About Deep Learning Online Training Course
ACTE Online Training courses are designed and led by industry experts with more than a decade of experience in data science and deep learning. Freshers and professionals at various stages of their career are acquiring Deep Learning skills by enrolling for expert-led online courses. The courses follow an experiential methodology and are delivered through videos, demos, case studies, and online instructor-led webinars. ACTE provides best-in-the-industry hands-on training makes you job ready from day one.
Benefits
The adaptation of Deep Learning applications has seen a rise in fields like bioinformatics, drug, toxicology and medical images, speech or image recognition, mobile advertising, financial fraud detection, and more. DL professionals work with lower to higher layers of raw input and derive meaningful insights. This highly rewarding job has captured the attention of data and machine learning enthusiasts.
Deep Learning is very useful for career.AI is a sound career choice for a while now and as the adoption of AI in various verticals continues to grow, the demand for trained professionals to do the jobs created by this growth is also skyrocketing. ... Therefore, if you are an AI enthusiast then be optimistic and prepare for a great career in AI.
Deep Learning , have great scope, Scope is high Basically, Machine learning is itself a type of artificial intelligence that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. Here is an overview of Big Data, Machine Learning, Deep Learning, Artificial Intelligence, Data Science, and the Internet of Things.
Even as a fresher, you can get a job in Deep Learning domain.AI is a sound career choice for a while now and as the adoption of AI in various verticals continues to grow, the demand for trained professionals to do the jobs created by this growth is also skyrocketing. ... Therefore, if you are an AI enthusiast then be optimistic and prepare for a great career in AI.
We are happy and proud to say that we have strong relationship with over 700+ small, mid-sized and MNCs. Many of these companies have openings for Deep Learning . Moreover, we have a very active placement cell that provides 100% placement assistance to our students. The cell also contributes by training students in mock interviews and discussions even after the course completion.
Artificial intelligence is a tool, and like many tools, its danger is fully dependent on humans and the ways that they use it.
Think about a hammer. It can be used for wonderful things, like building a home, but it can also be used to hurt someone else.
Unlike a hammer, however, which can only be used by one person at a time with relatively little impact, AI can be created by a single person and spread around, which can multiply its power for good or evil.
One way that artificial intelligence can be dangerous is when it is used to create autonomous weapons. Currently, almost every large nation is spending a lot of resources on the creation of autonomous weapons that can be used in upcoming conflicts.
- Starting out in deep learning is not as difficult as people might make you believe. There are a few elementary basics that you should cover before diving into deep learning. Deep learning requires knowledge of the following topics:
- Mathematics: You should be comfortable with probability, derivatives, linear algebra and a few other basic topics. Khan Academy offers a decent course covering almost all the above topics here.
- Statistics: The basics of statistics are required for going forward with any machine learning problem. Understanding the concepts of statistics are essential because most of the deep learning concepts are derived from assimilating the concepts of statistics. You can check the online courses available here.
- Tool: A decent level of coding skills are required for implementing deep learning into real life problems. Coursera’s, Introduction to Data Science in Python is a decent course to start off with Python as a tool.
- Machine Learning: Machine learning is the base for deep learning. One can not start learning deep learning without understanding the concepts of machine learning. You could go through Intro to Machine Learning or Andrew Ng’s course Machine Learning for a theoretical base.
You can use any languages for artificial intelligence, there is no such language in which you can't implement Artificial Intelligence or Machine Learning. Some common languages are: C (programming language) ... (Mostly used) Python (programming language).
Our course ware is designed to give a hands-on approach to the students in Deep Learning . The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.
It's definitely worth it! AI and ML are perhaps the two most talked about buzzwords today. It's not just a good idea to study these things; it's a GREAT IDEA. ... There are lots of jobs available for people who have knowledge and skills related to developing AI.
Basically, it takes between 365 days (1 year) to 1,825 days (5 years) to learn artificial intelligence (assuming you put in 4 – 0.5 learning hours a day). And how fast you learn also affects how long it takes you to be an expert.
Do the fast.ai course — Practical Deep Learning for Coders — Part 1. This takes about 4–6 weeks of effort. This course has a session on running the code on cloud.
- Bright career.
- AI is versatile.
- Skill of the century.
- Ingests huge amounts of data.
- Improved disaster management.
- Benefit of the society.
- AI improvises user experience.
Deep Learning Biggest Strengths
No Need for Feature Engineering
- Deep learning is largely responsible for today’s growth in the use of AI. The technology has given computers extraordinary powers, such as the ability to recognize speech almost as good as a human being, a skill too tricky to code by hand.
- Deep learning has also transformed computer vision and dramatically improved machine translation. It is now being used to guide and enhance all sorts of key processes in medicine, finance, marketing—and beyond.
- Feature engineering is the process of extracting features from raw data to better describe the underlying problem. It is a fundamental job in machine learning as it improves model accuracy. The process can sometimes require domain knowledge about a given problem.
- To better understand feature engineering, consider the following example.
- In the real estate business, the location of a house has a significant impact on the selling price. Suppose the location is given as the latitude and the longitude. Alone these two numbers are not of any use but put together they represent a location. The act of combining the latitude and the longitude to make one feature is feature engineering.
- One of deep learning’s main advantages over other machine learning algorithms is its capacity to execute feature engineering on it own. A deep learning algorithm will scan the data to search for features that correlate and combine them to enable faster learning without being explicitly told to do so.
- This ability means that data scientists can sometimes save months of work. Besides, the neural networks that a deep learning algorithm is made of can uncover new, more complex features that human can miss.
Best Results with Unstructured Data
- According to research from Gartner, up to 80% of a company’s data is unstructured because most of it exists in different formats such as texts, pictures, pdf files and more. Unstructured data is hard to analyze for most machine learning algorithms, which means it’s also going unutilized. That is where deep learning can help.
- Deep learning algorithms can be trained using different data formats, and still derive insights that are relevant to the purpose of its training. For example, a deep learning algorithm can uncover any existing relations between pictures, social media chatter, industry analysis, weather forecast and more to predict future stock prices of a given company.
No Need for Labeling of Data
- Getting good-quality training data is one of the biggest problems in machine learning because data labeling can be a tedious and expensive job.
- Sometimes, the data labeling process is simple but time-consuming. For example, labeling photos “dog” or “muffin” is an easy task, but an algorithm needs thousands of pictures to tell the difference. Other times, data labeling may require the judgments of highly skilled industry experts, and that is why, for some industries, getting high-quality training data can be very expensive.
Let’s look at the example of Microsoft’s project InnerEye, a tool that uses computer vision to analyze radiological images. To make correct, autonomous decisions, the algorithm requires thousands of well-annotated images where different physical anomalies of the human body are clearly labeled. Such work needs to be done by a radiologist with experience and a trained eye. According to Glassdoor, an average base salary for a radiologist is $290.000 a year, which puts the hourly rate just short of $200. Given that around 4-5 images can be analyzed per hours, proper labeling of all images will be expensive.
- With deep learning, the need for well-labeled data is made obsolete as deep learning algorithms excel at learning without guidelines. Other forms of machine learning are not nearly as successful with this type of learning. In the example above, a deep learning algorithm would be able to detect physical anomalies of the human body, even at earlier stages than human doctors.
Efficient at Delivering High-quality Results
- Humans need rest and fuel. They get tired or hungry and make careless mistakes. That is not the case for neural networks. Once trained correctly, a deep learning brain can perform thousands of repetitive, routine tasks within a shorter period of time than it would take a human being.
- The quality of its work never diminishes, unless the training data includes raw data that does not represent the problem you are trying to solve.
- Deep learning algorithms are applied to customer data in CRM systems, social media and other online data to better segment clients, predict churn and detect fraud. The financial industry is relying more and more on deep learning to deliver stock price predictions and execute trades at the right time. In the healthcare industry deep learning networks are exploring the possibility of repurposing known and tested drugs for use against new diseases to shorten the time before the drugs are made available to the general public.
Some of the job profiles for certified DL professionals are:
- Data Scientist
- DL Analyst
- DL Associate
- Deep Learning R&D Engineer
- Deep Learning Software Engineer
- Deep Learning Analyst/Consultant
Governmental institutions are also turning to deep learning for help to get real-time insights into metric like food production and energy infrastructure by analyzing satellite imagery.
- The list can go on, but one thing is clear: given the use cases and enthusiasm for deep learning, we can expect large investments to be made to further perfect this technology, and more and more of the current challenges to be solved in the future.
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