Tensorflow 2.0: Deep Learning and Artificial Intelligence

Learn Tensorflow 2.0, Deep Learning, Artificial Intelligence, Machine Learning, Neural Networks, Computer Vision, NLP, GANs, and more! Build your own models with Tensorflow, serve models with RESTful API, and deploy on mobile devices. Beginner to expert-level course. Machine Learning and Neural Networks for Computer Vision, Time Series Analysis, NLP, GANs, Reinforcement Learning, +More!

 

What you'll Learn

• Artificial Neural Networks (ANNs)/Deep Neural Networks (DNNs)
• Anticipate Stock Returns
• Time Series Determining
• Computer Vision
• The most effective method to construct a Deep Reinforcement Learning Stock Exchanging Bot
• GANs (Generative Antagonistic Networks)
• Recommender Systems
• Image Recognition
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Utilize Tensorflow Serving to serve your model utilizing a Soothing Programming interface
• Utilize Tensorflow Lite to trade your model for portable (Android, iOS) and implanted gadgets
• Utilize Tensorflow's Distribution Strategies to parallelize learning
• Low-level Tensorflow, inclination tape, and how to fabricate your own custom models
• Natural Language Processing (NLP) with Deep Learning
• Exhibit Moore's Regulation utilizing Code
• Transfer Learning to make cutting edge image classifiers

 

Necessities

Know how to code in Python and Numpy

For the hypothetical parts (discretionary), grasp subsidiaries and likelihood

Depiction

Welcome to Tensorflow 2.0!

 

What a thrilling time. It's been almost a long time since Tensorflow was delivered, and the library has developed to its true second rendition.

Tensorflow is Google's library for deep learning and artificial intelligence.

Deep Learning has been liable for a few astounding accomplishments as of late, for example,

Producing delightful, photograph reasonable images of individuals and things that never existed (GANs)

Beating title holders in the system game Go, and complex computer games like CS:GO and Dota 2 (Deep Reinforcement Learning)

Self-driving vehicles (Computer Vision)

Discourse recognition (for example Siri) and machine interpretation (Natural Language Processing)

In any event, making recordings of individuals doing and making statements they never did (DeepFakes - a possibly loathsome utilization of deep learning)

 

Tensorflow is the world's most well known library for deep learning, and it's worked by Google, whose parent Letters in order as of late turned into the most money rich organization on the planet (only a couple of days before I composed this). It is the library of decision for some organizations doing artificial intelligence and machine learning.

At the end of the day, to do deep learning, you need to know Tensorflow.

 

This course is for novice understudies as far as possible up to master level understudies. How is this possible?

On the off chance that you've quite recently taken my free Numpy essential, then you know all that you really want to bounce right in. We will begin with an extremely essential machine learning models and advance to cutting edge ideas.

En route, you will find out pretty much all of the significant deep learning designs, like Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (succession information).

Current tasks include:

Natural Language Processing (NLP)

Recommender Systems

Transfer Learning for Computer Vision

Generative Antagonistic Networks (GANs)

Deep Reinforcement Learning Stock Exchanging Bot

Regardless of whether you've taken every one of my past courses as of now, you will in any case find out about how to change over your past code so it utilizes Tensorflow 2.0, and there are all-new and never-before-seen projects in this course, for example, time series determining and how to do stock expectations.

This course is intended for understudies who need to catch on quickly, yet there are moreover "top to bottom" segments on the off chance that you need to dig somewhat deeper into the hypothesis (like what is a misfortune capability, and what are the various kinds of slope drop draws near).

 

High level Tensorflow subjects include:

Conveying a model with Tensorflow Serving (Tensorflow in the cloud)

Conveying a model with Tensorflow Lite (portable and inserted applications)

Disseminated Tensorflow preparation with Distribution Strategies

Composing your own custom Tensorflow model

Changing Tensorflow 1.x code over completely to Tensorflow 2.0

Constants, Factors, and Tensors

Energetic execution

Angle tape

 

Teacher's Note: This course centers around expansiveness as opposed to profundity, with less hypothesis for building more cool stuff. In the event that you are searching for a more hypothesis thick course, this isn't it. For the most part, for every one of these subjects (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, and so on) I as of now have courses uniquely centered around those points.

 

Gratitude for perusing, and I'll see you in class!

 

WHAT Request Would it be a good idea for me to TAKE YOUR COURSES Ready?:

Look at the talk "Machine Learning and computer based intelligence Essential Guide" (accessible in the FAQ of any of my courses, including the free Numpy course)

 

Exceptional Highlights

Each line of code made sense of exhaustively - email me any time assuming that you clash

No sat around "composing" on the console like different courses - can we just be real for a moment, it's not possible for anyone to truly compose code worth learning about in only a little ways without any preparation

Not scared of college level math - get significant insights regarding calculations that different courses forget about

Who this course is for:

Amateurs to cutting edge understudies who need to find out about deep learning and man-made intelligence in Tensorflow 2.0

 

Educators

Sluggish Developer Inc.

Artificial intelligence and machine learning engineer

• 4.6 Teacher Rating
• 149,846 Surveys
• 530,049 Understudies
• 34 Courses

Today, I invest the vast majority of my energy as an artificial intelligence and machine learning engineer with an emphasis on deep learning, despite the fact that I have likewise been known as an information researcher, huge information specialist, and full stack programmer.

I accepted my most memorable bosses degree quite a long time back in computer designing with a specialization in machine learning and example recognition. I accepted my second bosses degree in measurements with applications to monetary designing.

Experience incorporates internet promoting and computerized media as both an information researcher (streamlining snap and change rates) and large information engineer (building information processing pipelines). A few major information advances I oftentimes use are Hadoop, Pig, Hive, MapReduce, and Flash.

I've made deep learning models to anticipate active clicking factor and client conduct, as well with respect to image and flag processing and demonstrating text.

My work in suggestion systems has applied Reinforcement Learning and Cooperative Sifting, and we approved the outcomes utilizing A/B testing.

I have shown undergrad and graduate understudies in information science, measurements, machine learning, calculations, math, computer designs, and physical science for understudies going to colleges like Columbia College, NYU, Tracker School, and The New School.

Numerous organizations have profited from my web programming skill. I do all the backend (server), frontend (HTML/JS/CSS), and tasks/organization work. A portion of the innovations I've utilized are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Spine, and Rakish. For capacity/data sets I've utilized MySQL, Postgres, Redis, MongoDB, and the sky is the limit from there.

 

4.7 course appraising 9K Reviews

Aiden T.

Rating: 5.0 out of 5a week prior

AT

 

Magnificent course that covers such countless parts of deep learning through Tensorflow. In the end you have a full comprehension of many deep learning applications and the information to make your own undertakings or go after a position.

Yarin K.

Rating: 5.0 out of 52 weeks prior

 

The course is extremely huge and the Sluggish Programer goes inside and out to a variety of subjects. The scratch pad he utilizes are excluded, be that as it may, the means he takes are simple and he explaines everything. Energetically suggest for individuals acquainted with the subject yet need to go inside and out and figure out a somewhat expansive range of DL?

 

So Yiu N.

Rating: 4.5 out of 5a month prior

 

Some arithmetic are very difficult to get, yet generally excellent web-based course to learn more on Machine Learning and Neural

 

Mana M.

Rating: 5.0 out of 52 months prior

 

Extraordinary course. Worth each penny and moment you spend. The educator is great in the two his insight and educating strategy.

Useful Links:

1. Official Tensorflow website: https://www.tensorflow.org/
2. Tensorflow documentation: https://www.tensorflow.org/api_docs
3. Tensorflow GitHub repository: https://github.com/tensorflow/tensorflow
4. Tensorflow tutorials: https://www.tensorflow.org/tutorials
5. Tensorflow community forum: https://discuss.tensorflow.org/
6. Tensorflow Lite documentation: https://www.tensorflow.org/lite
7. Tensorflow Serving documentation: https://www.tensorflow.org/tfx/guide/serving
8. Tensorflow Distribution Strategies documentation: https://www.tensorflow.org/guide/distributed_training
9. Tensorflow Models GitHub repository: https://github.com/tensorflow/models
10. Tensorflow on YouTube: https://www.youtube.com/playlist?list=PLQY2H8rRoyvzoUYI26kHmKSJBedn3SQuB