This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in **Theano** and **TensorFlow**, and you know how to run code using the GPU.

This course is all about how to use deep learning for**computer vision** using **convolutional neural networks**. These are the state of the art when it comes to **image classification** and they beat vanilla deep networks at tasks like MNIST.

In this course we are going to up the ante and look at the**StreetView House Number (SVHN)** dataset - which uses larger color images at various angles - so things are going to get tougher both computationally and in terms of the difficulty of the classification task. But we will show that convolutional neural networks, or CNNs, are capable of handling the challenge!

Because**convolution** is such a central part of this type of neural network, we are going to go in-depth on this topic. It has more applications than you might imagine, such as **modeling artificial organs** like the pancreas and the heart. I'm going to show you how to build convolutional filters that can be applied to **audio**, like the echo effect, and I'm going to show you how to build filters for **image effects**, like the **Gaussian blur** and **edge detection**.

We will also do some**biology** and talk about how convolutional neural networks have been inspired by the **animal visual cortex**.

After describing the architecture of a convolutional neural network, we will jump straight into code, and I will show you how to extend the deep neural networks we built last time (in part 2) with just a few new functions to turn them into CNNs. We will then test their performance and show how convolutional neural networks written in both Theano and TensorFlow can outperform the accuracy of a plain neural network on the StreetView House Number dataset.

All the materials for this course are FREE. You can download and install Python, Numpy, Scipy, Theano, and TensorFlow with simple commands shown in previous courses.

This course focuses on**"how to build and understand"**, not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about **"seeing for yourself" via experimentation**. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

Suggested Prerequisites:

Tips for success:

This course is all about how to use deep learning for

In this course we are going to up the ante and look at the

Because

We will also do some

After describing the architecture of a convolutional neural network, we will jump straight into code, and I will show you how to extend the deep neural networks we built last time (in part 2) with just a few new functions to turn them into CNNs. We will then test their performance and show how convolutional neural networks written in both Theano and TensorFlow can outperform the accuracy of a plain neural network on the StreetView House Number dataset.

All the materials for this course are FREE. You can download and install Python, Numpy, Scipy, Theano, and TensorFlow with simple commands shown in previous courses.

This course focuses on

Suggested Prerequisites:

- calculus
- linear algebra
- probability
- Python coding: if/else, loops, lists, dicts, sets
- Numpy coding: matrix and vector operations, loading a CSV file
- linear regression, logistic regression
- neural networks and backpropagation
- Can write a feedforward neural network in Theano and TensorFlow

Tips for success:

- Use the video speed changer! Personally, I like to watch at 2x.
- Take handwritten notes. This will drastically increase your ability to retain the information.
- Write down the equations. If you don't, I guarantee it will just look like gibberish.
- Ask lots of questions on the discussion board. The more the better!
- Don't get discouraged if you can't solve every exercise right away. Sometimes it'll take hours, days, or maybe weeks!
- Write code yourself, this is an applied course! Don't be a "couch potato".

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

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I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...

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- Introduction and Outline (02:42) (FREE preview available)
- Where to get the code and data - instant access (01:42)
- How to use Github & Extra Coding Tips (Optional) (11:12)
- How to Succeed in this Course (05:52)

- Intro to Google Colab, how to use a GPU or TPU for free (12:32)
- Uploading your own data to Google Colab (11:41)
- Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn? (08:54)

- Review Section Introduction (02:37)
- What is Machine Learning? (14:26)
- Code Preparation (Classification Theory) (15:59)
- Classification Notebook (08:40)
- Code Preparation (Regression Theory) (07:18)
- Exercise: Predicting Diabetes Onset (02:34)
- Regression Notebook (10:34)
- Exercise: Real Estate Predictions (02:33)
- The Neuron (09:58)
- How does a model 'learn'? (10:53)
- Making Predictions (06:45)
- Saving and Loading a Model (04:27)
- Suggestion Box (03:10)

- Artificial Neural Networks Section Introduction (06:00)
- Forward Propagation (09:40)
- The Geometrical Picture (09:43)
- Activation Functions (17:18)
- Multiclass Classification (08:41)
- How to Represent Images (12:36)
- Code Preparation (ANN) (12:42)
- ANN for Image Classification (08:36)
- ANN for Regression (11:05)
- Exercise: E. Coli Protein Localization Sites (02:21)

- What is Convolution? (part 1) (16:38)
- What is Convolution? (part 2) (05:56)
- What is Convolution? (part 3) (06:41)
- Convolution on Color Images (15:58)
- CNN Architecture (20:58)
- CNN Code Preparation (15:13)
- CNN for Fashion MNIST (06:46)
- CNN for CIFAR-10 (04:28)
- Data Augmentation (08:51)
- Batch Normalization (05:14)
- Improving CIFAR-10 Results (10:22)
- Exercise: Facial Expression Recognition (01:35)

- Embeddings (13:12)
- Code Preparation (NLP) (13:17)
- Text Preprocessing (05:30)
- CNNs for Text (08:07)
- Text Classification with CNNs (06:10)
- Exercise: Sentiment Analysis (02:01)

- Mean Squared Error (09:11)
- Binary Cross Entropy (05:58)
- Categorical Cross Entropy (08:06)

- Gradient Descent (07:52)
- Stochastic Gradient Descent (04:36)
- Momentum (06:11)
- Variable and Adaptive Learning Rates (11:46)
- Adam Optimization (pt 1) (13:15)
- Adam Optimization (pt 2) (11:14)

- Introduction and Outline (01:51)
- Review of Important Concepts (03:43)
- Where to get the code and data for this course (02:08)
- How to Succeed in this Course (03:13)
- Tensorflow or Theano - Your Choice! (04:10)
- How to load the SVHN data and benchmark a vanilla deep network (05:04)

- Real-Life Examples of Convolution (08:53)
- Beginner's Guide to Convolution (06:28)
- What is convolution? (05:19)
- Convolution example with audio: Echo (06:40)
- Convolution example with images: Gaussian Blur (05:42)
- Convolution example with images: Edge Detection (03:22)
- Write Convolution Yourself (09:15)
- Alternative Views on Convolution (06:43)
- Suggestion Box (03:10)

- Translational Invariance (02:23)
- Architecture of a CNN (05:09)
- Convolution on 3-D Images (10:50)
- Tracking Shapes in a CNN (16:38)
- Relationship to Biology (02:19)
- Convolution and Pooling Gradients (02:40)
- LeNet - How the Shapes Go Together (12:52)

- Theano - Building the CNN components (04:20)
- Theano - Full CNN and Test on SVHN (17:27)
- Visualizing the Learned Filters (03:36)

- TensorFlow - Building the CNN components (03:40)
- TensorFlow - Full CNN and Test on SVHN (10:39)

- Practical Image Processing Tips (03:08)
- Advanced CNNs and how to Design your Own (11:10)

- Facial Expression Recognition Problem Description (12:22)
- The class imbalance problem (06:02)
- Utilities walkthrough (05:46)
- Convolutional Net in Theano (21:04)
- Convolutional Net in TensorFlow (19:04)

- Anaconda Environment Setup (20:21)
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

- Beginner's Coding Tips (13:22)
- How to Code Yourself (part 1) (15:55)
- How to Code Yourself (part 2) (09:24)
- Proof that using Jupyter Notebook is the same as not using it (12:29)
- Python 2 vs Python 3 (04:38)
- Is Theano Dead? (10:04)

- How to Succeed in this Course (Long Version) (10:25)
- Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
- What order should I take your courses in? (part 1) (11:19)
- What order should I take your courses in? (part 2) (16:07)

- What is the Appendix? (02:48)
- Where to get discount coupons and FREE deep learning material (05:31)

- Data Links