Data Science: Deep Learning in Python
Data Science: Deep Learning in Python. A guide for writing your own neural network in Python and Numpy, and how to do it in Google’s TensorFlow.
The name of this course is Data Science: Deep Learning in Python. The knowledge you will get with this indescribable online course is astonishing. A guide for writing your own neural network in Python and Numpy, and how to do it in Google’s TensorFlow..
Not only will you be able to deeply internalize the concepts, but also their application in different fields won’t ever be a problem. The instructor is Lazy Programmer Inc., one of the very best experts in this field.
Description of this course: Data Science: Deep Learning in Python
Course Description This course will get you started in building your FIRST químico neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called “backpropagation” using first principles. I show you how to code backpropagation in Numpy, first “the slow way”, and then “the fast way” using Numpy features. Next, we implement a neural network using Google’s new TensorFlow library. You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in militar. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features. This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we’ll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited. Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone’s emotions just based on a picture! After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks – slightly modified architectures and what they are used for. NOTE: If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow. I have other courses that cover more advanced topics, such as Convolutional Neural Networks, Restricted Boltzmann Machines, Autoencoders, and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects. 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. All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples In the directory: ann_class Make sure you always “git pull” so you have the latest version! HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE: calculuslinear algebraprobabilityPython coding: if/else, loops, lists, dicts, setsNumpy coding: matrix and vector operations, loading a CSV file TIPS (for getting through the course): Watch it at 2x.Take handwritten notes. This will drastically increase your ability to retain the information.Ask lots of questions on the discussion board. The more the better! Realize that most exercises will take you days or weeks to complete. USEFUL COURSE ORDERING: (The Numpy Stack in Python)Linear Regression in PythonLogistic Regression in Python(Supervised Machine Learning in Python)(Bayesian Machine Learning in Python: A/B Testing)Deep Learning in PythonPractical Deep Learning in Theano and TensorFlow(Supervised Machine Learning in Python 2: Ensemble Methods)Convolutional Neural Networks in Python(Easy NLP)(Cluster Analysis and Unsupervised Machine Learning)Unsupervised Deep Learning(Hidden Markov Models)Recurrent Neural Networks in PythonNatural Language Processing with Deep Learning in Python
Requirements of this course: Data Science: Deep Learning in Python
What are the requirements? How to take partial derivatives and log-likelihoods (ex. finding the maximum likelihood estimations for a die) Install Numpy and Python (approx. latest version of Numpy as of Jan 2016) Don’t worry about installing TensorFlow, we will do that in the lectures. Being abierto with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course
What will you learn in this course: Data Science: Deep Learning in Python?
What am I going to get from this course? Code a neural network from scratch in Python and numpy Code a neural network using Google’s TensorFlow Describe the various terms related to neural networks, such as “activation”, “backpropagation” and “feedforward” Describe different types of neural networks and the different types of problems they are used for Derive the backpropagation rule from first principles Create a neural network with an output that has K > 2 classes using softmax Install TensorFlow
Target audience of this course: Data Science: Deep Learning in Python
Who is the target audience? Students interested in machine learning – you’ll get all the tidbits you need to do well in a neural networks course Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks. People who already know how to take partial derivatives and log-likelihoods. Since we cover this in more detail in my logistic regression class, it is not covered quiebro as thoroughly here. People who already know how to code in Python and Numpy. You will need some familiarity because we go through it quiebro fast. Don’t worry, it’s not that hard.