About Course

Deep Learning with Python and Keras – FREE Course

This comprehensive course provides a complete introduction to Deep Learning using Python and Keras. It’s designed for beginners and intermediate programmers and data scientists who want to learn about and apply Deep Learning techniques to real-world problems.

Start with a review of Deep Learning applications and a recap of Machine Learning tools and techniques. Then, dive into Artificial Neural Networks and learn how they are trained to solve Regression and Classification problems.

Throughout the course, you’ll explore various neural network architectures including Fully Connected, Convolutional, and Recurrent Neural Networks. You’ll learn both the theory and practical applications of each, with plenty of examples to solidify your understanding.

This course strikes a balance between theory and practice. We explain mathematical details while providing exercises and sample code to apply what you’ve learned. This approach equips you with a strong foundation in Deep Learning, covering both the theoretical and practical aspects.

By the end of the course, you will be able to:

Identify problems that can be solved with Deep Learning
Design and train various Neural Network models
Utilize cloud computing to speed up training and enhance model performance

This course is completely FREE and available on Udemy. Enhance your data science skills today and unlock the power of Deep Learning!

To describe what Deep Learning is in a simple yet accurate way
To explain how deep learning can be used to build predictive models
To distinguish which practical applications can benefit from deep learning
To install and use Python and Keras to build deep learning models
To apply deep learning to solve supervised and unsupervised learning problems involving images, text, sound, time series and tabular data.
To build, train and use fully connected, convolutional and recurrent neural networks
To look at the internals of a deep learning model without intimidation and with the ability to tweak its parameters
To train and run models in the cloud using a GPU
To estimate training costs for large models
To re-use pre-trained models to shortcut training time and cost (transfer learning)

Course Content

1. Welcome to the course!

A Message from the Professor
001. Welcome to the course!.mp4

00:00
002. Introduction.mp4

00:00
003. Real world applications of deep learning.mp4

00:00
004. Download and install Anaconda.mp4

00:00
005. Installation Video Guide.mp4

00:00
006. Obtain the code for the course.html

00:00
007. Course Folder Walkthrough.mp4

00:00
008. Your first deep learning model.mp4

00:00

2. Data

001. Section 2 Intro.mp4

00:00
002. Tabular data.mp4

00:00
003. Data exploration with Pandas code along.mp4

00:00
004. Visual data Exploration.mp4

00:00
005. Plotting with Matplotlib.mp4

00:00
006. Unstructured Data.mp4

00:00
007. Images and Sound in Jupyter.mp4

00:00
008. Feature Engineering.mp4

00:00
009. Exercise 1 Presentation.mp4

00:00
010. Exercise 1 Solution.mp4

00:00
011. Exercise 2 Presentation.mp4

00:00
012. Exercise 2 Solution.mp4

00:00
013. Exercise 3 Presentation.mp4

00:00
014. Exercise 3 Solution.mp4

00:00
015. Exercise 4 Presentation.mp4

00:00
016. Exercise 4 Solution.mp4

00:00
017. Exercise 5 Presentation.mp4

00:00
018. Exercise 5 Solution.mp4

00:00
Section Quiz

3. Machine Learning

001. Section 3 Intro.mp4

00:00
002. Machine Learning Problems.mp4

00:00
003. Supervised Learning.mp4

00:00
004. Linear Regression.mp4

00:00
005. Cost Function.mp4

00:00
006. Cost Function code along.mp4

00:00
007. Finding the best model.mp4

00:00
008. Linear Regression code along.mp4

00:00
009. Evaluating Performance.mp4

00:00
010. Evaluating Performance code along.mp4

00:00
011. Classification.mp4

00:00
012. Classification code along.mp4

00:00
013. Overfitting.mp4

00:00
014. Cross Validation.mp4

00:00
015. Cross Validation code along.mp4

00:00
016. Confusion matrix.mp4

00:00
017. Confusion Matrix code along.mp4

00:00
018. Feature Preprocessing code along.mp4

00:00
019. Exercise 1 Presentation.mp4

00:00
020. Exercise 1 solution.mp4

00:00
021. Exercise 2 Presentation.mp4

00:00
022. Exercise 2 solution.mp4

00:00
Section Quiz

4. Deep Learning Intro

001. Section 4 Intro.mp4

00:00
002. Deep Learning successes.mp4

00:00
003. Neural Networks.mp4

00:00
004. Deeper Networks.mp4

00:00
005. Neural Networks code along.mp4

00:00
006. Multiple Outputs.mp4

00:00
007. Multiclass classification code along.mp4

00:00
008. Activation Functions.mp4

00:00
009. Feed forward.mp4

00:00
010. Exercise 1 Presentation.mp4

00:00
011. Exercise 1 Solution.mp4

00:00
012. Exercise 2 Presentation.mp4

00:00
013. Exercise 2 Solution.mp4

00:00
014. Exercise 3 Presentation.mp4

00:00
015. Exercise 3 Solution.mp4

00:00
016. Exercise 4 Presentation.mp4

00:00
017. Exercise 4 Solution.mp4

00:00

5. Gradient Descent

001. Section 5 Intro.mp4

00:00
002. Derivatives and Gradient.mp4

00:00
003. Backpropagation intuition.mp4

00:00
004. Chain Rule.mp4

00:00
005. Derivative Calculation.mp4

00:00
006. Fully Connected Backpropagation.mp4

00:00
007. Matrix Notation.mp4

00:00
008. Numpy Arrays code along.mp4

00:00
009. Learning Rate.mp4

00:00
010. Learning Rate code along.mp4

00:00
011. Gradient Descent.mp4

00:00
012. Gradient Descent code along.mp4

00:00
013. EWMA.mp4

00:00
014. Optimizers.mp4

00:00
015. Optimizers code along.mp4

00:00
016. Initialization code along.mp4

00:00
017. Inner Layers Visualization code along.mp4

00:00
018. Exercise 1 Presentation.mp4

00:00
019. Exercise 1 Solution.mp4

00:00
020. Exercise 2 Presentation.mp4

00:00
021. Exercise 2 Solution.mp4

00:00
022. Exercise 3 Presentation.mp4

00:00
023. Exercise 3 Solution.mp4

00:00
024. Exercise 4 Presentation.mp4

00:00
025. Exercise 4 Solution.mp4

00:00
026. Tensorboard.mp4

00:00

6. Convolutional Neural Networks

001. Section 6 Intro.mp4

00:00
002. Features from Pixels.mp4

00:00
003. MNIST Classification.mp4

00:00
004. MNIST Classification code along.mp4

00:00
005. Beyond Pixels.mp4

00:00
006. Images as Tensors.mp4

00:00
007. Tensor Math code along.mp4

00:00
008. Convolution in 1 D.mp4

00:00
009. Convolution in 1 D code along.mp4

00:00
010. Convolution in 2 D.mp4

00:00
011. Image Filters code along.mp4

00:00
012. Convolutional Layers.mp4

00:00
013. Convolutional Layers code along.mp4

00:00
014. Pooling Layers.mp4

00:00
015. Pooling Layers code along.mp4

00:00
016. Convolutional Neural Networks.mp4

00:00
017. Convolutional Neural Networks code along.mp4

00:00
018. Weights in CNNs.mp4

00:00
019. Beyond Images.mp4

00:00
020. Exercise 1 Presentation.mp4

00:00
021. Exercise 1 Solution.mp4

00:00
022. Exercise 2 Presentation.mp4

00:00
023. Exercise 2 Solution.mp4

00:00
Section Quiz

7. Cloud GPUs

8. Recurrent Neural Networks

001. Section 8 Intro.mp4

00:00
002. Time Series.mp4

00:00
003. Sequence problems.mp4

00:00
004. Vanilla RNN.mp4

00:00
005. LSTM and GRU.mp4

00:00
006. Time Series Forecasting code along.mp4

00:00
007. Time Series Forecasting with LSTM code along.mp4

00:00
008. Rolling Windows.mp4

00:00
009. Rolling Windows code along.mp4

00:00
010. Exercise 1 Presentation.mp4

00:00
011. Exercise 1 Solution.mp4

00:00
012. Exercise 2 Presentation.mp4

00:00
013. Exercise 2 Solution.html

00:00
Section Quiz

9. Improving performance

001. Section 9 Intro.mp4

00:00
002. Learning curves.mp4

00:00
003. Learning curves code along.mp4

00:00
004. Batch Normalization.mp4

00:00
005. Batch Normalization code along.mp4

00:00
006. Dropout.mp4

00:00
007. Dropout and Regularization code along.mp4

00:00
008. Data Augmentation.mp4

00:00
009. Continuous Learning.mp4

00:00
010. Image Generator code along.mp4

00:00
011. Hyperparameter search.mp4

00:00
012. Embeddings.mp4

00:00
013. Embeddings code along.mp4

00:00
014. Movies Reviews Sentiment Analysis code along.mp4

00:00
015. Exercise 1 Presentation.mp4

00:00
016. Exercise 1 Solution.html

00:00
017. Exercise 2 Presentation.mp4

00:00
018. Exercise 2 Solution.html

00:00
019. Exercise 3 Presentation.mp4

00:00

Add this certificate to your resume to demonstrate your skills & increase your chances of getting noticed.

Student Ratings & Reviews

5.0

Total 1 Rating

1 Rating

0 Rating

Abdul Saboor Khan

4 months ago

A Comprehensive course on deep learning, which covers almost everything you need to know about machine and deep learning. Recommended for beginners.

About Course

Deep Learning with Python and Keras – FREE Course

What Will You Learn?

Course Content

1. Welcome to the course!

A Message from the Professor

001. Welcome to the course!.mp4

002. Introduction.mp4

003. Real world applications of deep learning.mp4

004. Download and install Anaconda.mp4

005. Installation Video Guide.mp4

006. Obtain the code for the course.html

007. Course Folder Walkthrough.mp4

008. Your first deep learning model.mp4

2. Data

001. Section 2 Intro.mp4

002. Tabular data.mp4

003. Data exploration with Pandas code along.mp4

004. Visual data Exploration.mp4

005. Plotting with Matplotlib.mp4

006. Unstructured Data.mp4

007. Images and Sound in Jupyter.mp4

008. Feature Engineering.mp4

009. Exercise 1 Presentation.mp4

010. Exercise 1 Solution.mp4

011. Exercise 2 Presentation.mp4

012. Exercise 2 Solution.mp4

013. Exercise 3 Presentation.mp4

014. Exercise 3 Solution.mp4

015. Exercise 4 Presentation.mp4

016. Exercise 4 Solution.mp4

017. Exercise 5 Presentation.mp4

018. Exercise 5 Solution.mp4

Section Quiz

3. Machine Learning

001. Section 3 Intro.mp4

002. Machine Learning Problems.mp4

003. Supervised Learning.mp4

004. Linear Regression.mp4

005. Cost Function.mp4

006. Cost Function code along.mp4

007. Finding the best model.mp4

008. Linear Regression code along.mp4

009. Evaluating Performance.mp4

010. Evaluating Performance code along.mp4

011. Classification.mp4

012. Classification code along.mp4

013. Overfitting.mp4

014. Cross Validation.mp4

015. Cross Validation code along.mp4

016. Confusion matrix.mp4

017. Confusion Matrix code along.mp4

018. Feature Preprocessing code along.mp4

019. Exercise 1 Presentation.mp4

020. Exercise 1 solution.mp4

021. Exercise 2 Presentation.mp4

022. Exercise 2 solution.mp4

Section Quiz

4. Deep Learning Intro

001. Section 4 Intro.mp4

002. Deep Learning successes.mp4

003. Neural Networks.mp4

004. Deeper Networks.mp4

005. Neural Networks code along.mp4

006. Multiple Outputs.mp4

007. Multiclass classification code along.mp4

008. Activation Functions.mp4

009. Feed forward.mp4

010. Exercise 1 Presentation.mp4

011. Exercise 1 Solution.mp4

012. Exercise 2 Presentation.mp4

013. Exercise 2 Solution.mp4

014. Exercise 3 Presentation.mp4

015. Exercise 3 Solution.mp4

016. Exercise 4 Presentation.mp4

017. Exercise 4 Solution.mp4

5. Gradient Descent

001. Section 5 Intro.mp4

002. Derivatives and Gradient.mp4

003. Backpropagation intuition.mp4