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Unlock the Power of Deep Learning and Artificial Intelligence with TensorFlow 2.0

Master Deep Learning and Artificial Intelligence: Expanding Horizons with Machine Learning, Neural Networks in Computer Vision, Time Series Analysis, Natural Language Processing, Generative Adversarial Networks, Reinforcement Learning, and beyond!

Created by- Lazy Programmer Inc., Lazy Programmer Team

What you’ll learn in Deep Learning and Artificial Intelligence course

  • Understanding and implementing Artificial Neural Networks (ANNs) and Deep Neural Networks (DNNs)
  • Predicting stock returns with cutting-edge techniques
  • Employing Deep Learning for time series forecasting
  • Applying Deep Learning in Computer Vision
  • Building a Deep Reinforcement Learning-based Stock Trading Bot
  • Exploring Generative Adversarial Networks (GANs) in-depth
  • Building sophisticated Recommender Systems
  • Harnessing Deep Learning for Image Recognition
  • Getting hands-on with Convolutional Neural Networks (CNNs)
  • Implementing Recurrent Neural Networks (RNNs) in practice
  • Utilizing Tensorflow Serving for model serving using a RESTful API
  • Leveraging Tensorflow Lite for model exportation for mobile (Android, iOS) and embedded systems
  • Using Tensorflow’s Distribution Strategies for learning parallelization
  • Understanding low-level Tensorflow, gradient tape, and constructing custom models
  • Applying Deep Learning in Natural Language Processing (NLP)
  • Demonstrating Moore’s Law using practical code
  • Using Transfer Learning to create top-tier image classifiers.
 Deep Learning and Artificial Intelligence
TensorFlow 2.0

Course Content

  • Welcoming Note
  • Introduction to Google Colab
  • Journey into Machine Learning and Neurons
  • Exploration of Feedforward Artificial Neural Networks
  • Dive into Convolutional Neural Networks
  • Study of Recurrent Neural Networks, Time Series, and Sequence Data
  • Natural Language Processing (NLP) Uncovered
  • Discovery of Recommender Systems
  • Harnessing the Potential of Transfer Learning for Image Recognition
  • Introduction to GANs (Generative Adversarial Networks)
  • Deep Reinforcement Learning (Theory) Explored
  • Project: Stock Trading using Deep Reinforcement Learning
  • Mastering Advanced Tensorflow Usage
  • Deep Dive into Low-Level Tensorflow
  • In-Depth Study: Loss Functions
  • In-Depth Study: Gradient Descent
  • Additional Topics
  • Guide to Setting Up Your Environment (FAQ by Student Request)
  • Extra Assistance With Python Coding for Beginners (FAQ by Student Request)
  • Effective Strategies for Learning Machine Learning (FAQ by Student Request)
  • The Appendix / FAQ Finale


  1. Possess skills in Python and Numpy coding.
  2. For theoretical segments (optional), grasp the concepts of derivatives and probability.


Welcome to the world of Tensorflow 2.0!

This is a truly exhilarating moment. Almost four years have passed since the initial release of, and now we’ve reached the milestone of its second official version.

Tensorflow is a creation of Google, designed for deep learning and artificial intelligence tasks.

Deep Learning has been the driving force behind some truly astounding advancements lately, including:

  • Crafting stunning, lifelike images of people and objects that have never existed in reality (through Generative Adversarial Networks or GANs).
  • Overcoming world champions in strategic games like Go, and complex video games such as CS:GO and Dota 2 (via Deep Reinforcement Learning).
  • Developing autonomous driving capabilities (through Computer Vision techniques).
  • Enabling voice recognition systems (like Siri) and machine translation capabilities (under the domain of Natural Language Processing).
  • And even producing videos of individuals performing actions or uttering words they never actually did (an unsettling usage of deep learning known as DeepFakes).

Tensorflow reigns as the most widely used library for deep learning globally, and it’s developed by Google. In fact, Google’s parent company, Alphabet, recently clinched the title of the world’s wealthiest company. Tensorflow is the go-to library for numerous organizations engaging in AI and machine learning.

Put simply, to venture into the realm of deep learning, mastering is a must.

This course caters to a wide spectrum of students, from those at the beginner level to experts. You may wonder how?

Assuming you’ve completed my free Numpy prerequisite, you’re ready to dive right in. We’ll initiate with very basic machine learning models and progressively move to cutting-edge concepts.

In the course of your learning journey, you’ll be introduced to major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (for image processing), and Recurrent Neural Networks (for sequence data).

The ongoing projects encompass:

  • Processing Natural Language (NLP)
  • Systems for Recommending Content
  • Leveraging Transfer Learning for Computer Vision
  • Constructing Generative Adversarial Networks (GANs)
  • Creating a Deep Reinforcement Learning Stock Trading Bot

Regardless of whether you’ve participated in all my preceding courses, you’ll still gather knowledge on how to modify your previous code to leverage Tensorflow 2.0. Furthermore, this course introduces all-new, unprecedented projects such as forecasting time series and performing stock predictions.

This course is crafted for learners who desire quick understanding, but it also includes “in-depth” sections for those interested in delving deeper into the theory, like understanding what a loss function is, or exploring the various types of gradient descent methods.

In-depth Tensorflow subjects encompass:

  1. Model Deployment with Tensorflow Serving: Understanding how to usein a cloud environment.
  2. Model Deployment with Tensorflow Lite: Learning to deploy models in mobile and embedded applications.
  3. Distributed Tensorflow Training with Distribution Strategies: Acquiring skills to distribute Tensorflow training.
  4. Creating Custom Tensorflow Models: Learning how to tailor-make your own Tensorflow models.
  5. Converting Tensorflow 1.x code to Tensorflow 2.0: Mastering the process of updating code from Tensorflow 1.x to Tensorflow 2.0.
  6. Constants, Variables, and Tensors: Deepening your understanding of these fundamental Tensorflow concepts.
  7. Eager Execution: Exploring this computing mode in Tensorflow.
  8. Gradient Tape: Learning about this powerful feature in Tensorflow for automatic differentiation.

Instructor’s Note: This course is constructed with an emphasis on variety rather than intensive detail, favoring the creation of fascinating projects over extensive theoretical coverage. If you’re seeking a theory-intensive course, this may not be the ideal fit. Typically, for each of these subjects – recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc., I’ve already offered courses specifically concentrated on those areas.

Thank you for taking the time to read this, and I look forward to seeing you in the class!


  • Every line of code is meticulously explained – feel free to reach out if you think otherwise.
  • We avoid wasting time “typing” on the keyboard unlike other courses – honestly, it’s impossible to craft meaningful code from scratch in just 20 minutes.
  • We don’t shy away from university-level mathematics – gain vital insights into algorithms that other courses often omit.

This course is suitable for

  • Novice to expert learners who are eager to explore deep learning and AI using Tensorflow 2.0.

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Frequently Asked Questions (FAQs)

Q1: Who created the course?

A: The course is created by Lazy Programmer Inc., and the Lazy Programmer Team.

Q2: What key concepts will I learn in this course?

A: This course will provide a comprehensive understanding of various aspects of Artificial Intelligence and Deep Learning, such as implementing ANNs and DNNs, predicting stock returns, employing deep learning for time series forecasting, applying deep learning in Computer Vision, and much more.

Q3: What are the unique features of this course?

A: This course offers unique features like explaining every line of code in detail, and not wasting time typing on the keyboard, as well as including university-level mathematics for crucial insights into algorithms.

Q4: What is the main prerequisite for this course?

A: You should be proficient in Python and Numpy coding. For optional theoretical segments, a basic understanding of derivatives and probability is required.

Q5: Who is the target audience for this course?

A: This course is suitable for learners ranging from novices to experts who are eager to explore deep learning and AI using Tensorflow 2.0.

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