Deep Learning With Jupyter Notebooks In The Cloud
This step-by-step tutorial will show you how to set up and use Jupyter Notebook on Amazon Web Services (AWS) EC2 GPU for deep learning. While DataCamp's Introduction to Deep Learning in Python course gives you everything you need for doing deep learning on your laptop or personal computer, you’ll eventually find that you want to run deep learning models on a Graphical Processing Unit (GPU). This p
PySpark Cheat Sheet: Spark DataFrames in Python
This PySpark SQL cheat sheet is your handy companion to Apache Spark DataFrames in Python and includes code samples. You'll probably already know about Apache Spark, the fast, general and open-source engine for big data processing; It has built-in modules for streaming, SQL, machine learning and graph processing. Spark allows you to speed analytic applications up to 100 times faster compared to ot
Python Dictionary Comprehension Tutorial
Learn all about Python dictionary comprehension: how you can use it to create dictionaries, to replace (nested) for loops or lambda functions with map(), filter() and reduce(), ...! Dictionaries (or dict in Python) are a way of storing elements just like you would in a Python list. But, rather than accessing elements using its index, you assign a fixed key to it and access the element using the ke
Python Seaborn Tutorial For Beginners: Start Visualizing Data
Python Seaborn Tutorial For Beginners: Start Visualizing Data This Seaborn tutorial introduces you to the basics of statistical data visualization Visualization is a crucial aspect of data analysis and interpretation, as it allows for easy comprehension of complex data sets. It helps in identifying patterns, relationships, and trends that might not be apparent through raw data alone. In recent yea
Python LSTM (Long Short-Term Memory Network) for Stock Predictions
Data Visualization Now let's see what sort of data you have. You want data with various patterns occurring over time. plt.figure(figsize = (18,9)) plt.plot(range(df.shape[0]),(df['Low']+df['High'])/2.0) plt.xticks(range(0,df.shape[0],500),df['Date'].loc[::500],rotation=45) plt.xlabel('Date',fontsize=18) plt.ylabel('Mid Price',fontsize=18) plt.show() This graph already says a lot of things. The spe
TensorFlow Tutorial For Beginners
Learn how to build a neural network and how to train, evaluate and optimize it with TensorFlow Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain. TensorFlow is the second machine learning framework that Google created and used to design, build, and train deep learning models. You can use the TensorFlow library do
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