more frequently, perhaps because we live and work in a time-constrained and often reward-driven culture.
Practical applications of supervised and unsupervised machine learning are all around us. Learn about some common machine learning examples—some of which you may encounter every day, depending on your line of work. Since there’s considerable noise and debate around machine learning, it’s also helpful to demystify some common machine learning misconceptions. This will help you understand how to harness the power of machine learning and embrace the opportunities it provides. Thinking of implementing ML or AI in your organization, or just want to dive deeper? Follow these AI and ML industry blogs to learn about what’s hot in the market.