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[2026] Tensorflow 2: Deep Learning & Artificial Intelligence

Name: [2026] Tensorflow 2: Deep Learning & Artificial Intelligence
Rating: 4.5 (14187 reviews)

Machine Learning & Neural Networks for Computer Vision, Time Series Analysis, NLP, GANs, Reinforcement Learning, +More!

Created byLazy Programmer Inc., Lazy Programmer Team

Last updated 3/2026

English

English [Auto],Spanish [Auto],

What you'll learn

Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
Predict Stock Returns
Time Series Forecasting
Computer Vision
How to build a Deep Reinforcement Learning Stock Trading Bot
GANs (Generative Adversarial Networks)
Recommender Systems
Image Recognition
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Use Tensorflow Serving to serve your model using a RESTful API
Use Tensorflow Lite to export your model for mobile (Android, iOS) and embedded devices
Use Tensorflow's Distribution Strategies to parallelize learning
Low-level Tensorflow, gradient tape, and how to build your own custom models
Natural Language Processing (NLP) with Deep Learning
Demonstrate Moore's Law using Code
Transfer Learning to create state-of-the-art image classifiers
Earn the Tensorflow Developer Certificate
Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Course content

24 sections • 146 lectures • 26h 4m total length

Introduction4:03
Outline12:47
Outline of the course introduces tensor flow 2.0 and the caris API, plus Google Colab setup, covering linear models, CNNs, RNNs, time series forecasting, and applications.
Where to get the code, notebooks, and data4:38
Access the course code via the resources tab, using Code Link for notebooks and GitHub link for extra materials. Learn notebooks vs plain text Python files and common retrieval mistakes.

Intro to Google Colab, how to use a GPU or TPU for free12:32
Explore Google Colab, a cloud notebook for writing Python deep learning code with free GPU or TPU access, preinstalled libraries, and easy sharing via Google Drive.
Tensorflow 2 in Google Colab7:54
Uploading your own data to Google Colab11:41
Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?11:00
How to Succeed in This Course3:04
Temporary 403 Errors2:57
Course Updates3:52
Colab remains the official environment; updates occur as needed, replacing fitgenerator with fit, using tensorflow datasets and keras augmentation for images, and text vectorization layer for NLP.

What is Machine Learning?14:26
Code Preparation (Classification Theory)15:59
Classification Notebook22:21
Learn linear classification using TensorFlow 2 on the breast cancer data set to predict malignant versus benign tumors, with practical coding, data loading, preprocessing, and model training.
Code Preparation (Regression Theory)7:19
Regression Notebook27:28
The Neuron9:58
How does a model "learn"?10:54
Making Predictions6:45
Learn how to generate predictions with TensorFlow models using fit and predict, convert probabilities to class outputs, and verify accuracy while handling 2D and 1D shapes.
Saving and Loading a Model4:28
Why Keras?4:27
Suggestion Box3:10

Artificial Neural Networks Section Introduction6:00
Explore feed forward neural networks, inspired by brain neurons, and how architecture, activation functions, and multi-class classification extend linear models to unstructured data like images, text, and sound.
Beginners Rejoice: The Math in This Course is Optional12:31
Forward Propagation9:40
The Geometrical Picture9:43
Activation Functions17:18
Multiclass Classification8:41
Learn multiclass classification by applying the softmax activation to produce a probability distribution over K categories, and contrast it with binary sigmoid for two-class tasks.
How to Represent Images12:36
Explore how images are represented for deep learning: RGB color channels form a 3D tensor, 8-bit values scaled to 0–1, with grayscale as 2D, and flattening into vectors.
Color Mixing Clarification0:54
Code Preparation (ANN)12:42
ANN for Image Classification8:36
ANN for Regression11:05

What is Convolution? (part 1)16:38
What is Convolution? (part 2)5:56
What is Convolution? (part 3)6:41
Demonstrate the equivalence of convolution and matrix multiplication, using a one-dimensional example to show how repeating the filter creates a matrix; explain weight sharing for translational invariance and efficiency.
Convolution on Color Images15:58
CNN Architecture20:58
Learn the typical cnn architecture: convolution and pooling layers shrink images while increasing feature maps, then flatten and dense layers, with stride and global pooling for varying sizes.
CNN Code Preparation15:13
CNN for Fashion MNIST6:46
CNN for CIFAR-104:28
Explore image classification with a TensorFlow 2 convolutional neural network on CIFAR-10, via a prepared code lab notebook that builds, trains, and evaluates the model, covering data loading and overfitting.
Data Augmentation8:51
Batch Normalization5:14
Improving CIFAR-10 Results19:11

Sequence Data18:27
Learn about sequence data and time series in deep learning, including shapes n by t by d, one- and multi-dimensional data, and practical examples like stock, weather, speech, and text.
Forecasting10:35
Forecast time series by predicting multiple future values over a horizon. Use linear regression as a baseline, then extend to multi-step forecasts with sequential inputs and past values.
Autoregressive Linear Model for Time Series Prediction12:01
Proof that the Linear Model Works4:12
Show how a linear model learns a sine wave using an R2 model with two pass values of the time series to perfectly forecast a non-linear time series.
Recurrent Neural Networks21:34
RNN Code Preparation5:50
Learn to build a simple rnn in TensorFlow 2.0: load synthetic data, shape inputs to t by d, instantiate the rnn layer, and train, evaluate, and predict while handling shapes.
RNN for Time Series Prediction11:11
Paying Attention to Shapes8:27
Explore how shapes influence art understanding. Track shapes with a manual recurrent neural network forward pass, detailing t, d, m, k, and the associated weight matrices and biases.
GRU and LSTM (pt 1)17:35
GRU and LSTM (pt 2)11:36
A More Challenging Sequence9:19
Demo of the Long Distance Problem19:26
RNN for Image Classification (Theory)4:41
RNN for Image Classification (Code)4:00
Stock Return Predictions using LSTMs (pt 1)12:03
Stock Return Predictions using LSTMs (pt 2)5:45
Stock Return Predictions using LSTMs (pt 3)11:59
Other Ways to Forecast5:14

Transfer Learning Theory8:12
Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)5:41
Large Datasets and Data Generators7:03
2 Approaches to Transfer Learning4:51
Transfer Learning Code (pt 1)23:42
Learn transfer learning with data augmentation in TensorFlow using a pre-trained VGG16 model, including input preprocessing, augmentation layers, and a dense classifier, with a comparison to a non-augmented approach.
Transfer Learning Code (pt 2)24:48

Requirements

Know how to code in Python and Numpy
For the theoretical parts (optional), understand derivatives and probability

Description

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.

Welcome to Tensorflow 2.0!

What an exciting time. It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version.

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

Deep Learning has been responsible for some amazing achievements recently, such as:

Generating beautiful, photo-realistic images of people and things that never existed (GANs)
Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)
Self-driving cars (Computer Vision)
Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)
Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning)

Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.

In other words, if you want to do deep learning, you gotta know Tensorflow.

This course is for beginner-level students all the way up to expert-level students. How can this be?

If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.

Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).

Current projects include:

Natural Language Processing (NLP)
Recommender Systems
Transfer Learning for Computer Vision
Generative Adversarial Networks (GANs)
Deep Reinforcement Learning Stock Trading Bot

Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions.

This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).

Advanced Tensorflow topics include:

Deploying a model with Tensorflow Serving (Tensorflow in the cloud)
Deploying a model with Tensorflow Lite (mobile and embedded applications)
Distributed Tensorflow training with Distribution Strategies
Writing your own custom Tensorflow model
Converting Tensorflow 1.x code to Tensorflow 2.0
Constants, Variables, and Tensors
Eager execution
Gradient tape

Instructor's Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.

Thanks for reading, and I’ll see you in class!

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

UNIQUE FEATURES

Every line of code explained in detail - email me any time if you disagree
No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math - get important details about algorithms that other courses leave out

Who this course is for:

Beginners to advanced students who want to learn about deep learning and AI in Tensorflow 2.0

[2026] Tensorflow 2: Deep Learning & Artificial Intelligence

What you'll learn

Explore related topics

Course content

Welcome3 lectures • 21min

Google Colab7 lectures • 53min

Machine Learning and Neurons11 lectures • 2hr 7min

Feedforward Artificial Neural Networks11 lectures • 1hr 50min

Interlude: tf.data2 lectures • 25min

Convolutional Neural Networks11 lectures • 2hr 6min

Recurrent Neural Networks, Time Series, and Sequence Data18 lectures • 3hr 14min

Natural Language Processing (NLP)6 lectures • 1hr 45min

Recommender Systems2 lectures • 22min

Transfer Learning for Computer Vision6 lectures • 1hr 14min

Requirements

Description

Who this course is for: