In this video we will set up a Pytorch deep learning environment by installing Anaconda and PyCharm so that you have everything that you need so you can focus on the important stuff: coding and learning about machine learning! If you are just starting out then do not focus on the irrelevant parts, which is the IDE that you use etc. Just a small tip that I feel have benefited me :)

People often ask what courses are great for getting into ML/DL and the two I started with is ML and DL specialization both by Andrew Ng. Below you'll find both affiliate and non-affiliate links if you want to check it out. The pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
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DL Specialization (no affiliate): https://bit.ly/3t8JqA9

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Welcome to part 8 of the Deep Learning with Python, Keras, and Tensorflow series. In this tutorial, we're going to work on using a recurrent neural network to predict against a time-series dataset, which is going to be cryptocurrency prices.

Text tutorials and sample code: https://pythonprogramming.net/....cryptocurrency-recur

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We were invited to Bulgaria by "Our Home is Bulgaria" to explore the country for the first time this year and it was an amazing experience to travel through this space with so much history and diverse landscapes. Some of the places we feature here are the Seven Rila Lakes, Alexander Nevsky Cathedral, Thracian Tomb of Kazanlak, Belogradchik Fortress, Devetashka cave, and Mount Buzludzha.

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So...after considerable amounts of rendering time - 8k for Youtube made with the GH5 begs only one question...does size matter? Here's a tutorial.
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“Mank”, the movie that wins the Oscar for best cinematography in 2021 was shot using the RED Ranger Camera. RED Ranger Camera gave the delicate monochromatic look which gave the movie a unique feel. So what’s in it in the RED Ranger Camera? Watch the full video to know all the ins and outs of this great cinema camera.
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Caffe is a Deep Learning library that is well suited for machine vision and forecasting applications. With Caffe you can build a net with sophisticated configuration options, and you can access premade nets in an online community.

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Caffe is a C++/CUDA library that was developed by Yangqing Jia of Google. The library was initially designed for machine vision tasks, but recent versions support sequences, speech and text, and reinforcement learning. Since it’s built on top of CUDA, Caffe supports the use of GPUs.

Caffe allows the user to configure the hyper-parameters of a deep net. The layer configuration options are robust and sophisticated – individual layers can be set up as vision layers, loss layers, activation layers, and many others. Caffe’s community website allows users to contribute premade deep nets along with other useful resources.

Vectorization is achieved through specialized arrays called “blobs”, which help optimize the computational costs of various operations.

Have you ever used the Caffe library in one of your Deep Net projects? Please comment and share your experiences.

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