Top Tutorials To Learn Deep Learning With Python

Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind’s AlphaGo beat the World champion at Go — a game where intuition plays a key role.

But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that’s why it’s at the heart of Artificial intelligence.

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!

1. Deep Learning Prerequisites: Linear Regression in Python

Data science: Learn linear regression from scratch and build your own working program in Python for data analysis.

This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. It will cover the theory from the ground up: derivation of the solution, and applications to real-world problems. It will show you how one might code their own linear regression module in Python.

Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you’ll be returning to it for years to come. In the first section, It will show you how to use 1-D linear regression to prove that Moore’s Law is true.In the next section, It will extend 1-D linear regression to any-dimensional linear regression — in other words, how to create a machine learning model that can learn from multiple inputs.We will apply multi-dimensional linear regression to predicting a patient’s systolic blood pressure given their age and weight.

Finally, we will discuss some practical machine learning issues that you want to be mindful of when you perform data analysis, such as generalization, over fitting, train-test splits, and so on.

2. Deep Learning Prerequisites: Logistic Regression in Python

Data science techniques for professionals and students — learn the theory behind logistic regression and code in Python.

This course is a lead-in to deep learning and neural networks — it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. It will cover the theory from the ground up: derivation of the solution, and applications to real-world problems. It will show you how one might code their own logistic regression module in Python.

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, you’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!

3. Complete Guide to Tensor Flow for Deep Learning with Python

Learn how to use Google’s Deep Learning Framework — Tensor Flow with Python. Solve problems with cutting edge techniques.

This course will guide you through how to use Google’s Tensor Flow framework to create artificial neural networks for deep learning. It aims to give you an easy to understand guide to the complexities of Google’s Tensor Flow framework in a way that is easy to understand. It serves as a complete guide to using the Tensor Flow framework as intended, while showing you the latest techniques available in deep learning. It is designed to balance theory and practical implementation, with complete Jupiter notebook guides of code and easy to reference slides and notes. It has plenty of exercises to test your new skills along the way.

This course covers a variety of topics, including Neural Network Basics, Tensor Flow Basics, Artificial Neural Networks, Densely Connected Networks, Convolutional Neural Networks, Recurrent Neural Networks, AutoEncoders, Reinforcement Learning, OpenAI Gym and much more.

4. Zero to Deep Learning with Python and Keras

Understand and build Deep Learning models for images, text, sound and more using Python and Keras.

This course is designed to provide a complete introduction to Deep Learning. It is aimed at beginners and intermediate programmers and data scientists who are familiar with Python and want to understand and apply Deep Learning techniques to a variety of problems.

It will start with a review of Deep Learning applications and a recap of Machine Learning tools and techniques. Then it will introduce Artificial Neural Networks and explain how they are trained to solve Regression and Classification problems. Over the rest of the course it introduces and explains several architectures including Fully Connected, Convolutional and Recurrent Neural Networks, and for each of these it explains both the theory and give plenty of example applications.

5. Deep Learning: Convolutional Neural Networks in Python

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. It is 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 it will show that convolutional neural networks, or CNNs, are capable of handling the challenge.

It will show you how to build convolutional filters that can be applied to audio, like the echo effect, and how to build filters for image effects, like the Gaussian blur and edge detection.you will also do some biology and talk about how convolutional neural networks have been inspired by the animal visual cortex.

6. Deep Learning: Recurrent Neural Networks in Python

In the first section of the course we are going to add the concept of time to our neural networks.It’ll introduce you to the Simple Recurrent Unit, also known as the Elman unit.

In the next section of the course, you are going to revisit one of the most popular applications of recurrent neural networks — language modeling.

Another popular application of neural networks for language is word vectors or word embeddings. The most common technique for this is called Word2Vec, but It’ll show you how recurrent neural networks can also be used for creating word vectors.

In the section after, you will look at the very popular LSTM, or long short-term memory unit, and the more modern and efficient GRU, or gated recurrent unit, which has been proven to yield comparable performance. You will apply these to some more practical problems, such as learning a language model from Wikipedia data and visualizing the word embedding we get as a result.

7. Deep Learning A-Z?: Hands-On Artificial Neural Networks

Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.

In this course you will understand the intuition behind Artificial Neural Networks, will apply Artificial Neural Networks in practice, will understand the intuition behind Convolutional Neural Networks, will apply Convolutional Neural Networks in practice, will understand the intuition behind Recurrent Neural Networks, will apply Recurrent Neural Networks in practice, will understand the intuition behind Self-Organizing Maps, will apply Self-Organizing Maps in practice, will understand the intuition behind Boltzmann Machines, will apply Boltzmann Machines in practice and will understand the intuition behind AutoEncoders.

8. Modern Deep Learning in Python

Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.

In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time. You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate. You will also learn about adaptive learning rate techniques like AdaGrad, RMSprop, and Adam which can also help speed up your training.

9. Natural Language Processing with Deep Learning in Python

Complete guide on deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets

In this course you are going to look at advanced NLP. It will show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know. nWord2vec is interesting because it magically maps words to a vector space where you can find analogies, like:king — man = queen — woman, France — Paris = England — London, December — Novemeber = July — June. You are also going to look at the GLoVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems. Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train. You will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity.

Lastly, you’ll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words.

10. Artificial Intelligence: Reinforcement Learning in Python

Complete guide to artificial intelligence and machine learning, prep for deep reinforcement learning

When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing — playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more.And yet reinforcement learning opens up a whole new world. As you’ll learn in this course, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other. It’s led to new and amazing insights both in behavioral psychology and neuroscience. In this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It’s the closest thing we have so far to a true general artificial intelligence.

11. Data Science, Deep Learning, & Machine Learning with Python

Go hands-on with the latest neural network, artificial intelligence, and data science techniques employers are seeking.

If you’ve got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry — and prepare you for a move into this hot career path.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won’t find academic, deeply mathematical coverage of these algorithms in this course — the focus is on practical understanding and application of them. At the end, you’ll be given a final project to apply what you’ve learned.

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. It’ll cover the machine learning and data mining techniques real employers are looking for, including: Deep Learning / Neural Networks (MLP’s, CNN’s, RNN’s), Regression analysis, K-Means Clustering, Principal Component Analysis, Train/Test and cross validation, Bayesian Methods, Decision Trees and Random Forests, Multivariate Regression, Multi-Level Models, Support Vector Machines, Reinforcement Learning, Collaborative Filtering, K-Nearest Neighbor, Bias/Variance Trade off, Ensemble Learning, Term Frequency / Inverse Document Frequency, Experimental Design and A/B Tests and much more.